Copyright TechTarget - All rights reserved https://cyber.law.harvard.edu/rss/rss.html Techtarget Feed Generator en Thu, 19 Feb 2026 05:19:58 GMT https://www.techtarget.com/searchenterpriseai editor@techtarget.com <p>With its latest Analyst Studio update, ThoughtSpot continues its progress toward becoming an agentic platform.</p> <p>First <a href="https://www.techtarget.com/searchbusinessanalytics/news/366617947/ThoughtSpot-adds-data-preparation-with-Analyst-Studio-launch">released in January 2025</a>, Analyst Studio is ThoughtSpot's <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Top-data-preparation-challenges-and-how-to-overcome-them">data preparation</a> suite. Initial features included connectors that enable analysts and engineers to combine data from disparate sources, an AI-assisted <a href="https://www.techtarget.com/searchdatamanagement/definition/SQL">SQL</a>-based development environment, and capabilities aimed at helping customers control data management costs.</p> <p>Among other tools, the update, released on Wednesday, adds SpotCache, a caching capability that builds on Analyst Studio's pre-existing cost management capabilities, a data preparation agent that enables users to perform tasks using natural language and a native spreadsheet interface for scaling data preparation workloads.</p> <p>Coming just over two months after ThoughtSpot unveiled plans <a href="https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents">automate its analytics capabilities</a> with agents, the new version of Analyst Studio represents ThoughtSpot's progression beyond agentic analytics toward agentic data management as well, according to Donald Farmer, founder and principal of TreeHive Strategy.</p> <p>"I don't think this release is a big deal in itself, but it steadily moves ThoughtSpot forward on the path to an agentic data platform," he said. "With each release, the workflow is less dashboard-centric."</p> <p>Michael Ni, an analyst at Constellation Research, similarly noted that the Analyst Studio is significant because it shows ThoughtSpot moving beyond its roots as an analytics specialist toward becoming a more broad-based data and analytics provider.</p> <p>"Thoughtspot addresses a key pain by reducing prep friction, improving cost predictability and tightening governance," he said. "At the same time, it's strategic for ThoughtSpot. The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving."</p> <p>Based in Mountain View, Calif., ThoughtSpot provided an AI-powered analytics platform from its inception in 2012. Now, just as peers such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/252493644/Data-prep-in-browser-highlights-Tableau-BI-platform-update">Tableau</a> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">Qlik</a> did before, ThoughtSpot is expanding beyond its roots to provide a wider array of data, analytics and AI development capabilities.</p> <section class="section main-article-chapter" data-menu-title="Predictable data prep"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Predictable data prep</h2> <p>While some enterprises are <a target="_blank" href="https://www.ey.com/en_us/newsroom/2025/07/ai-investments-surge-but-agentic-ai-understanding-and-adoption-lag-behind" rel="noopener">investing heavily</a> in building AI tools that make employees better informed and operations more efficient, the expense required to develop and maintain agents, chatbots and other AI applications has <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">proven prohibitive</a> for many others.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The expansion upstream into data prep and cost control -- areas traditionally owned by hyperscalers and transformation tools -- moves [ThoughtSpot] toward becoming an AI workload optimizer. That's where enterprise dollars are moving. </figure> <figcaption> <strong>Michael Ni</strong>Analyst, Constellation Research </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>AWS recently made cost control one of the focal points of <a href="https://www.techtarget.com/searchdatamanagement/news/366635663/Latest-AWS-data-management-features-target-cost-control">the data management capabilities</a> it introduced during its annual re:Invent conference. In addition, numerous database vendors have made performance a priority so that customers can run more efficient workloads.</p> <p>With SpotCache now available in Analyst Studio, ThoughtSpot is similarly taking aim at helping customers reduce spending on part of the development process.</p> <p>Caching is the process of storing data in a temporary storage area -- a cache -- to enable fast access that improves the performance of applications and other systems. Using SpotCache, developers and analysts can create representations of data that can be queried an unlimited number of times in ThoughtSpot, which lowers costs by reducing the frequency data must be accessed in <a href="https://www.techtarget.com/searchdatamanagement/feature/Evaluate-cloud-data-warehouses-based-on-data-outcomes">cloud data warehouses</a>.&nbsp;</p> <p>Given that SpotCache addresses one of the problems enterprises encounter when trying to develop cutting-edge AI tools, it is perhaps the most valuable new feature in Analyst Studio, according to Ni.</p> <p>"SpotCache is the sleeper hit in their announcement," he said. "While agentic data prep is powerful, cost certainty is what unlocks enterprise scale. If leaders know they can run unlimited AI-driven queries without blowing up their warehouse bill, adoption accelerates. That's what makes this the most strategically significant feature."</p> <p>Farmer likewise highlighted SpotCache, noting that cloud cost control -- or lack thereof -- has been <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">an ongoing problem</a>.</p> <p>"I like that they are tackling the problem of scaling with predictable cost management," he said. "That has been a barrier to broader adoption for some time. So, SpotCache stands out as arguably the most valuable new feature here."</p> <p>Beyond SpotCache, ThoughtSpot's Analyst Studio update includes the following features:</p> <ul type="disc" class="default-list"> <li>A governed spreadsheet interface so that users familiar with Excel worksheets can perform data preparation tasks such as advanced manipulations in a familiar environment without having to leave Analyst Studio.</li> <li>A data prep agent that enables analysts to profile datasets, generate queries and troubleshoot schemas using natural language.</li> <li>Unified Data Mashup, a feature that enables data teams to deliver a unified, <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">trusted view</a> of their organization's business by blending data across cloud data warehouses, business applications and files such as Google Sheets and Microsoft Excel spreadsheets within Analyst Studio's SQL-based development environment.</li> <li>Flexibility to choose live connections for real-time needs or cached snapshots through SpotCache.</li> </ul> <p>Like Farmer and Ni, Anjali Kumari, ThoughtSpot's vice president of product management, named SpotCache the most valuable of Analyst Studio's new features. Meanwhile, she noted that the impetus for designing the new capabilities came from observing how agentic AI and generative AI have <a href="https://www.techtarget.com/searchbusinessanalytics/news/366618249/Trusted-data-at-the-core-of-successful-GenAI-adoption">intensified the need for data</a>.</p> <p>"Getting data ready for AI has become the most important step in how organizations use, benefit and see valuable return on investment," Kumari said. "We understand the pressures that are placed on data analysts, and these tools are designed to streamline their role by addressing their top concerns -- speed, efficiency and cost."</p> <p>While ThoughtSpot's expansion beyond business intelligence into data preparation with Analyst Studio is beneficial for the vendor's users, pairing analytics and data management is not unique. Not only do hyperscale cloud providers such as AWS, Google and Microsoft offer an array of data management, application development and analytics tools, but so do <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">Domo</a>, Qlik, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366553555/Sisense-unveils-composable-toolkit-for-app-development">Sisense</a>, Tableau and other one-time analytics specialists.</p> <p>However, the launch of a data prep agent is unique, according to Farmer.</p> <p>"The biggest differentiator from other prep tools, such as Tableau or [Microsoft's] Power Query, is that ThoughtSpot offers a natural language data prep agent whereas AI in other tools is mostly limited to 'smart suggestions' or separate AI copilots," he said.</p> <p>In addition, by integrating data preparation, data modeling and analytics in a single workflow designed for consumption via AI applications, ThoughtSpot is doing something different than its <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Top-cloud-based-analytics-tools-for-enterprise-use">closest competition</a>, according to Ni.</p> <p>"Data prep is consolidating into platforms, and every business intelligence vendor has some version of it. What's interesting here is that ThoughtSpot is making data prep part of an agentic operating model," he said. "Instead of separate tooling for prep, modeling and analysis, they're collapsing it into one workflow designed for AI readiness."</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the Analyst Studio update generally available, ThoughtSpot is focused on turning its platform into an enabler of autonomous action, according to Kumari.</p> <p>Spotter is <a href="https://www.techtarget.com/searchbusinessanalytics/news/366615693/ThoughtSpot-AI-agent-Spotter-enables-conversational-BI">ThoughtSpot's agent-powered interface</a>, and the vendor provides Spotter agents for specific tasks such as building dashboards and embedding intelligence. Together, with Spotter as the central orchestrator, ThoughtSpot aims to automate analysis and data preparation to deliver insights within user workflows.</p> <p>"To power this, we are investing heavily in data readiness and expanding our semantic and modeling capabilities, ensuring these agents operate on a robust, context-aware foundation," Kumari said.</p> <p>As ThoughtSpot expands beyond its roots, Farmer advised the vendor to add <a href="https://www.techtarget.com/searchcustomerexperience/news/366636690/Agentic-orchestration-the-next-AI-issue-for-CIOs-to-tackle">multi-agent coordination capabilities</a>. ThoughtSpot's <a target="_blank" href="https://modelcontextprotocol.io/docs/getting-started/intro" rel="noopener">Model Context Protocol</a> server enables agents to securely interact with data sources. Soon, as more enterprises deploy agents, the agents will need similar secure connections to each other to become fully autonomous.</p> <p>"Multi-agent coordination [is] moving from a single prep agent to a 'hive' where a prep agent automatically communicates with a security agent to apply row-level permissions during the transformation process," Farmer said.</p> <p>In addition, ThoughtSpot could add <a href="https://www.techtarget.com/whatis/definition/write-back">write-back</a> capabilities and add to its burgeoning support for unstructured data by integrating unstructured data processing directly into Analyst Studio.</p> <p>Ni, meanwhile, suggested that ThoughtSpot move beyond descriptive BI to incorporate more forward-looking <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Descriptive-vs-prescriptive-vs-predictive-analytics-explained">predictive and prescriptive capabilities</a>.</p> <p>"Descriptive BI is yesterday, diagnostic AI is today, and predictive and prescriptive intelligence that tell me what could happen and where I should focus is tomorrow," he said "ThoughtSpot is strong at explaining the past and present. … Their next leap is forecasting impact and prioritizing what matters next."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> A data prep agent and caching capabilities aimed at helping users control spending help the vendor stand out from its peers as it evolves toward becoming an agentic data platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchbusinessanalytics/news/366639258/ThoughtSpot-boosts-agentic-push-with-Analyst-Studio-update Wed, 18 Feb 2026 09:00:00 GMT <p>Streaming data specialist Redpanda on Wednesday launched new features in its Agentic Data Plane aimed at enabling customers to create a unified governance layer for managing connections between agents and data sources.</p> <p>Redpanda <a href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite">first launched the Agentic Data Plane</a> (ADP) in October 2025 featuring capabilities that enabled connectivity between agents and streaming data sources, including support for <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">the Model Context Protocol</a>&nbsp;(MCP) and&nbsp;<a href="https://www.techtarget.com/searchenterpriseai/news/366622027/Google-intros-tools-for-building-agents-and-a-new-protocol">Agent2Agent Protocol&nbsp;</a>(A2A) frameworks.</p> <p>However, capabilities that govern those connections were not yet ready.</p> <p>Now, the vendor is adding features such as AI Gateway to provide users a centralized governance pane, AI observability via the <a target="_blank" href="https://opentelemetry.io/docs/specs/otel/protocol/" rel="noopener">OpenTelemetry Protocol</a> (OTLP) to inspect and monitor agent behavior and new security controls.</p> <p>Given that agents and multi-agent systems require proper governance frameworks to ensure that they act in accordance with an enterprise's policies and meet regulatory requirements, the new ADP features are significant for Redpanda customers, according to William McKnight, president of McKnight Consulting.</p> <p>"The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI," he said. "The update addresses critical barriers by providing unified security and operational control over AI costs and token budgets. This update enables 'glass box' visibility and framework flexibility, allowing users to move … from risky experimentation to secure production."</p> <p>Based in San Francisco, Redpanda provides <a href="https://www.techtarget.com/searchdatamanagement/news/366574612/Redpanda-serverless-streaming-option-targets-cost-control">a streaming data platform</a> that enables users to capture and process data to fuel real-time analysis. Like many data management providers, the vendor has responded to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing interest in AI development</a> and added tools that let customers connect data to agents and other AI applications in addition to traditional data products such as dashboards and reports.</p> <section class="section main-article-chapter" data-menu-title="Keeping control"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Keeping control</h2> <p>Agents need governance.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The ADP has the potential to transform Redpanda from a simple streaming engine into a centralized governance layer for enterprise AI. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Unlike chatbots and other AI applications, agents are <a href="https://www.techtarget.com/searchenterpriseai/tip/A-technical-guide-to-agentic-AI-workflows">capable of autonomous behavior</a>. Rather than requiring user prompts before taking action, they can be trained to work independently.</p> <p>For example, they can constantly analyze an enterprise's data estate to surface insights that a human analyst might never have discovered. They can take on repetitive, menial work so that employees can be more efficient and spend time doing more meaningful work. And they can work together to optimize complex processes such as managing supply chains.</p> <p>Because they can make entire organizations better informed and more efficient, agents have been <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Agents-semantic-layers-among-top-data-analytics-trends">the dominant trend</a> in AI development over the past two years. But because just one agent taking the wrong action can <a target="_blank" href="https://hackernoon.com/22-examples-of-incompetent-ai-agents" rel="noopener">cause significant harm</a>, strict policies and procedures must be &nbsp;in place to ensure that agents can be trusted when put into production.</p> <p>With most AI initiatives <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">never making it past the pilot stage</a>, a mix of customer feedback and Redpanda's own experiences developing agents provided the impetus for adding new ADP features designed to engender trust that agents will act as intended once deployed, according to Tyler Akidau, the streaming data vendor's chief technology officer.</p> <p>"Our roadmap has been developed in response to direct customer feedback, our own experiences developing and deploying agents internally and our vision for what is needed to unlock agentic AI in the enterprise," he said.</p> <p>AI Gateway acts as a unified access layer for connecting agents with AI models and MCP servers by centralizing routing data, enforcing organizational policies, limiting <a href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage">spending that can spiral</a> when cloud usage isn't controlled and enabling observability of AI systems.</p> <p>Observability includes automatically generated metrics, traces, logs and transcripts using the OTLP Protocol so that users can check agent behavior in their Redpanda console and take appropriate action such as debugging when necessary.</p> <p>In addition, the ADP now includes security through <a href="https://www.techtarget.com/searchsecurity/feature/How-to-use-OpenID-Connect-for-authentication">the OpenID Connect standard</a> and fine-grained authorization policies so that every interaction with an agent, whether by a human user or another agent, is properly checked and governed.</p> <p>Meanwhile, the ADP is designed to work in conjunction with <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-agent-frameworks-A-guide-to-evaluating-agentic-platforms">any agentic framework</a> so that customers can easily run and govern agents fueled by Redpanda's streaming data capabilities.</p> <p>Collectively, the new ADP features are valuable for Redpanda customers given that they address numerous concerns and that they add governance to data that fuels real-time analysis, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This announcement … is comprehensive," he said." Redpanda's platform addresses data and AI governance, observability and even FinOps objectives. That's a broader set of capabilities than most platforms have. The announcement [also] stands out because Redpanda is building these capabilities onto a data streaming platform rather than a standard data-at-rest platform."&nbsp;</p> <p>Perhaps the most significant of the new ADP features is AI Gateway, according to McKnight. Meanwhile, from a competitive standpoint, Redpanda's new governance capabilities could help differentiate the vendor's capabilities from competing streaming data platforms <a href="https://www.techtarget.com/searchdatamanagement/news/366633567/Streaming-specialist-Confluent-unveils-AI-development-suite">such as Confluent</a>, which is the industry standard for commercial Apache Kafka and is now under agreement to be <a href="https://www.techtarget.com/searchdatamanagement/news/366636098/IBM-acquiring-Confluent-to-boost-AI-development-capabilities">acquired by IBM</a>, he continued.</p> <p>Redpanda's streaming capabilities are comparable to Confluent's, outperforming Confluent in some benchmark testing, McKnight noted. But AI governance is where Redpanda could truly stand apart.</p> <p>"Redpanda is starting to differentiate by shifting from 'data piping' to a dedicated AI governance infrastructure," McKnight said. "Unlike standard tools that require fragmented security at every source, its AI Gateway will provide a centralized control plane for managing policies, token budgets and Model Context Protocol servers."</p> <p>Petrie similarly suggested that the breadth of the new ADP features help Redpanda distinguish itself from competitors, which include AWS, Google Cloud and Microsoft, beyond <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">streaming data specialists</a>.</p> <p>"This announcement strengthens its competitive standing by integrating so many capabilities into one streaming solution," he said. "To get the same features from the larger vendors, you would need to buy multiple products. Redpanda also has the advantage of platform neutrality -- it operates across sources, systems and clouds."</p> </section> <section class="section main-article-chapter" data-menu-title="Next steps"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Next steps</h2> <p>Following the initial launch of the ADP last October, Redpanda's new governance features represent the second phase of the streaming data vendor's ADP rollout. With an overarching goal of helping customers deploy agentic AI across their organization, more features that enable users to <a href="https://www.techtarget.com/searchenterpriseai/opinion/Building-governance-for-machine-speed-The-path-to-trusted-AI-autonomy">trust and deploy agents</a> are a prominent part of Redpanda's roadmap, according to Akidau.</p> <p>"We'll be delivering additional features in our AI and MCP gateways, rolling out more agent evaluation functionality, delivering manual and automatic agent kill switches, connecting agents to more data sources, and rolling out [a SQL engine] for federated query support," he said.</p> <p>One way that Redpanda could better serve its current users and perhaps attract new ones would be to market AI Gateway as a tool for <a href="https://www.techtarget.com/searcherp/feature/Predictability-emerging-as-enterprise-ITs-new-north-star">financial governance</a> in addition to technical governance, according to McKnight.</p> <p>"They could also solidify the 'real-time' advantage by demonstrating that [its] low-latency foundation creates better agents, not just faster data," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> New Agentic Data Plane features enable users to create a governance layer for agents and could help the vendor differentiate itself from its closest competitors. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a373894778.jpg https://www.techtarget.com/searchdatamanagement/news/366639150/Streaming-specialist-Redpanda-adds-governance-to-AI-suite Wed, 18 Feb 2026 09:00:00 GMT <p>With retrieval-augmented generation pipelines struggling to deliver the relevant data that agents and other AI applications need to deliver trustworthy outputs, Graphwise launched GraphRAG to provide customers with an alternative designed to enable more successful AI development.</p> <p>Retrieval-augmented generation (<a href="https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation">RAG</a>) is a framework for connecting applications such as agents and chatbots with data sources. However, with most AI initiatives failing to make it past the pilot stage and into production, standard RAG pipelines haven't proven good enough on their own to enable enterprises to deliver usable, trustworthy AI tools.</p> <p>In January, <a href="https://www.techtarget.com/searchdatamanagement/news/366637142/New-Databricks-tool-aims-to-up-agentic-AI-response-accuracy">Databricks launched Instructed Retriever</a>, an alternative to RAG that adds more context to data retrieval such as user instructions and previous examples. &nbsp;Traditional RAG systems only use a user's query.</p> <p>Graphwise's GraphRAG, which was released on Feb. 16, unites agents and other applications with a <a href="https://www.techtarget.com/searchenterpriseai/definition/knowledge-graph-in-ML">knowledge graph</a> that acts as a semantic layer and is similarly aimed at improving on standard RAG pipelines.</p> <p>Alan Morrison, an independent analyst, noted that while knowledge graphs can be traced back to the 1960s, they are taking on greater importance because agents need the context knowledge graphs provide to perform to enterprise standards. As a result, GraphRAG is a significant addition for Graphwise users.</p> <p>"Graphwise can bring all enterprise data, content and knowledge together using standard-based graph description logic that's been around for decades, but only now becoming indispensable because the agent paradigm is here, and agents desperately need reliable context," he said. "With GraphRAG, tapping the power of that contextualized data becomes simpler."</p> <p>Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget, likewise noted that GraphRAG is valuable for Graphwise users given that it combines graph technology and RAG.</p> <p>"Bringing them together is powerful," he said. "GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve."</p> <p>With a North American headquarters in New York City and a European headquarters in Sofia, Bulgaria, Graphwise is a graph technology vendor formed in 2024 when Ontotext merged with Semantic Web Company. Competitors include specialists such as <a href="https://www.techtarget.com/searchdatamanagement/news/366630145/Neo4js-latest-targets-graph-database-performance-at-scale">Neo4j</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366618412/TigerGraph-launches-Savanna-to-aid-AI-development">TigerGraph</a> as well as broader-based database providers featuring graph database capabilities including AWS, Google Cloud and Microsoft.</p> <section class="section main-article-chapter" data-menu-title="Improving AI development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Improving AI development</h2> <p>Problems related to data aren't the sole reason <a target="_blank" href="https://www.rand.org/pubs/research_reports/RRA2680-1.html" rel="noopener">most AI initiatives fail</a> before making it into production. Unrealistic expectations, lack of a clear business case and difficulties integrating applications into real-world workflows are among the other reasons an estimated 80% of all AI projects fail.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> GraphRAG is a significant addition for Graphwise customers as it enables them to leverage knowledge graphs as a semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. This is something standard RAG systems struggle to achieve. </figure> <figcaption> <strong>Stephen Catanzano</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>But issues with data -- including the lack of sufficient high-quality, relevant data -- are among the main ones.</p> <p>Graphwise's new feature targets the discovery of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/Talend-CEO-discusses-importance-of-mining-relevant-data">relevant data</a>. Agents and other applications are built for specific tasks. For example, many enterprises are building agents that autonomously handle customer service. If the pipelines that feed those agents don't deliver the specific data relevant to an individual customer and their problem, the agent won't be effective.</p> <p>Semantic modeling -- ensuring that metadata is consistently and clearly classified whenever it is ingested or transformed -- is one means of improving search relevance and is <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">gaining popularity</a> as enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">invest more heavily in AI initiatives</a>. Graphwise's knowledge graph serves as a semantic layer, adding context to data and finding relationships between data points to make them easily discoverable.</p> <p>GraphRAG unites large language models, an enterprise's data, a structured knowledge graph and multiple search methods such as similarity search and keyword search to deliver appropriate data to agents and other applications to provide accurate outputs at a much higher rate than standard RAG.</p> <p>"The development of GraphRAG was driven by a specific structural failure in the market we call the 'Prototype Plateau,'" said Andreas Blumauer, founder of Semantic Web Company and Graphwise's senior vice president of growth. "While customers were indeed requesting better accuracy, the primary motivation came from observing enterprises stuck in a cycle of failed pilots."</p> <p>In particular, <a href="https://www.techtarget.com/searchenterpriseai/tip/RAG-best-practices-for-enterprise-AI-teams">RAG systems</a> didn't provide enough context to retrieving data, he continued.</p> <p>"Our motivation was to transform RAG from a simple associative engine into a reasoning engine," Blumauer said. "By injecting a 'Semantic Backbone', we moved beyond probability-based guesses to explicit, logic-based relationships."</p> <p>Specific GraphRAG features include the following:</p> <ul type="disc" class="default-list"> <li>Semantic Metadata Control Plane, a semantic model designed to substantially improve the accuracy of AI outputs, including reducing the likelihood of <a href="https://www.techtarget.com/searchenterpriseai/tip/Why-does-AI-hallucinate-and-can-we-prevent-it">AI hallucinations</a>, by grounding responses in an enterprise's consistent metadata.</li> <li>Explainability and Provenance Panels that display how AI responses are generated, enabling users to check for accuracy and supporting regulatory compliance by providing transparency.</li> <li>Visual debugging and monitoring capabilities that allow developers and engineers to trace an error path and drastically reduce the amount of time previously needed to troubleshoot.</li> <li>A low-code interface that enables business users to adjust AI logic without involving <a href="https://www.techtarget.com/whatis/definition/Python">Python</a> code experts.</li> <li>Built-in templates that provide governance and enable query expansion that would otherwise require extensive research and development and technical support.</li> <li><a target="_blank" href="https://www.w3.org/2004/02/skos/" rel="noopener">Simple Knowledge Organization System</a> (SKOS)-like enrichment to capture domain-specific intelligence so that AI tools can understand an enterprise's unique terminology and ensure that users get accurate responses regardless of how they phrase a query.</li> </ul> <p>Most valuable are the Semantic Metadata Control Plane and SKOS-style enrichment, according to Morrison, who noted that the control plane is where enterprises can make data accessible and discoverable across their entire data estate while SKOS-style enrichment allows non-technical users to work with data.</p> <p>Catanzano likewise highlighted the Semantic Metadata Control Plane. In addition, he noted the value of the <a href="https://www.techtarget.com/searchenterpriseai/tip/Low-code-no-code-tools-simplify-AI-customization-for-engineers">low-code interface</a>. Meanwhile, capabilities such as GraphRAG help Graphwise differentiate from competing graph technology vendors by integrating knowledge graphs with AI, Catanzano continued.</p> <p>"Its capabilities, such as explainability, provenance, and domain-specific intelligence, position it as a leader in making generative AI reliable and scalable, surpassing the limitations of traditional graph database vendors," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With GraphRAG now available, Graphwise's product development plans include adding AI-assisted automation capabilities and improving the memory of its platform, according to Blumauer.</p> <p>Memory initiatives include moving beyond session-based interactions to retaining user preferences and context to provide more personalization. AI-assisted automation plans include tools that augment text <a href="https://www.techtarget.com/searchdatamanagement/tip/How-to-make-a-metadata-management-framework">with metadata</a> to make it discoverable and generating <a href="https://www.techtarget.com/searchdatamanagement/definition/schema">schemas</a> to aid data modeling.</p> <p>"A major theme is reducing manual effort through AI-assisted automation," Blumauer said.</p> <p>Morrison advised that Graphwise to develop a multi-layer <a target="_blank" href="https://www.linkedin.com/pulse/context-graphs-capturing-why-age-ai-dharmesh-shah-oyyze/" rel="noopener">context graph</a> to expand on the context GraphRAG currently provides.</p> <p>Catanzano, meanwhile, suggested that Graphwise develop new integrations with data and AI providers to <a href="https://www.techtarget.com/searchbusinessanalytics/news/252515720/Gartner-Augmented-analytics-ecosystem-for-BI-now-key">expand its ecosystem</a> and create prebuilt templates that simplify its platform for enterprises in certain industries.</p> <p>"Industry-specific templates … would not only deepen its value for current users but also attract new customers seeking tailored, ready-to-deploy solutions," he said.</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> With standard RAG pipelines proving unreliable, the vendor's new feature uses knowledge graphs to add needed context to the data retrieval process that fuels AI outputs. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1304896250.jpg https://www.techtarget.com/searchdatamanagement/news/366639196/Graphwise-aims-to-boost-AI-accuracy-with-GraphRAG-launch Tue, 17 Feb 2026 14:34:00 GMT <p>Tuesday was a day of doubles for SurrealDB.</p> <p>The startup <a href="https://www.techtarget.com/searchdatamanagement/feature/The-rise-of-multi-model-databases-to-support-data-variety">multimodel database</a> vendor secured $23 million in venture capital funding, an extension of its Series A round that nearly doubles its total funding to $44 million. In addition, SurrealDB launched version 3.0 of its platform.</p> <p>The $23 million brings SurrealDB's Series A round to $38 million, with Chalfen Ventures and Begin Capital joining previous investors FirstMark and Georgian in their investment in the database specialist. As part of the deal, Mike Chalfen, founder of Chalfen Ventures, joins SurrealDB as a director</p> <p>Meanwhile, SurrealDB 3.0 includes new features such as a new control layer and improved vector storage and indexing capabilities, and is designed to help customers unify multiple data models -- relational, document, <a href="https://www.techtarget.com/searchdatamanagement/news/366593101/Graph-technology-helps-battle-election-misinformation">graph</a>, time-series, vector, <a href="https://www.techtarget.com/searchbusinessanalytics/news/252523998/Geospatial-data-a-key-means-of-combating-climate-change">geospatial</a> and key-value -- to fuel AI development.</p> <p>Both the new funding and platform update are significant for SurrealDB users, with one providing the cash that will enable SurrealDB to grow its multimodel capabilities and the other demonstrating those capabilities, according to Kevin Petrie, an analyst at BARC U.S.</p> <p>"This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI," he said. "They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. This level of funding can help SurrealDB deepen its product capabilities and get more serious go-to-market activities."</p> <p>Based in London, SurrealDB provides a platform that supports various data types so that users can integrate data to inform decisions based on more than just one type of data. Other vendors providing multimodel capabilities include <a href="https://www.techtarget.com/searchdatamanagement/news/366636081/Couchbase-adds-agentic-AI-development-suite-to-Capella-DBaaS">Couchbase</a>, Redis and the <a href="https://www.theserverside.com/tip/MySQL-vs-PostgreSQL-Compare-popular-open-source-databases">open-source PostgreSQL platform</a>, while hyperscale cloud providers AWS, Google Cloud and Microsoft also offer multimodel databases.</p> <section class="section main-article-chapter" data-menu-title="Cash infusion"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Cash infusion</h2> <p>SurrealDB's latest funding comes at a time when venture capital investments in data management vendors are few and far between.</p> <p>Throughout the 2010s and into the early 2020s, funding poured into the data and analytics market. In 2021 alone, vendors such as Aiven, Confluent, Databricks, Reltio, SnapLogic, ThoughtSpot and TigerGraph raised $100 million or more with Databricks' funding round reaching $1 billion. In early 2022, Aiven raised another $210 million and Sigma secured $300 million.</p> <p>But then <a href="https://www.techtarget.com/searchbusinessanalytics/news/252520740/Tech-stock-sell-off-signals-tough-times-for-data-vendors">tech stock prices plummeted</a> in mid-2022, and the funding for data and analytics vendors evaporated.</p> <p>Since then, while vendors such as <u>Aerospike</u> and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366585775/Differentiation-key-as-Sigma-Computing-raises-200M">Sigma</a> have attracted investments, few data and analytics vendors have raised capital. The common theme among the data and analytics vendors that continue to attract funding is their enablement of AI development.</p> <p>Databricks, for example, has focused heavily on AI, and continues to <a href="https://www.techtarget.com/searchdatamanagement/news/366636532/Databricks-adds-4B-funding-round-IPO-could-be-next">attract massive amounts of investment capital</a>.</p> <p>SurrealDB aims to be a layer in the AI development process. It's that focus on enabling customers to develop agents and other AI applications that helped the vendor raise funding, according to Tobie Morgan Hitchcock, SurrealDB's co-founder and CEO.</p> <p>"Raising now signals we're not just another database vendor, but increasingly an enabling layer for enterprise AI workflows," he said. "SurrealDB is being used as part of enterprise AI deployments, including agentic workflows that depend on fast, consistent data access. … Investors are leaning into infrastructure that makes AI systems production-grade, which is exactly what we are building."</p> <p>Matt Aslett, an analyst at ISG Software Research, similarly noted that SurrealDB's ability to attract venture capital funding reflects its focus on helping enterprises <a target="_blank" href="https://www.reuters.com/business/ai-venture-funding-continued-surge-third-quarter-data-shows-2025-10-06/" rel="noopener">build AI applications</a>.</p> <p>"While many VCs are chasing potential returns from investment in AI specialists, there is always investor interest in startups with the potential to make an impact in the lucrative database market, especially providers that are responding to the need for innovation to support AI initiatives," he said.</p> <p>SurrealDB plans to use the added $23 million to accelerate product engineering and improve go-to-market efforts, according to Hitchcock.</p> <p>"It lets us scale the team and the platform in parallel, shipping more capability, hardening reliability and security, and supporting larger deployments," he said. "In short, it accelerates our path from rapid adoption to durable, global scale."</p> <p>Given that SurrealDB, which was founded in 2021, is a relatively new database vendor compared to peers such as <a href="https://www.techtarget.com/searchdatamanagement/news/252525824/ArangoDB-expands-scope-of-graph-database-platform">ArangoDB</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/252514648/Redis-launches-JSON-database-capabilities-with-RedisJSON-20">Redis</a>, improving its platform and increasing its profile are wise areas of focus, according to Aslett.</p> <p>"SurrealDB is in the early stages, and its new funding round will help the company accelerate the development of both its core database and its platform capabilities, as well as expanding investment in support and services resources as well as raising its profile in a crowded market," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Platform update"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Platform update</h2> <p>While the new funding will be used, in part, to fuel future product development, SurrealDB 3.0 represents the vendor's current product development.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI. They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. </figure> <figcaption> <strong>Kevin Petrie</strong>Analyst, BARC U.S. </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>SurrealDB is designed to provide agents and other applications with unified data -- disparate data types integrated as one -- to give them proper context and memory so that they remember facts even as <a target="_blank" href="https://explodingtopics.com/blog/data-generated-per-day" rel="noopener">data volume and complexity increase</a>. To provide that proper context and memory, SurrealDB positions context graphs in its database next to the data.</p> <p>With that focus on enabling customers to build intelligence applications that feature contextual awareness and the memory to recall and learn from previous interactions, SurrealDB, though a startup competing against more established vendors for market share, has an opportunity to play the role of disruptor, according to Aslett.</p> <p>"The evolving requirements for operational databases are to support the development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by machine learning, generative AI and agentic AI," he said. "These evolving requirements are providing opportunities for emerging database providers to disrupt established incumbents."</p> <p>ISG predicts that within the next two years, around three-quarters of all enterprises will have adopted operational databases specifically designed to support the <a href="https://www.techtarget.com/whatis/definition/What-is-AI-inference">AI inferencing</a> capabilities that intelligent applications require, Aslett added.</p> <p>SurrealDB 3.0 builds on previous platform capabilities by adding the following:</p> <ul type="disc" class="default-list"> <li>Surrealism, a layer that enables developers and administrators to customize <a href="https://www.techtarget.com/whatis/definition/business-logic">business logic</a> -- the rules, processes and operations that determine how an enterprise uses SurrealDB -- including access controls and version controls.</li> <li>Improved vector search and indexing to enable the discovery and use of unstructured data such as text and images.</li> <li>Support for both <a href="https://www.techtarget.com/whatis/feature/Structured-vs-unstructured-data-The-key-differences">structured and unstructured data</a>.</li> <li>Architectural changes that add stability and improve SurrealDB's performance such as separating data values from data expressions.</li> <li>An improved developer experience, including a refined model that enables users to define custom API endpoints directly within the database, among other functions.</li> </ul> <p>A combination of customer feedback and market observations provided SurrealDB with the impetus for developing the individual features that comprise its update, according to Hitchcock.</p> <p>"The focus is on removing friction -- making the platform easier to adopt, operate, and scale -- while expanding the capabilities teams need in production," he said. "The goal is to keep the developer experience simple as use cases become more demanding."</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Looking ahead, SurrealDB is focused on three main initiatives, according to Hitchcock: maturing its platform to meet the needs of enterprises at scale, expanding its capabilities so customers don't have to integrate it with as many other tools to build AI applications and continuing to enhance <a href="https://www.techtarget.com/searchsoftwarequality/feature/6-key-ways-to-improve-developer-productivity">the developer experience</a>.</p> <p>"As more customers deploy AI in production, we're investing in capabilities that make it easier to deploy and scale AI-powered applications and agentic workflows," Hitchcock said. "Our focus is broad. … It's all about simplification."</p> <p>Despite still prioritizing some foundational capabilities such enabling enterprise-scale workloads and adding features that allow users to simplify their AI development stacks, SurrealDB is establishing itself as a viable alternative to other database vendors, according to Aslett.</p> <p>All database providers are similarly adding capabilities that foster AI development, such <a href="https://www.techtarget.com/searchdatamanagement/feature/Vector-search-now-a-critical-component-of-GenAI-development">as vector storage and indexing</a>. But Aslett noted that SurrealDB's support for various data types and the quality if its vector storage and indexing capabilities stand out.</p> <p>"SurrealDB is ahead of many established providers in terms of delivering differentiating capabilities, including enhanced vector search and indexing as well as native agent memory and context graphs, … an intuitive visual user interface and support for relational, document, graph, time-series, vector, search, geospatial and key-value data types in a single database."</p> <p>Petrie similarly noted that SurrealDB is staking out a place for itself in <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-NoSQL-database-types-in-the-cloud">a competitive market</a> with the variety of its multimodel capabilities.</p> <p>"I'm impressed with the range of data types that SurrealDB already supports as a Series A startup," he said. "This really simplifies your agentic AI architecture. The more you can consolidate diverse data and models onto a single platform, with real-time performance and memory, the better you can streamline your projects and reduce time to production."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> Though funding isn't flowing as freely into data management as it once was, the startup is attracting interest by focusing on enabling customers to unify data for AI development. https://cdn.ttgtmedia.com/rms/onlineimages/money_g1250581414.jpg https://www.techtarget.com/searchdatamanagement/news/366639042/SurrealDB-raises-23M-launches-update-to-fuel-agentic-AI Tue, 17 Feb 2026 09:51:00 GMT <p>The volume and variety of enterprise data collected for analytics and AI applications continue to increase. To gain valuable business insights from these complex data assets, organizations are also increasingly investing in data science tools and other data management and analytics technologies.</p> <p>For example, in a survey conducted by the Data &amp; AI Leadership Exchange in late 2025, 91% of chief data officers and other senior executives from 109 large businesses said their organizations are spending more money on data and AI initiatives. Ninety-seven percent said such investments are delivering measurable business value, according to a report on the annual survey <a target="_blank" href="https://static1.squarespace.com/static/62adf3ca029a6808a6c5be30/t/6942c3cb535da44088c2dbff/1765983179572/2026+AI+%26+Data+Leadership+Executive+Benchmark+Survey+Final.pdf" rel="noopener">published</a> in January 2026.</p> <p>A wide range of technologies can be used in <a href="https://www.techtarget.com/searchbusinessanalytics/feature/8-top-data-science-applications-and-use-cases-for-businesses">data science applications</a>. To help data leaders choose the right ones to achieve their organization's business goals, here are 18 top data science tools, listed in alphabetical order with details on their features and capabilities. The list was compiled by TechTarget editors based on research of available technologies and market analysis from Forrester Research and Gartner.</p> <section class="section main-article-chapter" data-menu-title="1. Apache Spark"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. Apache Spark</h2> <p>Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has made it a widely used platform since it was created in 2009, resulting in the Spark project being one of the largest open source communities among <a href="https://www.techtarget.com/searchdatamanagement/feature/15-big-data-tools-and-technologies-to-know-about">big data technologies</a>.</p> <p>Due to its speed, Spark is a good fit for <a href="https://www.techtarget.com/searchbusinessanalytics/definition/continuous-intelligence">continuous intelligence</a> applications driven by near-real-time processing of streaming data. However, it's a general-purpose distributed processing engine that's equally suited for SQL batch jobs, such as extract, transform and load processes. In fact, Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.</p> <p>Spark is still <a href="https://www.techtarget.com/searchdatamanagement/feature/Hadoop-vs-Spark-Comparing-the-two-big-data-frameworks">often used with Hadoop</a>, but it also runs standalone on top of other file systems and data stores. It features an extensive set of developer libraries and APIs, including a machine learning library and support for Python, Scala, Java, and R in addition to SQL. These capabilities make it easier for data scientists and analysts to develop Spark applications.</p> </section> <section class="section main-article-chapter" data-menu-title="2. D3"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. D3</h2> <p>Another open source tool, D3 is a JavaScript library for creating custom data visualizations in a web browser. Short for <i>data-driven documents</i>, D3 uses web standards such as HTML, Scalable Vector Graphics and CSS rather than its own graphical vocabulary. D3's developers describe it as a flexible tool that enables users to design dynamic, interactive visualizations.</p> <p>First released in 2011 and originally known as D3.js, the tool lets visualization designers use the Document Object Model API to bind data to documents representing the contents of a graphic; DOM manipulation methods can then be applied to make data-driven transformations to the documents. Animations, annotation capabilities and user-interaction features such as panning and zooming can be built into visualizations.</p> <p>D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, even basic charts might require significant coding -- and many data scientists don't have JavaScript skills. As a result, they might be more comfortable with Tableau, Power BI or another commercial data visualization tool, while D3 is used by data visualization developers and specialists who are also members of <a href="https://www.techtarget.com/searchbusinessanalytics/feature/How-to-structure-and-manage-a-data-science-team">data science teams</a>.</p> </section> <section class="section main-article-chapter" data-menu-title="3. IBM SPSS"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. IBM SPSS</h2> <p>IBM SPSS is a family of software for managing and analyzing complex statistical data and creating predictive models. It includes two primary products, IBM SPSS Statistics and IBM SPSS Modeler, plus several others that work with or incorporate them. IBM acquired the technologies when it bought SPSS Inc. in 2009.</p> <p>SPSS Statistics is a statistical analysis tool that helps users identify complex relationships, patterns and trends in data. It also supports <a href="https://www.techtarget.com/searchbusinessanalytics/definition/data-preparation">data preparation</a>, predictive modeling and forecasting. The tool includes a menu-driven UI, its own command syntax and sets of Python and R extension commands that add analytics capabilities beyond its built-in ones. AI Output Assistant, a feature added in 2025, interprets tables, charts and statistical outputs, generates data visualizations and summarizes analytics results.</p> <p>SPSS Modeler is a <a href="https://www.techtarget.com/searchenterpriseai/feature/Data-science-vs-machine-learning-How-are-they-different">data science and machine learning</a> tool that focuses on data mining and predictive modeling. It's designed for ad hoc analytics applications that combine data from multiple sources, while SPSS Statistics is geared toward regular reporting on specific data sets. SPSS Modeler includes a drag-and-drop UI and supports various types of machine learning algorithms. It also provides model management and deployment capabilities and can run R extensions and Python scripts for Spark.</p> <p>Users can export prepared data from SPSS Statistics to SPSS Modeler and run predictive models created in SPSS Modeler in the statistical analysis tool.</p> </section> <section class="section main-article-chapter" data-menu-title="4. Julia"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Julia</h2> <p>Julia is an open source programming language used for numerical computing and data science applications, such as machine learning. In a 2012 blog post announcing Julia's initial release, its four creators said they set out to design a single language that met all their needs. A key goal was to avoid the need to write programs in one language and then convert them to another for execution.</p> <p>To that end, Julia combines the convenience of using a high-level dynamic language with performance that's comparable to statically typed languages, such as C and Java. Users don't have to define data types in programs, but an option allows them to do so. A multiple dispatch approach used at runtime also helps boost execution speed.</p> <p>The documentation for Julia notes that because its compiler differs from the interpreters in data science languages like Python and R, new users "may find that Julia's performance is unintuitive at first." But, it claims, "once you understand how Julia works, it is easy to write code that is nearly as fast as C."</p> </section> <section class="section main-article-chapter" data-menu-title="5. Jupyter Notebook/JupyterLab"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. Jupyter Notebook/JupyterLab</h2> <p><a href="https://www.techtarget.com/searchenterpriseai/tutorial/How-to-use-and-run-Jupyter-Notebook-A-beginners-guide">Jupyter Notebook</a> and JupyterLab are open source web applications that enable interactive collaboration among data scientists, data engineers, mathematicians, researchers and other users. They're computational notebook tools used to create, edit and share software code, as well as explanatory text, images and other information. For example, Jupyter users can add code, computations, comments and data visualizations to a single notebook document, which can then be shared with and revised by colleagues.</p> <p>As a result, notebooks "can serve as a complete computational record" of interactive sessions involving various data science team members, according to Jupyter Notebook's documentation. The notebook documents are JSON files with built-in version control capabilities. In addition, users can render notebooks as static webpages for viewing by people who don't have Jupyter installed on their systems.</p> <p>Jupyter Notebook was the original tool -- it was initially part of the open source IPython interactive toolkit project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name, but in addition to supporting those three languages, Jupyter provides modular kernels for dozens of others. JupyterLab is a web-based UI added in 2018 that's more flexible and extensible than Jupyter Notebook.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Keras"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Keras</h2> <p>Keras is a programming interface that simplifies the use of several popular machine learning platforms by data scientists. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow, PyTorch and JAX. Keras initially supported multiple back ends, then was tied exclusively to TensorFlow starting with its 2.4.0 release in 2020. However, multiplatform support was restored in Keras 3.0, a full rewrite released in late 2023.</p> <p>As a high-level API, Keras was designed to accelerate <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-build-a-machine-learning-model-in-7-steps">implementation of machine learning models</a> -- in particular, deep learning <a href="https://www.techtarget.com/searchenterpriseai/definition/neural-network">neural networks</a> -- through a "quick and easy" development process, as the technology's documentation puts it. Keras enables data scientists to experiment during the model development process with less coding than other deep learning options require. Models can also be run on all the supported back-end platforms without any code changes.</p> <p>The Keras framework includes a sequential interface for creating relatively simple linear stacks of neural-network building blocks called <i>layers</i> with inputs and outputs, as well as a functional API for building more complex graphs of layers and writing deep learning models from scratch.</p> </section> <section class="section main-article-chapter" data-menu-title="7. Matlab"> <h2 class="section-title"><i class="icon" data-icon="1"></i>7. Matlab</h2> <p>Offered by software vendor MathWorks since 1984, Matlab is a high-level programming language and analytics platform for numerical computing, mathematical modeling and data visualization. It's primarily used by conventional engineers and scientists to analyze data, design algorithms and develop embedded systems for wireless communications, industrial control, signal processing and other applications. Users often pair it with a companion Simulink tool that offers model-based design and simulation capabilities.</p> <p>While Matlab isn't as widely used in data science applications as languages such as Python, R and Julia, it does support <a href="https://www.techtarget.com/searchenterpriseai/tip/AI-vs-machine-learning-vs-deep-learning-Key-differences">machine learning and deep learning</a>, predictive modeling, <a href="https://www.techtarget.com/searchbusinessanalytics/feature/6-big-data-benefits-for-businesses">big data analytics</a>, computer vision and other work done by data scientists. Data types and high-level functions built into the platform are designed to speed up exploratory data analysis and data preparation in analytics applications.</p> <p>Matlab -- short for <i>matrix laboratory</i> -- is considered relatively easy to learn and use. The platform includes prebuilt applications but also lets users build their own. It also provides a library of add-on toolboxes with discipline-specific software and hundreds of built-in functions, including the ability to visualize data in 2D and 3D plots.</p> </section> <section class="section main-article-chapter" data-menu-title="8. Matplotlib"> <h2 class="section-title"><i class="icon" data-icon="1"></i>8. Matplotlib</h2> <p>Matplotlib is an open source Python plotting library that's used to visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib. It works in Python scripts, the Python and IPython shells, Jupyter Notebook, JupyterLab, web application servers and various GUI toolkits.</p> <p>The library's large codebase can be challenging to master, but it's organized in a hierarchical structure that enables users to build visualizations primarily with high-level commands. The top component in the hierarchy is pyplot, a module that provides a state-machine environment and a set of simple plotting functions like those in Matlab.</p> <p>First released in 2003, Matplotlib also includes an object-oriented interface that supports low-level commands for more complex data plotting and can be used with pyplot or on its own. The library is primarily focused on creating 2D visualizations but offers an add-on toolkit with 3D plotting features.</p> </section> <section class="section main-article-chapter" data-menu-title="9. NumPy"> <h2 class="section-title"><i class="icon" data-icon="1"></i>9. NumPy</h2> <p>Short for <i>Numerical Python</i>, <a href="https://www.techtarget.com/whatis/definition/What-is-NumPy-Explaining-how-it-works-in-Python">NumPy</a> is an open source Python library that's used widely in scientific computing as well as data science and machine learning applications. The library consists of multidimensional array objects and processing routines that enable various mathematical and logic functions. It also supports linear algebra, random number generation and other operations.</p> <p>One of NumPy's core components is the N-dimensional array, or ndarray, which represents a collection of items that are the same type and size. An associated data-type object describes the format of the data elements in an array. The same data can be shared by multiple ndarrays, and data changes made in one can be viewed in another.</p> <p>NumPy was created in 2005 by combining and modifying elements of two earlier libraries. It's generally considered one of the most useful Python libraries due to its numerous built-in functions. NumPy is also known for its speed, which partly results from the use of optimized C code at its core. In addition, various other Python libraries are built on top of NumPy.</p> </section> <section class="section main-article-chapter" data-menu-title="10. Pandas"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10. Pandas</h2> <p>Another popular open source Python library, pandas is used to manipulate and analyze data. Built on top of NumPy, it features two primary data structures: Series, a one-dimensional array that holds data of any type, and DataFrame, a two-dimensional structure that can contain columns of different data types and supports data manipulation with integrated indexing. Both accept data from NumPy ndarrays and other inputs. A DataFrame can also incorporate multiple Series objects.</p> <p>Created in 2008, pandas provides built-in data visualization capabilities and exploratory data analysis functions. It supports file formats and languages such as CSV, SQL, HTML and JSON. Additional features include data aggregation and transformation, integrated handling of missing data and the ability to quickly merge and join data sets.</p> <p>To optimize its performance, key code paths in pandas are written in C or Cython, a superset of Python designed to provide C-like performance. The library can be used with various kinds of analytical and statistical data, including tabular, time series and text data sets.</p> </section> <section class="section main-article-chapter" data-menu-title="11. Python"> <h2 class="section-title"><i class="icon" data-icon="1"></i>11. Python</h2> <p>Python is the most widely used programming language for data science applications and scientific and numeric computing, and one of the most popular languages overall. The Python open source project's website describes it as a high-level interpreted, interactive, object-oriented language with a simple syntax, built-in data structures, and dynamic typing and binding capabilities. Python also supports both procedural and functional programming, as well as extensions written in C or C++.</p> <p>The multipurpose language is used for a wide range of data-driven tasks, including data analysis, data visualization, AI, <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-natural-language-processing-vs-machine-learning">natural language processing</a> and robotic process automation. Python includes an extensive library of functions and modules that can streamline application development, and thousands of third-party modules are available in the Python Package Index repository.</p> <p>Python 3.x is the recommended version for production use. Older Python 2.x releases can still also be downloaded from the Python website, but maintenance and technical support for the 2.x line ended in 2020.</p> </section> <section class="section main-article-chapter" data-menu-title="12. PyTorch"> <h2 class="section-title"><i class="icon" data-icon="1"></i>12. PyTorch</h2> <p>PyTorch is an open source Python library used to <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-PyTorch-vs-TensorFlow-for-AI-and-machine-learning">build and train deep learning models</a> based on neural networks. It was designed to be easier to use than Torch, a precursor machine learning framework written primarily in the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators.</p> <p>First released in 2017, PyTorch uses array-like tensors to encode model inputs, outputs and parameters. Its tensors are similar to NumPy's multidimensional arrays, which can be converted into tensors for processing in PyTorch, and vice versa. By default, PyTorch runs in an "eager mode" that executes computational operations immediately, an approach suited to model development. But operations can also be combined into computational graphs to deliver higher performance in production deployments.</p> <p>Other PyTorch components include an automatic differentiation package; a module for building neural networks; and ExecuTorch, a tool for deploying models on mobile phones and edge devices. In addition to the main Python API, PyTorch provides a C++ one that can be used as a separate front-end interface or to create extensions for Python applications. Users can run models built in PyTorch on CPUs, GPUs and custom hardware accelerators.</p> </section> <section class="section main-article-chapter" data-menu-title="13. R"> <h2 class="section-title"><i class="icon" data-icon="1"></i>13. R</h2> <p>The <a href="https://www.techtarget.com/searchbusinessanalytics/definition/R-programming-language">R programming language</a> is an open source environment designed for statistical computing and graphics applications as well as data manipulation, analysis and visualization. Many data scientists, academic researchers and statisticians use R to retrieve, cleanse, analyze and present data, making it one of the most popular languages for data science and advanced analytics.</p> <p>Thousands of user-created packages with libraries of code that enhance R's functionality are also available. One example is ggplot2, a well-known package for creating graphics that's part of the tidyverse collection of R-based data science tools. In addition, multiple vendors offer integrated development environments and commercial code libraries for R.</p> <p>R is an interpreted language, like Python, and it has a reputation for being relatively intuitive. It was created in the 1990s as an alternative version of S, a statistical programming language developed in the 1970s. R's name is both a play on S and a reference to the first letter of the names of its two creators.</p> </section> <section class="section main-article-chapter" data-menu-title="14. SAS"> <h2 class="section-title"><i class="icon" data-icon="1"></i>14. SAS</h2> <p>SAS is an integrated software suite for statistical analysis, advanced analytics, AI, BI and data management. Developed and sold by software vendor SAS Institute Inc., the platform helps users integrate, cleanse, prepare and manipulate data, then analyze it using different <a href="https://www.techtarget.com/searchbusinessanalytics/feature/15-common-data-science-techniques-to-know-and-use">statistical and data science techniques</a>. SAS supports a range of analytics tasks, from basic BI and data visualization to risk management, operational analytics, data mining, predictive analytics and machine learning.</p> <p>SAS development began in 1966 at North Carolina State University. Its use began to grow in the early 1970s, and SAS Institute was founded in 1976 as an independent company. The software was initially built for use by statisticians -- SAS was short for Statistical Analysis System. But over time, the SAS platform expanded to include a broad set of functionality.</p> <p>Development and marketing are now focused primarily on SAS Viya, a cloud-based version of the platform that was launched in 2016 and redesigned to be cloud-native in 2020. Viya supports Python, R, Java, Lua and REST APIs for programming. It also includes built-in AI governance features and SAS Viya Copilot, a conversational AI assistant that uses Microsoft Foundry services to help users generate SAS code and build AI and analytics models.</p> </section> <section class="section main-article-chapter" data-menu-title="15. Scikit-learn"> <h2 class="section-title"><i class="icon" data-icon="1"></i>15. Scikit-learn</h2> <p>Scikit-learn is an open source Python machine learning library that's built on the SciPy and NumPy scientific computing libraries and Matplotlib for plotting data. It supports both <a href="https://www.techtarget.com/searchenterpriseai/feature/Comparing-supervised-vs-unsupervised-learning">supervised and unsupervised machine learning</a> and includes numerous algorithms and models, called <i>estimators</i> in scikit-learn parlance. It also provides functionality for model fitting, selection and evaluation, as well as data preprocessing and transformation.</p> <p>Initially called scikits.learn, the library began as a Google Summer of Code project in 2007 and was publicly released in 2010. The first part of its name is short for <i>SciPy toolkit</i> and is also used by other SciPy add-on packages. Scikit-learn primarily works on numeric data that's stored in NumPy arrays or SciPy sparse matrices.</p> <p>The library's suite of tools also enables other tasks, such as loading data sets and creating workflow pipelines that combine data transformer objects and estimators. But scikit-learn has some limits due to design constraints. For example, it doesn't support deep learning or <a href="https://www.techtarget.com/searchenterpriseai/definition/reinforcement-learning">reinforcement learning</a>, and GPUs aren't supported by default. The library's website also says its developers "only consider well-established algorithms for inclusion."</p> </section> <section class="section main-article-chapter" data-menu-title="16. SciPy"> <h2 class="section-title"><i class="icon" data-icon="1"></i>16. SciPy</h2> <p>SciPy is another open source Python library that supports scientific computing. Short for <i>Scientific Python</i>, it features a set of mathematical algorithms and high-level commands and classes for data manipulation and visualization. The library is organized into more than a dozen subpackages that contain algorithms and functions for different scientific computing domains. That includes areas such as data optimization, integration and interpolation, as well as clustering, image processing and statistics.</p> <p>SciPy is built on top of NumPy and can operate on NumPy arrays. But it extends beyond NumPy's capabilities by providing additional array computing tools and specialized data structures, including sparse matrices and K-dimensional trees.</p> <p>SciPy also predates NumPy: It was created in 2001 by combining multiple add-on modules from the Numeric library, one of NumPy's two predecessors. Like NumPy, SciPy uses compiled code to optimize performance. In its case, most of the performance-critical parts of the library are written in C, C++ or Fortran.</p> </section> <section class="section main-article-chapter" data-menu-title="17. TensorFlow"> <h2 class="section-title"><i class="icon" data-icon="1"></i>17. TensorFlow</h2> <p>TensorFlow is an open source machine learning platform developed by Google that's particularly popular for building deep learning neural networks. Like PyTorch, TensorFlow structures data inputs as tensors akin to NumPy multidimensional arrays. It supports the same two processing methods as PyTorch, but in reverse: By default, TensorFlow creates computational graphs to flow data through a set of operations specified by developers, while also offering an eager execution programming environment that runs operations individually.</p> <p>Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and incorporates Keras as a high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations -- <i>ops</i>, for short -- can be built in C++.</p> <p>The platform also includes TFX, a module initially called TensorFlow that automates the deployment of production machine learning pipelines. In addition, it supports LiteRT, a runtime tool for mobile and IoT devices. TensorFlow models can run on CPUs, GPUs and Google's special-purpose Tensor Processing Units.</p> </section> <section class="section main-article-chapter" data-menu-title="18. Weka"> <h2 class="section-title"><i class="icon" data-icon="1"></i>18. Weka</h2> <p>Weka is an open source workbench that provides a collection of <a href="https://www.techtarget.com/whatis/definition/machine-learning-algorithm">machine learning algorithms</a> for use in data mining tasks. Weka's algorithms, called <i>classifiers</i>, can be applied directly to data sets without any programming via a GUI or a command-line interface that offers additional functionality. They can also be implemented through a Java API.</p> <p>The workbench can be used for classification, clustering, regression, and association rule mining applications. It also includes a set of data preprocessing and visualization tools. Weka supports integration with R, Python, Spark and other libraries, such as scikit-learn. For deep learning uses, an add-on package combines it with the Eclipse Deeplearning4j library.</p> <p>Weka is free software licensed under the GNU General Public License. It was developed at the University of Waikato in New Zealand starting in 1992. An initial version was rewritten in Java to create the current workbench, which was first released in 1999. Weka stands for the Waikato Environment for Knowledge Analysis. It is also the name of a flightless bird native to New Zealand that the technology's developers say has "an inquisitive nature."</p> </section> <section class="section main-article-chapter" data-menu-title="Data science and machine learning platforms"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Data science and machine learning platforms</h2> <p>Numerous software vendors offer commercially licensed platforms that provide integrated functionality for machine learning, AI and other data science applications. These product offerings are diverse: They include machine learning operations hubs, <a href="https://www.techtarget.com/searchenterpriseai/tip/Compare-top-AutoML-tools-for-machine-learning-workflows">automated machine learning platforms</a> and full-function analytics suites, with some products combining MLOps, AutoML and analytics capabilities. Many of the platforms incorporate some of the data science tools listed above.</p> <p>IBM SPSS Modeler, Matlab and SAS can also be counted among the data science platforms. Other prominent platform options for data science teams include the following technologies:</p> <ul class="default-list"> <li>Altair RapidMiner.</li> <li>Alteryx One.</li> <li>Amazon SageMaker.</li> <li>Anaconda.</li> <li>Azure Machine Learning.</li> <li>BigML.</li> <li>Databricks Data Intelligence Platform.</li> <li>Dataiku.</li> <li>DataRobot.</li> <li>Domino Enterprise AI Platform.</li> <li>Google Cloud Vertex AI Platform.</li> <li>H2O AI Cloud.</li> <li>IBM Watson Studio.</li> <li>Knime.</li> <li>Qubole.</li> <li>Saturn Cloud.</li> </ul> <p>Some platforms, such as Dataiku and H2O, are also available in free open source or community editions. Knime combines an underlying open source analytics platform with a commercial Knime Business Hub software package that supports team-based collaboration and analytics workflow automation, deployment and management capabilities.</p> <p><b>Editor's note: </b><i>TechTarget editors updated this article in February 2026 for timeliness and to add new information.</i></p> <p><i>Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.</i></p> </section> Numerous tools are available for data science applications. Read about 18, including their features, capabilities and uses, to see if they fit your analytics needs. https://cdn.ttgtmedia.com/rms/onlineimages/keyboard_g1140860048.jpg https://www.techtarget.com/searchbusinessanalytics/feature/15-data-science-tools-to-consider-using Mon, 16 Feb 2026 00:00:00 GMT <div> <p paraeid="{873fa489-e04a-4fa3-a52a-33efef0db521}{62}" paraid="183277101"><span xml:lang="EN-US" data-contrast="auto">AI laws and regulations vary widely across the globe, reflecting administrative priorities, national initiatives and industry competitiveness. These policies and laws are designed to enforce risk management and </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchenterpriseai/feature/How-to-ensure-AI-transparency-explainability-and-trust"><span xml:lang="EN-US" data-contrast="none"><span data-ccp-charstyle="Hyperlink">promote responsible AI best practices</span></span></a><span xml:lang="EN-US" data-contrast="auto">, as AI systems can pose serious risks around privacy, bias, discrimination and security.</span></p> </div> <div> <p paraeid="{873fa489-e04a-4fa3-a52a-33efef0db521}{103}" paraid="1523426970"><span xml:lang="EN-US" data-contrast="auto">Businesses that monitor global AI legislation can identify and proactively mitigate these risks by aligning the development, use and implementation of AI systems with legal standards and guidelines. Adhering to AI laws builds trust among stakeholders, customers and investors, while supporting innovation through </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchenterpriseai/feature/Leading-AI-with-ethics-The-new-governance-mandate"><span xml:lang="EN-US" data-contrast="none"><span data-ccp-charstyle="Hyperlink">ethical AI development</span></span></a><span xml:lang="EN-US" data-contrast="auto">. Noncompliance can lead to legal penalties, fines and reputational damage.</span></p> </div> <div> <p paraeid="{873fa489-e04a-4fa3-a52a-33efef0db521}{130}" paraid="654981478"><span xml:lang="EN-US" data-contrast="auto">With these stakes in mind, we've compiled the major AI laws, frameworks and policies across the U.S., the EU, Asia and the Americas in a tracker. This tracker breaks down proposed and enacted laws and frameworks by region, focusing on scope, jurisdiction and risk categories. To ensure accuracy and relevance, the tracker will be updated with relevant changes, as noted in the </span><strong><span xml:lang="EN-US" data-contrast="auto">Revision Log</span></strong><span xml:lang="EN-US" data-contrast="auto"> column.</span></p> </div> <div> <p paraeid="{873fa489-e04a-4fa3-a52a-33efef0db521}{176}" paraid="1458757526"><strong><span xml:lang="EN-US" data-contrast="auto">Editor's note: </span></strong><em><span xml:lang="EN-US" data-contrast="auto">An editor used AI tools to aid in the research of this tracker. Our expert editors always review and edit content before publishing.</span></em></p> </div> <div> <iframe title="Global AI regulations and policies" aria-label="Table" id="datawrapper-chart-njO6Y" src="https://datawrapper.dwcdn.net/njO6Y/4/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="777" data-external="1"></iframe> <script type="text/javascript">window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}});</script> </div> <div> <p paraeid="{873fa489-e04a-4fa3-a52a-33efef0db521}{198}" paraid="1445927687"><em><span xml:lang="EN-US" data-contrast="auto">Jennifer English is editorial director for TechTarget's AI &amp; Emerging Tech group.</span></em></p> </div> With AI, it's better to be proactive, not reactive. This tracker compiles the major AI legislation, laws and frameworks across the U.S., Europe, Asia and beyond. https://cdn.ttgtmedia.com/rms/onlineimages/legal_g1134366930.jpg https://www.techtarget.com/searchenterpriseai/tip/Global-AI-legislation-and-regulation-tracker Fri, 13 Feb 2026 15:57:00 GMT <p>Contact centers sit at the intersection of customer experience, brand trust and operational efficiency. As customer expectations rise and AI becomes embedded in service operations, the challenges facing contact centers have grown more complex -- and more consequential.</p> <p>Customer service has moved beyond single-channel support, with contact centers now expected to manage interactions across voice and digital channels while maintaining consistency, context and speed. Contact centers have <a href="https://www.techtarget.com/searchcustomerexperience/feature/Call-center-vs-contact-center-Whats-the-difference">evolved beyond mere call-handling hubs</a> into sophisticated, multichannel engagement centers that play a vital role in shaping customer experiences. With the advent of digital transformation, contact centers now integrate various communication platforms, including phone calls, email, chat, social media and video conferencing.</p> <p>The commercial landscape for businesses and customers is rapidly changing, <a href="https://www.techtarget.com/searchcustomerexperience/feature/Important-contact-center-AI-features-and-their-benefits">driven by technological advancements</a>, evolving customer expectations and the increasing importance of personalized service. Enterprises are under pressure to deliver consistent, high-quality customer interactions over different modes of communication, while managing costs and maintaining operational efficiency.</p> <p>Customer interactions now span multiple channels, yet customers expect consistent context, personalization and responsiveness regardless of how they engage. This complex environment necessitates a strategic approach to managing contact centers, addressing inherent challenges and <a href="https://www.techtarget.com/searchcustomerexperience/How-to-choose-a-contact-center-software-system">using technology to enhance customer service capabilities</a>.</p> <section class="section main-article-chapter" data-menu-title="Key contact center challenges and remedies"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Key contact center challenges and remedies</h2> <p>Providing different modes of interaction is among the many challenges for modern contact centers. Other issues include agent attrition, increased customer expectations, ever-growing customer queues, generalization of content, barriers to understanding and security.</p> <h3>1. Meeting customer expectations</h3> <p>Customers expect quick, personalized and seamless interactions across all channels. They also expect an interaction in one channel to be consistent with the experience they've had in other channels. They increasingly demand high levels of service and are less tolerant of delays, repeating their information and impersonal responses.</p> <p>Advanced CRM systems and AI-driven analytics can help understand, contextualize and anticipate customer needs, <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-comprehensively-personalize-the-customer-experience">enabling more personalized and consistent interactions</a>. Regularly updating service protocols to align with customer feedback is equally important.</p> <p>Meeting these expectations increasingly depends on how well organizations unify customer data and govern AI-assisted interactions across channels, not just on agent performance alone.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/addressing_the_demands_of_todays_complex_contact_centers-f.png 1280w" alt="Contact center challenges and remedies" height="487" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>For every challenge confronting contact centers, there's a remedy. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>2. High contact volumes and longer wait times</h3> <p>Managing the high volumes of customer contacts, especially during peak times, can lead to long wait times and customer dissatisfaction. When customers call into contact centers of certain businesses, the first response they might typically get is a recording, "We're currently experiencing high call volumes" -- at least during normal business hours. This kind of experience, exacerbated by limited staffing and inefficient call routing, frustrates customers.</p> <p>Implementing intelligent call routing and queuing systems can optimize resource allocation and reduce wait times. Most new systems <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-manage-remote-call-center-agents">enable contact center agents to work from home</a>, which increases the flexibility of companies deploying agents globally. Self-service options, such as chatbots and automated responses, can reduce contact volumes, but they also raise expectations for the quality and efficiency of the interactions that reach live agents.</p> <p>Chatbots can handle routine types of interactions, like password resets, quick orders and simple questions, but complex situations that require empathy and understanding are still best left to humans. Improvements in machine learning and AI can also help mitigate high contact volumes and wait times and provide customers with other ways to resolve their queries independently.</p> <h3>3. Personalization shortfalls and content generification</h3> <p>Generic responses and interactions usually fail to meet customer expectations for personalized service. This lack of personalization inevitably results in decreased customer satisfaction and loyalty.</p> <p>Using <a href="https://www.techtarget.com/searchcustomerexperience/tip/Customer-interaction-analytics-spurs-better-business-results">customer data and analytics to tailor interactions</a> and recommendations can improve personalization, but doing so effectively requires strong data governance and consistent context across channels. Training call center agents to express empathy and use customer information effectively during their interactions is especially important. New large language models can improve the quality of agent responses by combining the specifics of customer data with best practices in knowledge bases.</p> <h3>4. Language barriers</h3> <p>Contact centers often serve a diverse, global customer base. Language barriers can impede effective communication, leading to misunderstandings and frustration. Any enterprise that aspires to be global must deal with this issue. Even companies that see themselves as local will become global when they put their presence on the web.</p> <p>Hiring multilingual agents and providing language training can bridge communication gaps. Additionally, real-time translation services and AI-powered language tools have come a long way and can facilitate smoother interactions.</p> <h3>5. Agent attrition</h3> <p>High turnover rates among contact center agents <a href="https://www.techtarget.com/searchcustomerexperience/tip/Why-contact-centers-have-high-turnover-and-how-to-combat-it">pose a significant challenge</a>. Increased job openings and competition for talent in good economies can only make this problem worse. Attrition is usually costly, impacting operational efficiency and the quality of customer interactions. Factors contributing to high attrition include job stress, lack of career advancement opportunities and inadequate compensation.</p> <p>In many environments, tool sprawl and cognitive overload also contribute to burnout, making technology simplification as important as compensation and career development.</p> <p>Good customer service is vital to retention and brand loyalty. <a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-agent-training-programs">Implementing comprehensive training programs</a>, offering competitive salaries and creating clear career progression paths can help reduce attrition. Providing a supportive work environment and recognizing agent contributions also play a crucial role in retaining talent. Technology has made it possible for more agents to work remotely, enabling companies to find the best qualified representatives wherever they're located.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/average_call_center_agent_salaries-f.png 1280w" alt="Contact center agent salaries in the U.S." height="403" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact center agents in some regions demand higher than average salaries. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>&nbsp;6. Lack of subject matter expertise</h3> <p>Agents often face complex queries requiring specialized knowledge. As the "first line of defense" in resolving customer inquiries, it's often difficult, if not impossible, for contact center agents to achieve mastery or even appear to be knowledgeable in all aspects of company products. The result could be incorrect or inadequate information conveyed to the customer.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/answer/5-ways-to-improve-call-center-agent-performance">Continuous training and access to a centralized knowledge base</a> can empower remote work agents with the necessary information to handle complex queries effectively. Encouraging collaboration and knowledge sharing among agents can also enhance overall understanding.</p> <h3>7. Quantitative and qualitative performance metrics</h3> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Top-7-call-center-agent-performance-metrics-to-track">Accurately measuring and analyzing contact center performance</a> is essential for continuous improvement. Traditional metrics often don't fully capture the quality of customer interactions or agent performance since measuring customer satisfaction can often be subjective.</p> <p>Adopting a comprehensive set of KPIs that include quantitative <i>and</i> qualitative metrics can provide a more accurate picture of performance. Incorporating customer feedback and sentiment analysis into performance reviews can also provide valuable insights and a more holistic view of contact center effectiveness.</p> <h3>8. Data access vs. protection</h3> <p>Contact centers store and handle sensitive customer information, making data security a foundational requirement for customer trust rather than a secondary compliance concern. As the types and frequency of interactions increase, breaches are becoming more frequent and consequential, leading to significant financial and reputational damage. More sophisticated deep fakes are rendering voice recognition ineffective as a method of customer verification.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-security-best-practices-to-protect-customer-data">Implementing comprehensive cybersecurity measures</a>, including encryption, multifactor authentication, and regular security audits, safeguard customer data. Sensitive customer data can be better protected through advanced security protocols, security tools such as system scanners with <a href="https://www.techtarget.com/searchcustomerexperience/tip/How-to-train-agents-on-call-center-fraud-detection">data loss prevention, and fraud detection</a>. Most companies need to adopt zero trust architectures and principles, and agents need to be trained on data protection protocols. It should be standard practice to have a culture of security awareness, including periodic companywide security training.</p> <p>Across these challenges, AI increasingly acts as both a solution and a source of new complexity, raising the bar for data quality, governance and trust in contact center operations.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg"> <img data-src="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg 960w,https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg 1280w" alt="Multifunctional contact centers" height="288" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact centers are evolving into complex facilities that meet business and customer needs. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Build on flexibility, scalability and humanity"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Build on flexibility, scalability and humanity</h2> <p>Addressing contact center challenges requires more than incremental tooling changes. As customer expectations rise and AI reshapes service interactions, contact centers must balance efficiency with empathy, automation with oversight, and data access with security. Organizations that approach these challenges strategically -- rather than tactically -- are better positioned to turn their contact centers into long-term assets rather than ongoing cost centers.</p> <p><b>Editor's note:</b><i>&nbsp;This article has been updated to reflect the changing nature of modern contact center challenges.</i></p> <p><i>Jerald Murphy is senior vice president of research and consulting at Nemertes Research. He has more than three decades of technology experience, including neural networking research, integrated circuit design, computer programming, global data center designing and CEO of a managed services company.</i></p> </section> Modern contact centers face persistent challenges around customer expectations, staffing and data access. Addressing them requires more than incremental operational fixes. https://cdn.ttgtmedia.com/rms/onlineimages/chatbot_g1250576636.jpg https://www.techtarget.com/searchcustomerexperience/tip/Contact-center-challenges-and-how-to-overcome-them Wed, 11 Feb 2026 00:00:00 GMT <p>Although the terms <i>call center</i> and <i>contact center</i> are often used interchangeably, the distinction has become more consequential as organizations invest in omnichannel engagement, automation and AI-driven customer support.</p> <p>In a 2025 Gartner survey of service and support leaders, 77% said they feel pressure from senior executives to deploy AI, and 75% reported increased budgets for AI initiatives compared to the prior year. What was once a difference in channels now shapes technology strategy, data use and customer experience outcomes.</p> <p><a href="https://www.techtarget.com/searchcustomerexperience/definition/Call-Center">Call centers</a> were once the gold standard for customer service, but advances in digital communication, customer data platforms and automation have steadily reshaped how businesses interact with customers. That shift is reinforced by sustained enterprise investment: A 2024 Forrester survey found that 67% of AI decision-makers planned to increase spending on generative AI initiatives in the year ahead.</p> <p>As analog and simple telephone communication gave way to multiple digital channels, many call centers by necessity morphed into more complex, multifunctional <a href="https://www.techtarget.com/searchcustomerexperience/definition/contact-center">contact centers</a>.</p> <p>A call center consists of customer service professionals, known as call center agents, who handle inbound and outbound calls. Agents who take inbound calls&nbsp;help customers with account inquiries, scheduling, technical support, complaints and questions about products and services. Outbound calls focus on telemarketing, fundraising, lead generation, scheduling, customer retention and debt collection.</p> <p>Call centers continue to provide dependable, real-time customer service through voice interactions. However, they are typically optimized for phone-based workflows and limited customer context compared with modern contact centers.</p> <p>While many contact centers include traditional call handling, they are designed to orchestrate interactions across voice and digital channels, unify customer context and route engagements based on intent and history. By using multiple channels, <a href="https://www.techtarget.com/searchcustomerexperience/feature/Benefits-of-omnichannel-marketing">companies can collect more marketing data</a> and enable customers to interact with the business in more convenient ways.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/5_differences_between_call_centers_and_contact_centers-f.png 1280w" alt="Difference between call centers and contact centers" height="333" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Call centers and contact centers share some similarities, but their differences are noteworthy. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <section class="section main-article-chapter" data-menu-title="Call centers vs. contact centers"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Call centers vs. contact centers</h2> <p>Call centers and contact centers provide customer service and outreach, but they differ in several key areas, including channels of communication, types of customer data collected, customer self-service (<a href="https://www.techtarget.com/whatis/definition/customer-self-service-CSS">CSS</a>) capabilities, agent skills and job requirements, and technologies and applications.</p> <h3>Channels of communication</h3> <p>Call centers emerged at a time before digital channels and they continue to use the phone as the major channel of communication. Still, they benefit many businesses because phone calls with live agents can offer a&nbsp;personalized experience&nbsp;that other channels often lack. However, the multiple channels provided by contact centers offer customers the convenience of interacting with a company on the channel of their choice.</p> <h3>Types of customer data collected</h3> <p>Because contact centers provide more communication channels than call centers, they can collect more diverse customer data, enhance&nbsp;<a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-profiling">customer profiling</a>, provide targeted customer support and improve customer experiences. Contact centers, for example, can use social media data to determine customer affiliations and attitudes that might not be apparent over the phone.</p> <p>Still, call centers can use speech analysis software to analyze phone calls and gain some degree of insight into a customer's behavior and buying patterns.</p> <h3>Customer self-service</h3> <p>For&nbsp;CSS capabilities, most call centers use interactive voice response (<a href="https://www.techtarget.com/searchcustomerexperience/definition/Interactive-Voice-Response-IVR">IVR</a>) systems -- automated phone assistants that respond to voices and keypad entries. IVR systems can route callers to relevant agents and perform simple tasks, such as reorders, but they can also annoy customers with lengthy menu options that fail to address specific needs.</p> <p>Contact center CSS&nbsp;goes beyond IVR and includes chatbots, FAQ webpages, forums and online knowledge bases to help customers resolve inquiries independently. Contact center CSS can also provide automated text messages that confirm or cancel appointments and mobile applications where customers can place or change orders. CSS tools can help reduce customer wait times, live agent workloads and operating costs.</p> <h3>Agent skills and job requirements</h3> <p><a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-agent-training-programs">Customer service skills and experience are essential</a> for call center and contact centers agents to solve problems and provide customers with the intangibles of empathy, patience and friendliness. Contact center agents require additional skills to handle interactions over multiple channels, including phone, email, live chat, text messaging and social media. Their job might require reading comprehension, sound writing skills, social media etiquette and multitasking capabilities.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg"> <img data-src="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/crm-contact_centers_mobile.jpg 960w,https://www.techtarget.com/rms/onlineImages/crm-contact_centers.jpg 1280w" alt="multidimensional contact centers image" height="288" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Contact centers are seen as a multidimensional force for businesses. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Why the distinction matters now"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Why the distinction matters now</h2> <p>As organizations adopt advanced analytics, automation and GenAI, the gap between voice-centric call centers and omnichannel contact centers continues to widen. Contact centers are increasingly treated as engagement platforms that unify data, AI and human agents across channels, rather than as expanded call-handling operations.</p> <h3>Technologies and applications</h3> <p>Automation is also changing expectations for customer support. According to Metrigy <a target="_blank" href="https://metrigy.com/the-evolving-role-of-ai-in-customer-experience-insights-from-metrigys-2024-25-study/" rel="noopener">research</a>, AI is fully automating roughly 20% of customer interactions today, and CX leaders expect that figure to rise to approximately 37% by 2028. As automation expands, contact centers require broader data integration, orchestration and governance capabilities that extend beyond traditional call center models.</p> <p>Aside from the basic requirements of phones, computers and headsets, call center technologies include the following:</p> <ul type="disc" class="default-list"> <li><strong>IVR.</strong>&nbsp;Automated phone assistants select the right agent or department to service a customer based on voice and keypad responses.</li> <li><strong>Automated call distributor (ACD).&nbsp;</strong>After an IVR determines the best route for the caller, an ACD automatically transfers the caller to that agent or department.</li> <li><strong>Speech analysis software.&nbsp;</strong>These tools can <a href="https://www.techtarget.com/searchcustomerexperience/tip/Sentiment-analysis-Why-its-necessary-and-how-it-improves-CX">analyze calls to detect customer emotions</a>, such as satisfaction and anger. They also determine when to follow up with unsatisfied customers.</li> <li><strong>Workforce management (WFM) system.</strong>&nbsp;Certain days in a call center can be busier than others. WFM systems can&nbsp;schedule the appropriate number of agents for each day.</li> <li><strong>Enhanced internet access.&nbsp;</strong>Agents who work remotely need a fast and secure connection to use call center software, which might require internet upgrades.</li> </ul> <p>Although some call center and contact center technologies overlap, the multifunctional aspects of contact centers, together with <a href="https://www.techtarget.com/searchcustomerexperience/opinion/How-contact-center-modernization-plays-into-AI-strategies">GenAI's penetration into the contact center</a>, dictate implementing additional technologies and applications, including the following:</p> <ul type="disc" class="default-list"> <li><strong>Email response management system.</strong>&nbsp;These systems can organize, track and archive large volumes of emails.</li> <li><strong>Omnichannel routing.&nbsp;</strong>Because contact centers use multiple channels, agents might struggle to manage various interactions. Omnichannel routing&nbsp;uses AI to identify a customer's intent&nbsp;and forward all requests to a live agent, regardless of the channel.</li> <li><b>Advanced analytics</b><strong>.</strong>&nbsp;This capability includes various AI technologies and analysis techniques, providing a holistic view of the customer journey and predictive insights into a customer's future choices.</li> <li><strong>Channel reports.&nbsp;</strong>Reporting software collects raw data across channels to create key performance indicators (KPIs), such as first contact resolution and customer effort scores.&nbsp;Managers can monitor KPIs&nbsp;to ensure quality assurance across channels.</li> </ul> <p>Gartner <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290" rel="noopener">forecasts</a> that agentic AI will autonomously resolve the majority of common customer service issues over time, reducing operational costs and reshaping agent roles. Against that backdrop, GenAI is expected to enhance automated customer support through chatbots and virtual assistants, personalize interactions with tailored responses, improve agent effectiveness with real-time assistance and simulation training, and accelerate content creation for FAQs and knowledge bases.</p> <p>For organizations evaluating customer support strategies, the difference between a call center and a contact center is no longer semantic. It reflects how customer interactions are captured, analyzed and acted on across the business. Companies that approach contact centers as integrated engagement platforms -- rather than as upgraded call centers -- are better positioned to scale service quality, govern automation responsibly and adapt to evolving customer expectations.</p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/SRKWbLNV4bs?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> <p><strong>Editor's note:</strong><i> This article has been updated to provide the latest information on call centers and contact centers</i><i> and provide enterprise technology buyers up-to-date insights on market advancements.</i></p> <p><i>Tim Murphy is a former site editor for TechTarget's Customer Experience and Content Management sites. He now covers broader CIO topics.</i></p> <p><i>Ron Karjian is an industry editor and writer at TechTarget covering business analytics, artificial intelligence, data management, security and enterprise applications.</i></p> </section> Call centers focus on voice support, while contact centers manage customer interactions across channels using shared data, automation and AI to shape modern CX strategies. https://cdn.ttgtmedia.com/rms/onlineimages/customer_service11.jpg https://www.techtarget.com/searchcustomerexperience/feature/Call-center-vs-contact-center-Whats-the-difference Wed, 11 Feb 2026 00:00:00 GMT <p>As agentic AI becomes the new means of building business intelligence tools and analyzing data, Qlik is keeping up with the competition.</p> <p>On Tuesday, the vendor made its agentic experience generally available in Qlik Cloud.</p> <p>The suite of capabilities includes the <a href="https://www.techtarget.com/searchbusinessanalytics/news/366623785/Qlik-unveils-agentic-AI-capabilities-launches-lakehouse">previously available Qlik Answers</a> to provide a natural language interface, powered by the Qlik Analytics Engine, for exploring and analyzing data -- structured <a target="_blank" href="https://mitsloan.mit.edu/ideas-made-to-matter/tapping-power-unstructured-data" rel="noopener">and unstructured</a> -- and Discovery Agent, a tool that monitors data and metrics for changes and anomalies so data teams can quickly act to address issues or take advantage of competitive opportunities.</p> <p>In addition, Qlik's agentic experience features a <a href="https://www.techtarget.com/searchdatamanagement/feature/One-year-of-MCP-Support-a-must-for-data-management-vendors">Model Context Protocol</a> (MCP) server so that customers can connect AI applications they've developed to data in Qlik to inform decisions, and Data Products for Analysis, a feature that monitors curated, governed datasets to ensure quality.</p> <p>Beyond launching its agentic experience, Qlik recently joined <a href="https://www.techtarget.com/searchdatamanagement/news/366631576/New-consortium-to-aid-AI-by-standardizing-semantic-modeling">the Open Semantic Interchange</a> , a consortium of data management and analytics vendors committed to creating an open standard for semantic data modeling to make data more consistent and discoverable for AI.</p> <p>Vendors such as <a href="https://www.techtarget.com/searchbusinessanalytics/news/366622614/Tableau-enters-the-agentic-AI-era-with-the-launch-of-Next">Tableau</a>, ThoughtSpot and Domo likewise provide natural language interfaces and agents that simplify complex, time-consuming analytics tasks. Similarly, numerous vendors -- including <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637466/GoodData-launches-MCP-server-to-fuel-AI-powered-analysis">GoodData</a> and Sisense along with the aforementioned trio -- now provide MCP servers.</p> <p>However, whether or not Qlik is the first to offer agentic AI features and tools that enable AI development, the capabilities are significant for the vendor's users and show that Qlik is keeping up with evolving AI trends, according to Mike Leone, an analyst at Omdia, a division of Informa TechTarget.</p> <p>"This feels like the necessary evolution of what they started with Qlik Answers," he said. "We saw them tackle unstructured data first. Now they are connecting that brainpower to structured data and the agents people will be using every day via MCP. They understand the future is injecting trusted context directly into the messy reality of operational workflows rather than forcing users back into a dashboard."</p> <p>David Menninger, an analyst at ISG Research, likewise noted that Qlik's agentic experience shows that the vendor is evolving to meet current customer needs.</p> <p>"It's significant in the sense that agentic AI is the battlefield right now," he said. "Enterprises expect their software providers to be adding these types of features."</p> <p>Based in King of Prussia, Penn., Qlik is a longtime analytics vendor that added a data integration platform beginning with <a href="https://www.techtarget.com/searchbusinessanalytics/news/252445579/Qlik-Podium-acquisition-aims-to-boost-BI-data-management">its 2018 acquisition of Podium Data</a>. In response to <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">surging interest in AI development</a> over the past three years, Qlik, like many data management and analytics vendors, has added AI capabilities and a suite for AI development that now includes its agentic experience.</p> <section class="section main-article-chapter" data-menu-title="AI-powered analytics"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI-powered analytics</h2> <p>A strong data foundation is a prerequisite for any AI initiative. Without high-quality data that can be trusted to inform agents and other AI tools, projects will never make it past the pilot stage. In fact, the absence of a trustworthy data foundation is one of the main reasons <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">many AI projects fail</a>.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> We saw them tackle unstructured data first. Now they are connecting that brainpower to structured data and the agents people will be using every day via MCP. They understand the future is injecting trusted context directly into the messy reality of operational workflows rather than forcing users back into a dashboard. </figure> <figcaption> <strong>Mike Leone</strong>Analyst, Omdia </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>As interest in AI development has increased over the past few years, Qlik has made it a priority to help customers create a strong data foundation.</p> <p>The vendor <a href="https://www.techtarget.com/searchdatamanagement/news/366595446/Qlik-launches-Talend-Cloud-aims-to-ensure-data-is-trusted">launched Qlik Talend Cloud</a> in July 2024 to help users integrate their data, <a href="https://www.techtarget.com/searchbusinessanalytics/news/366610199/Qlik-AutoML-update-targets-trust-with-visibility-simplicity">updated its AutoML capabilities</a> in September 2024 to provide greater visibility into machine learning model performance and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366626961/Qlik-adds-trust-score-to-aid-data-prep-for-AI-development">introduced an AI Trust Score</a> in July 2025 to help customers understand the preparedness of their data.</p> <p>Now, with the general availability of its agentic experience, Qlik is delivering features that build on a strong data foundation to generate insights.</p> <p>Qlik Answers calls on an enterprise's data foundation to enable AI-powered data exploration and analysis, providing citations and explanations about how it reached its conclusion so users can audit responses. The MCP server enables agents to connect to trusted data to inform their actions. And the Discovery Agent continuously monitors the data foundation for potential insights.</p> <p>Meanwhile, Data Products for Analytics helps ensure that reusable <a href="https://www.techtarget.com/searchdatamanagement/opinion/Trusted-data-is-the-foundation-of-data-driven-decisions-GenAI">data remains trustworthy</a>.</p> <p>Regarding the impetus for developing the features that comprise the agentic experience, customer conversations and market observations were each factors, according to Drew Clarke, Qlik's executive vice president of product and technology.</p> <p>"Customer feedback was the spark, but the bigger driver is where enterprise AI is headed," he said. "Teams do not just want another chat interface. They want systems that can reason across analytics and documents, keep permissions intact and explain what they did."</p> <p>Although it has been generally available for a year-and-a-half, Qlik Answers is perhaps the most valuable feature of the new agentic experience given that it provides users with governed, explainable responses to their queries, according to Menninger.</p> <p>Regarding Qlik's competitive standing now that its agentic experience is generally available, he added that many analytics vendors now <a href="https://www.techtarget.com/searchbusinessanalytics/news/366636078/ThoughtSpot-automates-full-platform-with-new-Spotter-agents">provide similar capabilities</a> with subtle differences that give different vendors advantages in specific niches of AI-driven analysis.</p> <p>"At this point, there is very little new under the 'AI sun,'" Menninger said. "All the software providers are chasing the same goals of making their products easier to use and helping to automate more business processes through agentic AI. Each will have its own advantages, depending on its existing strengths. For instance, Qlik has the advantage over some of its competitors with its data capabilities."</p> <p>Leone likewise noted that competitive advantages are subtle, with Qlik's main differentiator its integration of AI-powered analysis with <a href="https://www.techtarget.com/searchbusinessanalytics/news/366587232/Trusted-data-key-for-Qlik-as-it-develops-foundation-for-AI">a strong data foundation</a>.</p> <p>"The differentiator isn't the agentic capability itself since the entire market is heading in that direction," he said. "The real value is likely how they are layering this on top of the foundation they built with Qlik Answers. By ensuring the data feeding those agents is grounded in that same governance and lineage, they are tackling the trust gap that is currently stalling a lot of real-world deployments."</p> <p>Highlight capabilities, meanwhile, include the MCP server and Data Products for Analytics, Leone continued.</p> <p>"You need those curated data products to ensure the AI isn't just guessing, and the MCP server is what finally lets that trusted intelligence travel into the apps people actually use," he said.</p> </section> <section class="section main-article-chapter" data-menu-title="Looking ahead"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Looking ahead</h2> <p>With the agentic experience now available, once of Qlik's product development priorities is advancing the feature set by adding more specialized agents and broadening its MCP server's capabilities to connect to more data sources, according to Clarke.</p> <p>Other focal points include improving its Open Lakehouse to better support high data volumes and <a href="https://www.techtarget.com/searchbusinessanalytics/news/252529637/Data-streaming-platforms-fuel-for-agile-decision-making">streaming data</a>, and further connecting its data integration and analytics capabilities to provide trusted foundation for AI.</p> <p>While Qlik's agentic experience includes an MCP server to connect agents with data sources, it does not feature a framework such as <a target="_blank" href="https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/" rel="noopener">Agent2Agent Protocol</a> that helps govern how agents interact with one another. &nbsp;Menninger noted that it is important for Qlik and its peers to add such capabilities.</p> <p>"Most vendors lack strong multi-agent orchestration and coordination capabilities," he said. "MCP is a starting point, but agent-to-agent protocols are also needed as well as the ability to orchestrate the various agents and their activities."</p> <p>Leone, meanwhile, suggested that Qlik demonstrate that its governance capabilities not only enable agents to respond to user prompts, but also safely <a href="https://www.techtarget.com/searchenterpriseai/news/366617713/Autonomous-AI-agents-on-the-rise">act on their own</a> so that customers can improve their efficiency by turning over time-consuming tasks to trusted agents.</p> <p>"The next frontier for them is proving that these agents can safely take autonomous action," he said. "We're seeing agents that can answer questions, but the real value unlocks when those agents can confidently fix a problem without a human double-checking every step. If Qlik can prove their governance makes that level of automation safe, they solve a massive operational bottleneck."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</i></p> </section> The vendor's latest capabilities, including an insight-generating agent and an MCP server, show that it is evolving to keep pace with current trends in data and analytics. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a373894778.jpg https://www.techtarget.com/searchbusinessanalytics/news/366638938/Qlik-launches-agentic-experience-to-fuel-AI-powered-analysis Tue, 10 Feb 2026 08:30:00 GMT <p>The dramatic growth of <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI">AI-generated content</a> is reshaping business risk faster than most organizations can adapt. Among the risks related to AI-generated content are intellectual property theft, consumer trust erosion and <a href="https://www.techtarget.com/whatis/definition/deepfake">deepfake</a> attacks that are happening with alarming regularity.</p> <p>According to Gartner's 2025 cybersecurity leaders <a target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2025-09-02-why-cios-cannot-ignore-the-rising-tide-of-deepfake-attacks" rel="noopener">survey</a> of nearly 500 senior business executives, 62% of organizations experienced at least one deepfake attack in the previous 12 months, 43% reported at least one deepfake audio call incident and 37% had experienced deepfakes in video calls.</p> <p>The cost of deepfakes can be painful. A<a href="https://www.techtarget.com/searchcio/tip/How-executives-can-counter-AI-impersonation"> single incident cost global engineering firm Arup $25.6 million</a> in January 2024 when a finance employee joined what appeared to be a video conference with the CFO and other colleagues, all of which were AI-generated deepfakes.</p> <p>The inconvenient truth is AI-generated forgeries have become increasingly difficult to detect.</p> <p><a href="https://www.techtarget.com/searchenterpriseai/definition/AI-watermarking">AI watermarks</a> are a potential solution, providing a degree of verifiable authenticity to AI-generated content. There's momentum behind watermark use as new regulations, such as the EU AI Act and California's AI Transparency Act, include watermark use requirements.</p> <section class="section main-article-chapter" data-menu-title="How AI watermarking works"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How AI watermarking works</h2> <p>Watermarking isn't a new concept. It's been used for centuries on banknotes, postage stamps and official documents to prove authenticity and limit forgery risk. The digital age has <a href="https://www.techtarget.com/searchvirtualdesktop/answer/Can-IT-add-digital-watermarks-to-its-virtual-desktops">adapted this time-tested approach</a> for new media.</p> <p>AI watermarking embeds a recognizable, unique signal into content during or after generation, creating a digital signature that verifies authenticity without degrading quality. The process involves two stages, encoding watermarks during model training and detecting them after content generation, and requires two primary approaches that have emerged:</p> <ol class="default-list"> <li><b>Metadata-based systems. </b>The Coalition for Content Provenance and Authenticity (<a href="https://www.techtarget.com/whatis/definition/Coalition-for-Content-Provenance-and-Authenticity-C2PA">C2PA</a>) oversees an open source technical standard used to verify the origin and subsequent history of media. It cryptographically embeds information about who created content, when, where and with what tools. This data travels with the file and can be verified using free tools, with any tampering becoming immediately evident. The C2PA coalition now includes Adobe, BBC, Google, Meta, Microsoft, OpenAI, Publicis Groupe, Sony and Trupic.</li> <li><b>Pattern-based systems. </b>These watermarks alter content in ways imperceptible to people but detectable by algorithms. Google DeepMind's SynthID subtly biases the words an AI chooses during text generation, creating statistical patterns invisible to readers but identifiable through analysis. SynthID watermarks content across text, images, audio and video.</li> </ol> </section> <section class="section main-article-chapter" data-menu-title="Business benefits of watermarking"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Business benefits of watermarking</h2> <p>Watermarking has a range of potential business benefits.</p> <h3>Regulatory compliance and risk management</h3> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The strongest argument for adopting watermarking is strategic readiness, building operational experience and governance muscle memory. </figure> <figcaption> <strong>Nik Kale</strong>Cisco CX Engineering principal engineer </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>The EU AI Act is among the first to <a href="https://www.techtarget.com/searchenterpriseai/tip/Is-your-business-ready-for-the-EU-AI-Act">include an AI watermarking mandate</a>. It requires machine-readable marking for AI-generated outputs by August 2026. Penalties for failing to use them reach €15 million or 3% of the preceding financial year's global turnover, whichever is higher. California's AI Transparency Act mandated that AI providers with more than one million monthly users have invisible watermarks in place by January 2026.</p> <p>Complying with these <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">emerging regulations</a> is the strongest rationale for acting now, according to Nik Kale, principal engineer at Cisco CX Engineering. Companies that implement watermarking will be better prepared operationally, even if underlying techniques remain imperfect, he said.</p> <p>"The strongest argument for adopting watermarking is strategic readiness, building operational experience and governance muscle memory ahead of future regulatory and policy requirements," Kale said.</p> <p>Jean-Claude Renaud, CEO of Winston AI, an AI content detection technology provider, had a similar take: "Watermarking makes sense as part of a broader trust and governance stack, not as a silver bullet," he explained. "Implemented early, it helps businesses prepare for regulation, partner requirements and future provenance standards without scrambling later."</p> <h3>Authenticity and IP protection</h3> <p>With the growing volume of deepfake incidents, watermarking provides a verification infrastructure. Businesses that attempt to track the provenance of AI-generated content are showing a level of maturity with the technology to customers, partners and regulators, Kale said. "It isn't a guarantee of security," he added, "but it signals seriousness and preparedness when used as part of a broader governance program."</p> <h3>Customer trust and transparency</h3> <p>Businesses using AI-generated content must be concerned about how any <a href="https://www.techtarget.com/searchcio/tip/AI-transparency-What-is-it-and-why-do-we-need-it">lack of transparency</a> has potential to erode trust. Watermarks can help mitigate this risk. "To customers, regulators and enterprise buyers, watermarking shows intent, you're taking content transparency seriously, even if the tooling isn't perfect yet," Renaud said.</p> <h3>Sector-specific benefits</h3> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Early adopters building watermarking infrastructure ahead of regulatory deadlines gain compliance advantages and brand trust positioning. </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>For <a href="https://www.techtarget.com/searchenterpriseai/opinion/An-instructors-perspective-on-the-use-of-AI-in-education">AI use in education</a> and healthcare, watermarking is taking on added significance. Universities and colleges could use it to help alleviate concerns about students no longer creating original content for courses, said Tiffany Masson, founder of AI consultancy Falkovia. Pressure will continue for them to prove that systems are in place with relevant policies and procedures, she said. In healthcare, she noted, <a href="https://www.techtarget.com/healthtechanalytics/feature/AI-in-healthcare-A-guide-to-improving-patient-care-with-AI">transparency is critical for healthcare providers</a> using AI-generated recommendations to ensure ethical healthcare for patients.</p> <h3>Competitive differentiation</h3> <p>Early adopters building watermarking infrastructure ahead of regulatory deadlines gain compliance advantages and brand trust positioning. This approach demonstrates proactive governance to customers and business partners.</p> </section> <section class="section main-article-chapter" data-menu-title="Challenges and limitations of AI watermarking"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Challenges and limitations of AI watermarking</h2> <p>While watermarking offers important benefits, the current technology faces constraints that businesses must understand.</p> <h3>Reliability issues</h3> <p>The track record shows persistent reliability issues. OpenAI launched an AI text detector for ChatGPT in January 2023, but <a target="_blank" href="https://openai.com/index/new-ai-classifier-for-indicating-ai-written-text/" rel="noopener">shut it down</a> six months later, citing its low rate of accuracy. This failure underscores a fundamental challenge: Watermarks are often easy to remove or degrade, particularly through routine content workflows.</p> <p>"Most watermarking systems hold up reasonably well against light compression or simple re-encoding," Renaud said. "Once you introduce cropping, resizing, screenshots, format hopping or copy-paste workflows, reliability drops quickly."</p> <p>The bigger issue is that watermarking only works when the entire pipeline cooperates, Renaud added. If a single step strips <a href="https://www.techtarget.com/whatis/definition/metadata">metadata</a>, flattens content or re-renders it, the watermark is gone.</p> <p>Enterprises must set realistic expectations, Kale said. "From an enterprise risk management perspective," he explained, "organizations should consider watermarking as a deterrent and a signal of intent, rather than a reliable method to prevent tampering or to serve as forensic evidence."</p> <h3>False positive risks</h3> <p>Malicious actors could add watermarks to authentic human-created content to cast doubt on its legitimacy. Random chance could also produce patterns that mimic watermarks, leading to incorrect accusations. These risks complicate business decision-making around content verification and dispute resolution.</p> <h3>Barriers to adoption</h3> <p>Technical fragility is limiting widespread adoption of watermarking technology. Beyond that, the following main obstacles are preventing wider adoption, according to Renaud:</p> <ol class="default-list"> <ol class="default-list"> <ul class="default-list"> <li><b>Fragmentation.</b> There's no universal standard that works across models, platforms and downstream tools. A watermark applied in one system might be unreadable in another.</li> <li><b>False confidence.</b> Some businesses assume watermarks equal protection or traceability, when, in fact, they're easy to remove, intentionally and unintentionally. This mindset creates a dangerous gap between perception and reality.</li> <li><b>Lack of immediate ROI.</b> Watermarking is mostly defensive. It doesn't drive revenue, improve performance or enhance user experience on its own.</li> </ul> </ol> </ol> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/wMnVHeXPb6c?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="The future of AI watermarking"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The future of AI watermarking</h2> <p>Watermarking is evolving rapidly. Breakthrough technologies like watermark <a href="https://www.techtarget.com/searchbusinessanalytics/definition/Ensemble-modeling">ensembling</a> let multiple watermarks coexist without overwriting, creating stronger provenance chains. <a href="https://www.computerweekly.com/news/252516064/Danish-researcher-explains-zero-knowledge-proofs-and-post-quantum-encryption">Zero-knowledge-proof</a> systems enable verification without exposing detection algorithms.</p> <p>Yet, an arms race is underway. Researchers have found watermarks can be removed through sophisticated attacks. Commercial bypass services advertise high success rates. The realistic goal, according to experts, is to raise barriers rather than achieve perfect detection.</p> <p><a href="https://www.techtarget.com/searchdatamanagement/tip/AI-data-governance-is-a-requirement-not-a-luxury">Alternative approaches are gaining traction</a> alongside watermarking. Post-hoc detection tools analyze statistical patterns in content, though accuracy varies. The consensus view favors layered defense: watermarking combined with metadata standards, <a href="https://www.techtarget.com/searchSecurity/feature/Types-of-deepfake-detection-technology-and-how-they-work">detection tools</a> and organizational protocols.</p> <p>IT leaders should consider the following steps to benefit from AI watermarking's potential:</p> <ol class="default-list"> <ol class="default-list"> <ul class="default-list"> <li>Audit all AI systems and outputs, classifying them by regulatory risk category.</li> <li><a target="_blank" href="https://spec.c2pa.org/specifications/specifications/2.3/specs/C2PA_Specification.html" rel="noopener">Adopt</a> C2PA Content Credentials for published media.</li> <li>Establish internal policies requiring disclosure of AI-generated content.</li> <li>Join industry standards bodies to influence evolving requirements.</li> </ul> </ol> </ol> <p>With more regulations on the horizon that could include watermarking requirements, time is of the essence for businesses to be ready to comply.</p> <p>"Businesses that move now gain learning, operational readiness and credibility," Renaud said. "In practice, that's often more valuable than waiting for a technically perfect system that may never fully arrive."</p> <p><i>Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.</i></p> </section> AI watermarking is being used to identify deepfakes, enhance transparency and build trust in AI-generated content, but reliability issues and false positives present challenges. https://cdn.ttgtmedia.com/rms/onlineimages/ai_g1183318665.jpg https://www.techtarget.com/searchenterpriseai/feature/How-watermarking-AI-content-benefits-businesses Thu, 05 Feb 2026 23:30:00 GMT <p>The evolution of AI has enabled the development of sophisticated large language models (LLMs) capable of ingesting, processing and delivering an enormous amount of detailed information to human users. AI tools, such as <a href="https://www.techtarget.com/whatis/definition/ChatGPT">ChatGPT</a> and other generative AI systems, are fundamentally changing the way people work, study and search for information.</p> <p>But, as with people, finding the most meaningful answer from AI involves asking the right questions. AI is neither psychic nor telepathic. Although the technology can glean context with ever-increasing effectiveness, it can't intuit, which means it doesn't know what a user wants until it's explicitly stated. In addition, AI can't provide specific details until the user provides precise parameters for their question. Users must coax or prompt AI systems to deliver a desired output by adding specific, actionable details to their question.</p> <p>As AI continues to evolve and demonstrate value in human endeavors, there's a need for professionals who understand how to prompt, posing questions or actions to the AI in the most efficient and effective ways possible. <a href="https://www.techtarget.com/searchenterpriseai/definition/prompt-engineering">Prompt engineering</a> is becoming a viable computer science role and career path.</p> <section class="section main-article-chapter" data-menu-title="What is prompt engineering?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What is prompt engineering?</h2> <p>Prompting is a familiar concept. It's done every time users query an AI-driven search engine like Google or Bing. In the broadest sense, prompt engineering is the art of asking the best question to elicit the most meaningful response from an AI system. This involves strong language skills, using nouns, verbs, relevant examples and other vocabulary arts to query the AI.</p> <p>A <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI">generative AI</a> user performs some of the tasks of a prompt engineer when they ask AI questions. An engineer, on the other hand, delves into the nuances of language input and observes how the AI output responds. This lets the prompt engineer refine the <a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM">LLM's</a> development and find AI limitations, errors and defects that AI developers can address. As part of training an AI system, prompt engineers can also help it understand how to interpret and deal with various prompts. The role of prompt engineer is a mix of programming, instructing and teaching.</p> <p>To understand the importance of prompts, consider the example of a user querying AI about a simple real-world topic, such as awards in the entertainment industry. A user asks the following question:</p> <figure class="main-article-image half-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/prompt-h.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/prompt-h_half_column_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/prompt-h_half_column_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/prompt-h.png 1280w" alt="Comic illustrating how prompt engineering works" height="257" width="279"> <figcaption> <i class="icon pictures" data-icon="z"></i>Prompt engineering often requires specific and clear requests to receive the desired output. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <p>"Who won the movie award?"</p> <p>This is an ineffective prompt because it's far too broad. The AI might respond by returning every winner of every movie-related award for every year where data is available. It would result in an unmanageable list that a user would need to parse manually.</p> <p>However, the user could <a href="https://www.techtarget.com/searchenterpriseai/opinion/The-role-of-precision-in-crafting-generative-AI-prompts">ask a more specific question</a>, such as the following:</p> <p>"What movie won the Academy Award for Best Picture in 2025?"</p> <p>The AI with adequate data and training could effectively respond with a specific answer. In this case, the movie <i>Anora</i> won the Academy Award for Best Picture in 2025.</p> <p>The prompt engineer must ensure the AI's answers to questions are correct. If the results are absent, incomplete, unpredictable or unintended, the engineer can train the AI to provide the correct answers -- or report issues to the development team for remediation.</p> <p>For the Academy Award example, a prompt engineer might test the AI by asking the following question:</p> <p>"What movie won the Academy Award for Best Picture in 1917?"</p> <p>Since the first Academy Award ceremony was held in 1929, the prompt engineer should pay particular attention to how the AI responds and what explanations it provides. This is because there is no data to answer the query, as the Academy Awards didn't exist in 1917. Any other response would be erroneous and require corrective training from the prompt engineer.</p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/Bq-ncjOGeVU?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="Prompt engineering techniques"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Prompt engineering techniques</h2> <p>Prompt engineers use <a href="https://www.techtarget.com/searchenterpriseai/tip/Prompt-engineering-tips-and-best-practices">numerous techniques to craft the inputs that guide AI systems</a> toward relevant, accurate and detailed responses. Successful engineers understand and use a range of techniques to build prompts for diverse situations and platforms. The following are some of these techniques:</p> <ul class="default-list"> <li><b>Direct prompting.</b> A prompt with precise and detailed directions. This approach avoids ambiguity through action verbs, and defines the length, format and tone of the output.</li> <li><b>Persona prompting.</b> Also known as role playing, persona prompting instructs the AI to take a defined role, such as an experienced sales professional. This can cause the AI to improve its contextual understanding and generate specialized output.</li> <li><b>Few-shot or zero-shot prompting.</b> Examples, or shots, help an AI recognize patterns and deliver specific styles and formats in its output. However, examples can be difficult or even impossible to provide, forcing the prompt engineer to construct prompts using few-shot or no-shot prompting. If no examples are provided (zero-shot), the AI only uses its trained information.</li> <li><b>Chain-of-thought prompting. </b>The path from an AI prompt to AI output can be opaque and difficult to follow. <a href="https://www.techtarget.com/searchenterpriseai/definition/chain-of-thought-prompting">CoT prompting</a> asks the AI to approach complex tasks or problems as a series of logical steps. This helps the engineer understand how the AI arrived at its output, provides transparency and enables better corrective actions as needed.</li> <li><b>Decomposition prompting.</b> Also called <a href="https://www.techtarget.com/searchenterpriseai/definition/prompt-chaining">prompt chaining</a>, decomposition is the reverse of CoT prompting. Here, the prompt engineer breaks the problem into a series of smaller problems using several prompts where the output of one prompt delivers the input to the next.</li> <li><b>Contextual prompting.</b> These prompts include important background information, relevant data and meaningful scenario descriptions. <a href="https://www.techtarget.com/searchenterpriseai/feature/Context-engineering-takes-prompting-to-a-higher-business-level">Context engineering is a level of prompt engineering</a> that helps the AI to produce more refined or tailored responses.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Prompt engineering tools and platforms"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Prompt engineering tools and platforms</h2> <p>Prompt engineers don't simply use the AI interface to pose their queries. They also rely on an <a href="https://www.techtarget.com/searchenterpriseai/feature/Compare-prompt-engineering-tools">assortment of tools and platforms to facilitate prompt creation</a>, testing, optimization, version control, cost, performance monitoring and automation. Job seekers can improve their opportunities by demonstrating familiarity with numerous tool types, such as the following:</p> <ul class="default-list"> <li><b>Prompt prototyping.</b> Prompt engineers use prototyping tools for rapid prompt creation, testing and iteration. These are especially useful when interactive or complex workflows are involved. The goal is to visualize features, test <a href="https://www.techtarget.com/searchcio/definition/UX-user-experience">user experience</a>, test hypotheses and create functional examples that users can share with project team members and other stakeholders. Examples of prototyping tools include Anthropic's Claude Console, Microsoft's Azure OpenAI Studio, Google's Vertex AI and OpenAI's Playground.</li> <li><b>Prompt management.</b> Prompts can be straightforward, but prompt engineers focus on building complex and detailed prompt structures. These can involve numerous prompts chained together. Prompt engineering treats prompts like code, and prompt management tools can save and <a href="https://www.techtarget.com/searchsoftwarequality/feature/Words-to-go-Version-control-process">apply version controls</a> to document each prompt. Examples of prompt management tools include LangChain's LangSmith, PromptLayer and PromptPanda.</li> <li><b>LLM frameworks.</b> Prompt engineers work extensively with LLMs, which must understand, process and respond to prompts. <a href="https://www.techtarget.com/searchsoftwarequality/tip/Understanding-the-fundamentals-of-LLM-observability">LLM frameworks</a> help orchestrate prompt creation, manage data flow, validate responses and enable AI agents to handle complex multistep reasoning. Examples of LLMs include LangChain, LlamaIndex and Microsoft's Semantic Kernel.</li> <li><b>Prompt testing.</b> When engineers treat prompts like a segment of software code, comprehensive testing and evaluation are required. Prompt testing and evaluation tools can test, benchmark and offer improvements to LLM applications. This lets prompt engineers gauge prompt quality, compare model versions and performance, and ensure that AI systems meet established operational criteria. Examples of testing and evaluation tools include LangSmith Evaluation, OpenAI Evals, PromptimizeAI and PromptLayer.</li> <li><b>Prompt optimization.</b> Engineers can often refine AI prompts for better results or performance. Optimization and automation tools refine prompts for faster, more accurate and less expensive results. These tools often use internal AI to refine and systematically find the most effective instructions. Examples of prompt optimization tools include AutoPrompt, OpenAI's Prompt Optimizer, PromptLayer and PromptPerfect.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="What does a prompt engineer do?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What does a prompt engineer do?</h2> <p>From an employment perspective, prompt engineering is an abstract form of <a href="https://www.techtarget.com/searchapparchitecture/definition/user-interface-UI">UI</a> engineering. For example, a traditional UI engineer is responsible for ensuring that the UI is intuitively designed, easy to navigate and provides clear responses and behaviors for users. Generative AI poses different UI challenges because users simply ask the AI for what they want. The UI is the prompt mechanism itself for LLMs like GPT. The prompt engineer must understand how the AI works, recognize how the AI will respond given specific prompts, ensure that the AI delivers meaningful output or responses for any input prompt and recommend corrective action when it doesn't.</p> <p>Today's prompt engineer serves a cross-disciplinary role with the following three components:</p> <h3>Develop, test and refine AI prompts</h3> <p>The core role of any prompt engineer is to work with AI platforms to develop new prompts. Engineers test AI behaviors and the outputs resulting from prompts. They improve prompts and implement AI guardrails to maintain safe, ethical and predictable AI output when given new or unexpected prompts. Troubleshooting is also a core responsibility. A prompt engineer must spot AI limitations and errors, and develop strategies to remediate them.</p> <p>For example, a prompt engineer working with a <a href="https://www.techtarget.com/healthtechanalytics/feature/Top-12-ways-artificial-intelligence-will-impact-healthcare">healthcare AI platform</a> might develop prompts to inquire about patient diagnoses and test how the AI responds when input, such as patient names or medical terminology, is misspelled. Then, they make recommendations or changes to refine acceptable prompts or AI responses. Prompt engineers are usually involved in ongoing AI training and refinement.</p> <h3>Collaborate across-disciplines</h3> <p>A prompt engineer is typically part of the development team, often serving in a consulting and quality-control role. The engineer works with product developers to design and code the AI platform. They can also be part of the data team, which establishes the data set and <a href="https://www.techtarget.com/searchenterpriseai/tip/Explore-the-role-of-training-data-in-AI-and-machine-learning">trains the AI platform</a>, or the business team, which is comprised of project stakeholders.</p> <p>A prompt engineer is usually tasked with aligning prompts to company goals and user needs. For example, at a healthcare provider, a prompt engineer might work on how an AI platform uses medical terms, and how it presents patient data and diagnoses. Prompt engineers also collaborate with project or business teams to optimize prompts for performance and cost, such as minimizing compute time and latency.</p> <h3>Analyze and report</h3> <p>Finally, a prompt engineer must understand analytics. They need to monitor and correlate inputs and outputs and establish meaningful metrics to measure the AI platform's behavior and performance. These analytics are useful to AI developers, <a href="https://www.techtarget.com/searchenterpriseai/tip/11-data-science-skills-for-machine-learning-and-AI">data scientists</a> and the business team.</p> <p>Garbage in, garbage out, or <a href="https://www.techtarget.com/searchsoftwarequality/definition/garbage-in-garbage-out">GIGO</a>, is one of the oldest axioms of computer science, but with AI, it's more relevant than ever. For example, a prompt engineer might analyze AI responses to specific prompt sets and alert the data science team to signs of <a href="https://www.techtarget.com/searchenterpriseai/definition/machine-learning-bias-algorithm-bias-or-AI-bias">data bias</a> that could require more training or a review of data content. Similarly, AI prompt responses that indicate data gaps or cause unpredictable results might signal the need for a data review, algorithm refinement or model training.</p> <div class="extra-info"> <div class="extra-info-inner"> <h3 class="splash-heading">Prompt engineer salaries</h3> <p>How much can a prompt engineer make? Estimates vary dramatically, but salary reports range anywhere from about $100,000 at the entry level to $200,000 for senior staff to more than $400,000 per year for top earners in certain industries.</p> <p>Based on Glassdoor 2026 U.S. salary <a target="_blank" href="https://www.glassdoor.com/Salaries/prompt-engineer-salary-SRCH_KO0,15.htm" rel="noopener">data</a>, the average annual salary for a prompt engineer was about $127,000, depending on experience and industry. This might seem like a lot for relatively straightforward work, but it's important to look deeper.</p> <p>Prompt engineering is a growing IT role, and job complexity and responsibilities vary by company and AI platform. The highest-paying roles require deep knowledge of AI and extensive programming skills to construct complex prompts that involve thousands of precisely chosen words. That's extensive expertise that few people possess.</p> </div> </div> </section> <section class="section main-article-chapter" data-menu-title="5 core skills needed to become a prompt engineer"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5 core skills needed to become a prompt engineer</h2> <p>The skill set needed to qualify for a prompt engineering job isn't long, but it can be deceptively broad. This is typical for emerging roles that are still being defined by a rapidly developing industry. Prompt engineering roles generally ask for the following five skills:</p> <ol type="1" start="1" class="default-list"> <li><b>Strong verbal and written communication skills.</b> Unlike many IT roles, prompt engineers must communicate with AI systems through words and phrases. Detailed prompts can be quite complex and involve hundreds, even thousands, of carefully chosen words. In addition, the cross-disciplinary nature of prompt engineering makes communication and collaboration important.</li> <li><b>Programming proficiency.</b> Although prompt engineering isn't exactly programming, it's common for prompt engineers to have some involvement in coding -- whether they participate in developing the AI platform itself or use the programming skills to automate testing and other functions. This often requires several years of experience with languages, such as Python or its peers. It also helps to have a strong knowledge of APIs, operating systems and <a href="https://www.techtarget.com/searchwindowsserver/definition/command-line-interface-CLI">CLIs</a>. Precise requirements will depend on the company and the AI platform.</li> <li><b>Prior prompt experience.</b> Given that prompt engineering is a relatively new role, it's difficult to pinpoint a minimum level of prior experience, since the traditional three-to-five-year experience benchmark is hardly applicable. Still, most employers will look for prompt engineers with demonstrated experience in building and testing AI prompts, along with experience working with major models, <a href="https://www.techtarget.com/searchenterpriseai/feature/ChatGPT-vs-GPT-How-are-they-different">such as GPT, and platforms, such as ChatGPT</a>.</li> <li><b>AI technology knowledge.</b> Prompt engineers rely on language skills, but they still require a comprehensive understanding of <a href="https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP">natural language processing</a>, LLMs, machine learning and AI-generated content development. This is important if the prompt engineer has coding or other AI platform development responsibilities.</li> <li><b>Data analysis experience.</b> Prompt engineers must understand the data provided to an AI platform, the data used in prompts and the data the AI generates in response. This requires strong knowledge of data analytics techniques and tools. An employer might look for several years of experience analyzing structured and unstructured data sources. This knowledge is essential when looking for data bias and other data issues and ensures the quality of the AI output.</li> </ol> <p>In addition to these five core skills, prompt engineers must demonstrate mastery of <a href="https://www.techtarget.com/searchcio/feature/People-skills-todays-CIOs-and-IT-leaders-need">soft skills</a>, such as problem-solving and analysis, along with the ability to collaborate effectively with cross-functional teams.</p> </section> <section class="section main-article-chapter" data-menu-title="Prompt engineering courses, certifications and career opportunities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Prompt engineering courses, certifications and career opportunities</h2> <p>Today, there aren't any college-level degrees in prompt engineering. Most prompt engineering candidates start with a bachelor's or master's degree in computer science.</p> <p>Degrees in AI, engineering, data science and even linguistics can also launch a prompt engineer's career.</p> <p>Beyond that broad foundation, prompt engineers rely on a mix of practical experience along with online courses and certifications. Certifications can include Blockchain Council's Certified Prompt Engineer; universities such as MIT, Purdue University and the University of Michigan offer related certifications in AI and ML. Online education platforms, such as Coursera, edX, NetCom Learning, Refonte Learning and Udemy, also provide practical skills in prompt engineering.</p> <p>Prompt engineering roles have their own unique requirements. Understand the education and experience required for a specific job opportunity, and ensure that formal education, continuing coursework and practical experience meet those requirements.</p> <p><i>Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.</i></p> </section> With the rise of generative AI, prompt engineering has emerged as a new profession. Desired skills include refining prompts, analyzing AI output and ensuring alignment with business goals. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a238006601.jpg https://www.techtarget.com/searchenterpriseai/feature/5-skills-needed-to-become-a-prompt-engineer Thu, 05 Feb 2026 19:00:00 GMT <p>Contact center software has existed since the dawn of digital contact centers decades ago. But, in recent years, the contact center software industry has changed significantly.</p> <p>New technologies, such as generative AI, have spawned powerful and innovative contact center features. Hyperscalers, too, like Microsoft and Amazon, have entered the space, hoping to use their command of adjacent markets to claim a slice of the contact center software ecosystem.</p> <p>All these developments prompt a re-evaluation of <a href="https://www.techtarget.com/searchcustomerexperience/feature/The-ultimate-guide-to-contact-center-modernization">modern contact center platform options</a>. Below, we identify the leading contact center platforms and summarize their key features and drawbacks so businesses can make informed decisions when evaluating these products.</p> <p>In developing this list, we examined research and independent user reviews from leading analyst firms and buyer intelligence platforms. Based on this analysis, we created an unranked list of the top 19 contact center platforms. The list is in alphabetical order.</p> <p>The software providers range from new players to more established vendors. While they all deliver <a href="https://www.techtarget.com/searchcustomerexperience/How-to-choose-a-contact-center-software-system">core contact center software capabilities</a>, they vary in areas like major features, pricing, AI capabilities, scalability and integrations.</p> <section class="section main-article-chapter" data-menu-title="1. 8x8"> <h2 class="section-title"><i class="icon" data-icon="1"></i>1. 8x8</h2> <p>Founded in 1987, 8x8 has built up its contact center platform over many years, largely through acquisitions. What began as a basic voice calling tool has evolved into a full-fledged platform for multi-channel customer interaction.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Analytics.</b> Detailed analytics and reporting provide real-time feedback on customer interactions.</li> <li><b>Intelligent call routing.</b> Interactive voice response and customized call routing help to personalize the <a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-experience-CX">customer experience</a> (CX).</li> <li><b>Extensive CRM integration.</b> Integrations with popular CRM platforms make it easy to use CRM data during customer interactions.</li> </ul> <h3>Scalability</h3> <p>8x8's cloud-based hosting model allows the platform's software to scale easily. Flexible licensing also helps enable scalability from a purchasing standpoint.</p> <h3>Integrations</h3> <p>8x8 integrates by default with major CRM and communications platforms like Salesforce, HubSpot and Microsoft Teams. An API enables custom integrations.</p> <h3>Pricing</h3> <p>Pricing varies widely depending on feature selection, and 8x8 offers custom quotes rather than publishing pricing details publicly. As a baseline, however, pricing generally starts around $20 per user per month, although it can extend above $100 per user per month for feature-rich plans.</p> <p>8x8 is most notable for its affordable pricing for basic plans and easy integration with external platforms.</p> </section> <section class="section main-article-chapter" data-menu-title="2. Amazon Connect"> <h2 class="section-title"><i class="icon" data-icon="1"></i>2. Amazon Connect</h2> <p>Introduced in 2017, Amazon Connect offers a centralized hub from which <a href="https://www.techtarget.com/searchcustomerexperience/answer/5-ways-to-improve-call-center-agent-performance?Offer=ab_MeteredFormCopyEoc_var3">contact center agents</a> can engage with customers across multiple channels, including voice, chat and messaging. It also integrates with other Amazon products and services. In 2023, Amazon Connect incorporated several AI-based capabilities, such as support for creating virtual assistants.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Centralized interface.</b> Contact center agents can handle interactions via voice, chat, email and text through a centralized channel.</li> <li><b>No-code flow builder.</b> To configure workflows for different types of interactions or customer needs, businesses can use a visual workflow builder.</li> <li><b>AI-driven automation.</b> Partly via integrations with other Amazon services -- such as Lex, which powers AI chatbots -- Amazon Connect enables the automation of some interactions using AI. For example, users can use Amazon Q in Connect to deploy GenAI chatbots. AI features can also automatically route requests to agents.</li> </ul> <h3>Scalability</h3> <p>As a platform hosted across multiple regions in the AWS cloud, Connect is a highly scalable and <a href="https://www.techtarget.com/searchcloudcomputing/tip/Compare-high-availability-vs-fault-tolerance-in-AWS?Offer=ab_MeteredFormCopyEoc_var3">fault-tolerant service</a>. It can support a virtually unlimited volume of agents or interactions.</p> <h3>Integrations</h3> <p>Connect integrates most closely with other services within the Amazon cloud. However, it supports limited integrations with external platforms, such as Salesforce and Zendesk, which businesses can use to look up or import data during customer interactions.</p> <h3>Pricing</h3> <p>Connect pricing is based mostly on volume usage. It starts at around $0.018 per minute for voice calls and $0.004 per chat message. Additional fees apply for using optional features, like Amazon Q.</p> <p>Amazon Connect is most notable for hyperscale-level scalability and availability, as well as tight integration with other Amazon services.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/ai_sharpens_contact_center_features_and_actions-f.png 1280w" alt="Integrating AI in contact center software" height="355" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>AI and generative AI integration is remaking contact center software. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="3. Avaya"> <h2 class="section-title"><i class="icon" data-icon="1"></i>3. Avaya</h2> <p>Traditionally, Avaya focused its contact center software on <a href="https://www.techtarget.com/searchcustomerexperience/tip/On-premises-vs-cloud-contact-center-Whats-the-difference">on-premises hosting models</a>. However, it has expanded into cloud-based options that support public and private cloud deployments. Avaya provides all the core capabilities that businesses expect from a modern contact center platform as well as certain innovative features like AI-based virtual assistants.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Flexible deployment options.</b> Avaya offers on-premises and cloud-based contact center products. The on-prem offering may be an advantage for organizations that, due to <a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-compliance-checklist-for-hybrid-workforces">compliance or privacy concerns</a>, can't or don't want to store contact center data on third-party infrastructure.</li> <li><b>Process optimization.</b> Native features assist with the optimization of tasks such as scheduling and agent training.</li> <li><b>Real-time reporting.</b> Continuous analytics further assist with the identification of opportunities to optimize.</li> </ul> <h3>Scalability</h3> <p>While the scalability of Avaya's on-premises offering is limited by the scope of the host infrastructure, its cloud-based platform can scale virtually without limit.</p> <h3>Integrations</h3> <p>Avaya integrates with popular CRM platforms like Salesforce, ServiceNow and Microsoft Dynamics 365. Custom integrations are available through an API.</p> <h3>Pricing</h3> <p>The cost of Avaya starts at $20 per user per month for the Core plan. The highest-cost plan is priced at $35 per user per month. These prices reflect a 20% discount for a yearly contractual commitment.</p> <p>Avaya is most notable for its on-premises deployment option and competitive pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="4. Cisco Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>4. Cisco Contact Center</h2> <p>Although Cisco is best known for its networking and communications tools, it has also invested significantly in the contact center space. Its Contact Center product employs Webex, a meeting and collaboration application, as the foundation for omnichannel customer interactions.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Security.</b> Cisco Contact Center goes <a href="https://www.techtarget.com/searchcustomerexperience/tip/Call-center-security-best-practices-to-protect-customer-data">above and beyond in the security realm</a>, offering advanced capabilities like endpoint hardening and data masking.</li> <li><b>Enterprise scalability.</b> While the product can work for small businesses, it's designed especially for large-scale, enterprise-grade communications.</li> <li><b>Customer sentiment analysis.</b> The platform uses AI to assess customer reactions to interactions.</li> </ul> <h3>Scalability</h3> <p>Cisco Contact Center scales especially well for large enterprises.</p> <h3>Integrations</h3> <p>Cisco Contact Center integrates tightly with other Cisco tools, particularly the Webex and Jabber communication apps. In fact, to some extent, the contact center service depends on these integrations with other Cisco tools. Integrations are also available for major CRM and IT ticketing platforms.</p> <h3>Pricing</h3> <p>Cisco doesn't publish pricing details for its contact center service, and costs vary depending on features and usage. As a rough baseline, expect to pay anywhere in the range of $30 to $200 per user per month.</p> <p>Cisco Contact Center is most notable for its security features and enterprise-grade scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="5. CloudTalk"> <h2 class="section-title"><i class="icon" data-icon="1"></i>5. CloudTalk</h2> <p>CloudTalk is most notable for its heavy focus on <a target="_blank" href="https://www.cloudtalk.io/blog/call-center-analytics-guide/" rel="noopener">automation and analytics features</a> designed to streamline contact center performance and increase operations efficiency. It also offers innovative AI-powered features, such as topic extraction, which automatically monitors conversational topics.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Agent collaboration.</b> In addition to supporting multi-channel customer engagement, CloudTalk offers native features for agent collaboration, like internal call conferencing and shared workspaces.</li> <li><b>Advanced analytics.</b> CloudTalk offers particularly detailed reporting on engagement metrics and agent performance.</li> <li><b>Extensive integrations.</b> The platform provides a broad range of integrations that include major CRM platforms and communication and automation tools like Slack and Zapier.</li> </ul> <h3>Scalability</h3> <p>As a cloud-based offering, CloudTalk works well at virtually any scale. Flexible pricing terms also enable easy scalability.</p> <h3>Integrations</h3> <p>As noted above, CloudTalk integrates out-of-the-box with a particularly wide range of external platforms. It also provides an API for custom integrations.</p> <h3>Pricing</h3> <p>CloudTalk pricing starts around $25 per user per month. The most feature-rich plan costs about $50 per user per month.</p> <p>CloudTalk is notable for its advanced analytics and broad integrations.</p> </section> <section class="section main-article-chapter" data-menu-title="6. Content Guru"> <h2 class="section-title"><i class="icon" data-icon="1"></i>6. Content Guru</h2> <p>Launched in 2005, Content Guru offers a contact center and <a href="https://www.techtarget.com/searchcustomerexperience/definition/customer-engagement">customer engagement</a> service tailored for verticals that require high availability and security, like government and finance. Although the service can be and is used by all types of businesses.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-powered automation.</b> Content Guru makes extensive use of AI to automate tasks like call routing. It also supports AI-powered virtual agents.</li> <li><b>Workforce management.</b> Native capabilities assist with scheduling contact center agents and managing workflows.</li> <li><b>Video call support.</b> Supports customer engagement via video as well as more conventional channels, such as voice and text.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment enables easy scalability up and down.</p> <h3>Integrations</h3> <p>Content Guru integrates with major CRM platforms out-of-the-box, and an API is available for developing custom integrations.</p> <h3>Pricing</h3> <p>Content Guru pricing varies based on total agent count, type and feature availability. It starts at $22 per digital-only agent per month. Voice agents cost at least $70 per month.</p> <p>Content Guru is most notable for AI-powered automation and workflow optimization capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="7. Dialpad"> <h2 class="section-title"><i class="icon" data-icon="1"></i>7. Dialpad</h2> <p>Dialpad initially focused on providing internal communications software for businesses and added contact center software capabilities in 2018. Dialpad is most notable for its extensive investment in AI-based capabilities, such as AI-driven voice analysis and call summaries, as well as AI-powered <a href="https://www.techtarget.com/searchcustomerexperience/definition/virtual-agent">virtual agents</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI capabilities.</b> Dialpad makes especially extensive use of AI to provide capabilities like real-time transcription and sentiment analysis.</li> <li><b>Collaboration.</b> Built-in chat, file sharing and other collaboration tools help agents communicate.</li> <li><b>Broad integrations.</b> Dialpad integrates with external productivity and collaboration platforms like Google Workspace and Microsoft Teams in addition to CRM tools.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment and multiple pricing plans make Dialpad easy to scale for businesses of virtually all sizes.</p> <h3>Integrations</h3> <p>As mentioned, Dialpad is notable for integrating with popular CRM platforms, like Salesforce and Zendesk, and productivity and collaboration suites, like Google Workspace and Microsoft Teams. Customers can also build custom workflows.</p> <h3>Pricing</h3> <p>Dialpad pricing starts at $15 per user per month for the Standard plan. The Pro plan is $25 per user per month. An Enterprise plan is also available.</p> <p>Dialpad is most notable for advanced AI features, extensive integrations and competitive entry-level pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="8. Five9"> <h2 class="section-title"><i class="icon" data-icon="1"></i>8. Five9</h2> <p>Five9 provides a fully cloud-based call and contact center platform. It also places special emphasis on transparency and security for businesses concerned with <a href="https://www.techtarget.com/searchcustomerexperience/answer/How-do-companies-protect-customer-data">protecting sensitive customer data</a> or meeting strict compliance mandates related to customer calls.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Dynamic routing.</b> Five9 offers a particularly powerful routing tool that can route calls based on a variety of factors, such as priority level, agent expertise and geographical location.</li> <li><b>Workforce management.</b> Built-in capabilities, including forecasting and automated scheduling, assist with agent workforce management.</li> <li><b>AI capabilities.</b> Five9 includes advanced AI features such as speech recognition and predictive dialing.</li> </ul> <h3>Scalability</h3> <p>Five9 is highly scalable because of its cloud-based deployment model and its flexible pricing terms and plans, which cater to a wide range of business sizes.</p> <h3>Integrations</h3> <p>Five9 integrates with CRM platforms as well as popular IT management suites, like ServiceNow.</p> <h3>Pricing</h3> <p>Five9 doesn't publish full pricing details of all its plans, but its most basic plan starts at $119 per user per month. Its Core plan, which has more features, is $159 per user per month.</p> <p>Five9 is most notable for especially efficient and flexible call routing capabilities and advanced AI features.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/basic_contact_center_business_goals-f.png 1280w" alt="Business goals for contact center software" height="260" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Today's contact center software must satisfy several business goals. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="9. Genesys"> <h2 class="section-title"><i class="icon" data-icon="1"></i>9. Genesys</h2> <p>Founded in 1990, Genesys has spent decades building a feature-rich contact center and customer engagement platform. The company caters especially to medium-size and large businesses.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>On-premises option.</b> An on-premises deployment option is available, as well as a <a href="https://www.techtarget.com/searchcloudcomputing/tip/Evaluate-on-premises-vs-cloud-computing-pros-and-cons">cloud-based offering</a>.</li> <li><b>Virtual agents.</b> AI capabilities include virtual agents.</li> <li><b>Collaboration.</b> Internal screen sharing and conferencing capabilities help agents collaborate.</li> </ul> <h3>Scalability</h3> <p>Genesys can operate on any scale, but it focuses especially on deployments for midsize and enterprise customers.</p> <h3>Integrations</h3> <p>Out-of-the-box integrations focus mostly on CRM platforms. An API is available for custom integrations.</p> <h3>Pricing</h3> <p>Costs start at $75 per user per month and range up to $240 per user per month.</p> <p>Genesys is most notable for its on-premises deployment option and extensive collaboration capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="10. Google Cloud Contact Center as a Service"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10. Google Cloud Contact Center as a Service</h2> <p>Google Cloud Contact Center as a Service (CCaaS) -- also referred to as Google's Contact Center AI Platform (CCAI Platform) -- is among the newer cloud-based contact center products and is focused on AI capabilities such as virtual agents. Behind the scenes, however, Google's contact center offering is powered largely by UJET, an independent contact center platform known for its analytics features and <a href="https://www.techtarget.com/searchcustomerexperience/tip/Contact-center-back-end-integrations-drive-revenue-growth">integration with CRM systems</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI capabilities.</b> Advanced AI capabilities include chatbots and virtual agents.</li> <li><b>Speech recognition.</b> AI also enables real-time speech transcription and sentiment analysis.</li> <li><b>Google Cloud integrations.</b> Google's contact center integrates tightly with other Google Cloud services.</li> </ul> <h3>Scalability</h3> <p>Although designed especially for large enterprise customers, Google Cloud's CCaaS can also support smaller teams.</p> <h3>Integrations</h3> <p>The contact center integrates most seamlessly with other Google Cloud products and services, as well as popular CRMs like Salesforce. An API is available for developing custom integrations.</p> <h3>Pricing</h3> <p>Pricing is mostly a pay-as-you-go model and starts at around $0.06 per chat session and $0.05 per voice minute. Some capabilities cost extra, like Conversational Insights, which provides engagement analytics.</p> <p>The CCAI Platform is most notable for its close integration with Google Cloud services and enterprise-grade scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="11. Microsoft Dynamics 365 Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>11. Microsoft Dynamics 365 Contact Center</h2> <p>Microsoft developed the Microsoft Dynamics contact center platform in-house and released it in July 2024. Microsoft emphasizes self-service on a customer-preferred channel as well as <a href="https://www.techtarget.com/searchcustomerexperience/tip/Best-practices-for-call-center-monitoring">monitoring and reporting features to improve operational efficiency</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Workforce management.</b> Built-in tools assist with agent scheduling and performance assessment.</li> <li><b>Microsoft integrations.</b> Dynamics 365 Contact Center connects to other Microsoft tools and platforms, like Teams, Outlook and Power BI.</li> <li><b>AI features.</b> Dynamics 365 Contact Center uses GenAI services hosted on the Microsoft Azure cloud to enable virtual agents and chatbots.</li> </ul> <h3>Scalability</h3> <p>As a hyperscale-based service, Dynamics 365 offers immense scalability from an infrastructure perspective. That said, its pricing models are flexible enough to accommodate the needs of smaller teams as well.</p> <h3>Integrations</h3> <p>The contact center service integrates most tightly with other Microsoft products, as well as popular CRM platforms. Custom integrations are possible through an API.</p> <h3>Pricing</h3> <p>Costs begin at $95 per user per month. A free trial is also available.</p> <p>Dynamics 365 Contact Center is most notable for integration with other Microsoft products, which facilitates integrating contact center capabilities into broader Microsoft software suites.</p> </section> <section class="section main-article-chapter" data-menu-title="12. Nextiva"> <h2 class="section-title"><i class="icon" data-icon="1"></i>12. Nextiva</h2> <p>Nextiva offers all the key features that businesses need to operate an effective contact center, such as<a href="https://www.techtarget.com/whatis/definition/skill-based-routing-SBR"> skills-based call routing</a> and advanced call management. Nextiva has invested in AI-based capabilities and places special emphasis on platform reliability and a fast response to service requests from its customers.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Intelligent call routing.</b> Nextiva provides highly flexible and efficient call routing capabilities based on criteria defined by users.</li> <li><b>AI capabilities.</b> The platform uses AI to generate call summaries. An AI answering feature is also available.</li> <li><b>High availability.</b> Nextiva's platform is cloud-based, and the company focuses on achieving particularly high availability through a multi-site hosting model.</li> </ul> <h3>Scalability</h3> <p>Multi-site hosting and flexible pricing plans enable a high degree of scalability.</p> <h3>Integrations</h3> <p>Nextiva connects to major CRM platforms. An API supports custom integrations.</p> <h3>Pricing</h3> <p>Costs start at $15 per user per month, and increase to $75 per user per month for more features geared toward small businesses. Larger enterprise plans are also available.</p> <p>Nextiva is most notable for reliability and affordable entry-level pricing.</p> </section> <section class="section main-article-chapter" data-menu-title="13. NiCE CXone Mpower"> <h2 class="section-title"><i class="icon" data-icon="1"></i>13. NiCE CXone Mpower</h2> <p>Launched in 2024, CXone Mpower from NiCE is one of the newest contact center platforms on our list. The company promotes CXone Mpower as a "CX-aware" service because it uses AI to inject <a target="_blank" href="https://www.linkedin.com/pulse/transform-customer-experiences-real-time-using-contextual-goyal-hlw8c/" rel="noopener">context into customer interactions</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI integrations.</b> The platform makes extensive use of AI to help optimize workflows and generate context for customer integrations.</li> <li><b>Chatbots and virtual agents.</b> AI also supports chatbots and virtual agents within CXone Mpower.</li> <li><b>Scalability.</b> The platform is particularly notable for its ability to cater to customers of all types and sizes, from small businesses to large enterprises.</li> </ul> <h3>Scalability</h3> <p>As noted above, CXone Mpower is an especially scalable service due to its cloud-based hosting model and the ease of accommodating increased customers or communication channels.</p> <h3>Integrations</h3> <p>Core integrations support major CRM platforms. Custom integrations are possible through an API.</p> <h3>Pricing</h3> <p>Costs range from $110 to $249 per user per month.</p> <p>NiCE CXone Mpower is most notable for AI-enhanced efficiency capabilities.</p> </section> <section class="section main-article-chapter" data-menu-title="14. RingCentral"> <h2 class="section-title"><i class="icon" data-icon="1"></i>14. RingCentral</h2> <p>Founded in 1999, RingCentral originally specialized in on-premises phone connectivity. Since then, it has expanded into a broad set of business communication and collaboration services, including a contact center platform.&nbsp;</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Mobile app for agents.</b> A mobile app allows agents to engage with customers from virtually any location.</li> <li><b>Collaboration.</b> Internal video calling, team messaging and file sharing help agents collaborate.</li> <li><b>Analytics.</b> RingCentral supports both real-time and historical reporting on agent performance and service levels.</li> </ul> <h3>Scalability</h3> <p>A cloud-based deployment model enables a high degree of scalability.</p> <h3>Integrations</h3> <p>RingCentral integrates with major CRM platforms as well as certain business productivity suites, such as Google Workspace.</p> <h3>Pricing</h3> <p>RingCentral's RingCX product features a Standard plan at $65 per user per month. The Professional plan is $95 per user per month, and the Elite plan is $145 per user per month. An enterprise package is also available.</p> <p>RingCentral is most notable for its agent <a href="https://www.techtarget.com/searchcustomerexperience/tip/7-reasons-why-businesses-need-mobile-apps">mobile app option</a>, collaboration features and scalability.</p> </section> <section class="section main-article-chapter" data-menu-title="15. Salesforce Service Cloud Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>15. Salesforce Service Cloud Contact Center</h2> <p>Although Salesforce is best known for CRM, its Service Cloud platform includes a contact center offering to pull customer data into contact center engagements and tightly integrate with the Salesforce product ecosystem.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI features.</b> Using Salesforce's Einstein AI tools, Service Cloud uses AI to automate tasks like routing.</li> <li><b>Custom chatbots.</b> Businesses can also use Einstein AI to configure custom AI chatbots to serve as virtual agents.</li> <li><b>Knowledge management.</b> Built-in knowledge management capabilities aim to accelerate the rate at which agents can solve customer requests.</li> </ul> <h3>Scalability</h3> <p>Service Cloud can support businesses of all sizes, but it's geared especially toward large, enterprise-scale customers.</p> <h3>Integrations</h3> <p>Salesforce contact center integrates most tightly with other Salesforce products but also provides core integration with certain third-party platforms, such as Zendesk and HubSpot.</p> <h3>Pricing</h3> <p>Salesforce offers one pricing plan, at $150 per user per month, for its contact center software.</p> <p>The Salesforce contact center is most notable for enterprise-grade scalability and extensive Salesforce integrations.</p> </section> <section class="section main-article-chapter" data-menu-title="16. Talkdesk"> <h2 class="section-title"><i class="icon" data-icon="1"></i>16. Talkdesk</h2> <p>Talkdesk promotes its CX automation via its AI multi-agent workflows and AI-first <a href="https://www.techtarget.com/searchcustomerexperience/tip/5-customer-journey-phases-for-businesses-to-understand">customer journey</a>. Talkdesk also emphasizes its capabilities across several vertical industries. The product -- dubbed Customer Experience Automation, or CXA -- is known for its ease of use, intuitive interface and call routing capabilities.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>Virtual agents.</b> Talkdesk offers GenAI-powered virtual agents to automate customer interactions.</li> <li><b>No-code workflow management.</b> A visual interface enables workflow configuration and modifications.</li> <li><b>Hybrid cloud deployment option.</b> While Talkdesk can't run fully on-premises, a hybrid deployment model is available that allows businesses to route communications through on-prem telephony infrastructure, which can be advantageous from a privacy and compliance standpoint.</li> </ul> <h3>Scalability</h3> <p>A flexible deployment architecture enables a high degree of scalability, making Talkdesk appropriate for small businesses and large enterprises.</p> <h3>Integrations</h3> <p>Integrations focus mostly on CRM platforms, but Google Workspace is also supported, and a custom integration API is available.</p> <h3>Pricing</h3> <p>Costs range from $85 to $225 per user per month.</p> <p>Talkdesk is most notable for its feature-rich virtual agents and hybrid deployment option.</p> </section> <section class="section main-article-chapter" data-menu-title="17. Vonage Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>17. Vonage Contact Center</h2> <p>Vonage Contact Center's natively built features, including AI-powered virtual assistants, rely on integrations with external platforms, particularly Salesforce, to power some of its capabilities and access customer data. Vonage also emphasizes <a target="_blank" href="https://www.vonage.com/resources/articles/video-contact-center/" rel="noopener">video-based customer engagement</a>.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-based sentiment analysis.</b> Vonage uses AI to evaluate customer interactions across multiple channels, including voice, text and social media.</li> <li><b>Virtual agents.</b> AI also powers virtual agents, which businesses can configure to perform a range of custom tasks.</li> <li><b>Business continuity.</b> Vonage offers business continuity and disaster recovery features, such as emergency call routing options.</li> </ul> <h3>Scalability</h3> <p>Cloud-based deployment provides a high degree of scalability.</p> <h3>Integrations</h3> <p>Integrations focus mostly on CRM platforms, with an API available for custom integrations.</p> <h3>Pricing</h3> <p>Vonage does not list pricing information on its website specifically for its contact center plans, which include a Priority plan, Premium plan and add-on options. It offers volume-based API pricing with rates at $0.00809 per SMS and $0.01446 per minute for voice calls. Additional capabilities, like anti-fraud features and customer identification, cost extra.</p> <p>Vonage is most notable for omnichannel sentiment analysis, affordable volume-based pricing and business continuity features.</p> </section> <section class="section main-article-chapter" data-menu-title="18. Zendesk Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>18. Zendesk Contact Center</h2> <p>Although primarily a CRM platform, Zendesk also provides a dedicated contact center offering. The company first entered the call center space in 2011, but it completed a major overhaul of its customer communications and engagement platform in 2025, which now features cutting-edge AI capabilities.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI-powered automation.</b> Zendesk contact center makes extensive use of AI to automate virtually all core tasks, from routing to agent response.</li> <li><b>Chatbots.</b> AI-powered chatbots can perform custom tasks.</li> <li><b>Knowledge management.</b> Native knowledge management tools assist agents in finding the information they need to address customer requests.</li> </ul> <h3>Scalability</h3> <p>Zendesk contact center can support businesses of all sizes, but it caters especially to midsize and enterprise organizations.</p> <h3>Integrations</h3> <p>Core integrations support other Zendesk products and other popular CRMs, including Salesforce and HubSpot, as well as communications platforms like Slack.</p> <h3>Pricing</h3> <p>Price plans start at $19 per user per month. The Suite Enterprise plan is $169 per user per month for enterprise-grade capabilities. Other plans are priced at $55 and $115 per user per month.</p> <p>Zendesk is most notable for its AI capabilities and a broad set of pricing options.</p> </section> <section class="section main-article-chapter" data-menu-title="19. Zoom Contact Center"> <h2 class="section-title"><i class="icon" data-icon="1"></i>19. Zoom Contact Center</h2> <p>Best known for its teleconferencing software, Zoom launched a contact center platform originally called Video Engagement Center and rebranded as Zoom Contact Center. The platform offers all core contact center software features with a focus on video-based customer meetings, while supporting other communications media over multiple channels.</p> <h3>Key features</h3> <ul type="disc" class="default-list"> <li><b>AI agent assist.</b> AI capabilities help guide human agents by suggesting actions and providing information during customer interactions.</li> <li><b>Virtual agents.</b> Fully independent, AI-powered agents are also available for engaging customers.</li> <li><b>Video support.</b> Zoom Contact Center supports customer engagement via video as well as more traditional channels.</li> </ul> <h3>Scalability</h3> <p>Zoom Contact Center provides a high degree of scalability due to its cloud-based deployment model, although its pricing plans are geared mainly toward midsize and larger organizations.</p> <h3>Integrations</h3> <p>Zoom Contact Center integrates with popular CRM platforms as well as other Zoom software.</p> <h3>Pricing</h3> <p>Pricing ranges from $69 to $149 per user per month.</p> <p>Zoom is most notable for its video calling support and AI capabilities that can assist human agents as well as power autonomous virtual agents.</p> <p>Clearly, the contact center market is crowded with many options for contact center buyers and C-suite decision-makers. Many of the platforms have similar and overlapping features, especially around AI capabilities, integrations with adjacent products and scalability performance. Contact center buyers need to evaluate these platforms carefully to find the right one for their organization.</p> <p><b>Editor's note:</b>&nbsp;<i>This article was updated to reflect recent developments in contact center platforms and the market in general.</i></p> <p><i>Chris Tozzi is an adjunct research adviser at IDC as well as an adviser for Fixate IO and a professor of IT and society at a polytechnic university in upstate New York.</i></p> </section> By now, many contact center software providers offer similar features. But large and small enterprises should consider some key differences among vendors. https://cdn.ttgtmedia.com/rms/onlineimages/chatbot_g1206801125.jpg https://www.techtarget.com/searchcustomerexperience/tip/Top-10-contact-center-platforms Thu, 05 Feb 2026 12:00:00 GMT <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{178}" paraid="485118795"><span xml:lang="EN-US" data-contrast="auto">Databricks Lakebase is now generally available, eight months after the PostgreSQL database purposed for AI development was first unveiled in public preview.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{184}" paraid="1686772772"><span xml:lang="EN-US" data-contrast="auto">Lakebase, which was launched on AWS on Feb. 3, is the result of Databricks' $1 billion </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366623864/Databricks-adds-Postgres-database-with-1B-Neon-acquisition"><span xml:lang="EN-US" data-contrast="none">acquisition of Neon</span></a><span xml:lang="EN-US" data-contrast="auto">, a cloud-based database vendor providing a platform built on the open-source PostgreSQL format, in May 2025. Databricks has since rebranded Neon's capabilities, and now Databricks has integrated them with </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366560094/Databricks-puts-AI-at-core-of-new-Data-Intelligence-Platform"><span xml:lang="EN-US" data-contrast="none">its Data Intelligence Platform</span></a><span xml:lang="EN-US" data-contrast="auto"> to provide customers with an operational database in conjunction with its data lakehouse.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{200}" paraid="560365120"><span xml:lang="EN-US" data-contrast="auto">Beyond integration with Databricks' broader platform, Lakebase fosters AI development by separating compute from storage, unlike many PostgreSQL databases that couple them together. By separating the processing power for queries from the power needed for storage, Lakebase eliminates competition between the two for memory resources and the resulting resource management tasks that can slow development initiatives.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{206}" paraid="1946560941"><span xml:lang="EN-US" data-contrast="auto">In addition, Lakebase features autoscaling to help users </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/tip/Implement-AI-driven-cloud-cost-optimization-to-reduce-waste"><span xml:lang="EN-US" data-contrast="none">control the cost</span></a><span xml:lang="EN-US" data-contrast="auto"> of building agents and other AI applications, and unified governance through Databricks' Unity Catalog, among other capabilities.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{225}" paraid="729396782"><span xml:lang="EN-US" data-contrast="auto">Given that Lakebase better integrates PostgreSQL workloads with the broader Databricks platform, it is a significant addition for the vendor's customers, according to Devin Pratt, an analyst at IDC.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{231}" paraid="2001649299"><span xml:lang="EN-US" data-contrast="auto">"The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{49891a88-3192-4adf-a73b-ce0322e46867}{237}" paraid="586289825"><span xml:lang="EN-US" data-contrast="auto">William McKnight, president of McKnight Consulting, similarly noted that Lakebase's value lies in its integration with other Databricks capabilities, reducing the need for </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/definition/data-egress"><span xml:lang="EN-US" data-contrast="none">data egress</span></a><span xml:lang="EN-US" data-contrast="auto"> pipelines between the database and other tools.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{1}" paraid="1002527863"><span xml:lang="EN-US" data-contrast="auto">"This architectural shift minimizes fragile pipelines by co-locating transactional workloads with heavy analytics under a single governance model," he said. "It effectively removes the 'architectural tax' that has historically separated live apps from data lakes."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Prowess of PostgreSQL</h2> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{13}" paraid="1406101758"><span xml:lang="EN-US" data-contrast="auto">Based in San Francisco and one of the pioneers of the data lakehouse architecture for storing data, Databricks, like many data management vendors, has added AI development capabilities over the past few years in response to </span><a rel="noreferrer noopener" target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai"><span xml:lang="EN-US" data-contrast="none">rising customer interest</span></a><span xml:lang="EN-US" data-contrast="auto"> in building AI tools that call on an enterprise's proprietary data to understand its unique operations.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The opportunity is to reduce friction between operational and analytical data so real-time applications and AI agents can work from governed data that stays current, with less ETL and duplication. </figure> <figcaption> <strong>Devin Pratt </strong>Analyst, IDC </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{24}" paraid="1459340638"><span xml:lang="EN-US" data-contrast="auto">Because PostgreSQL databases are more flexible than many other databases, PostgreSQL is now the most popular database format, according to the </span><a rel="noreferrer noopener" target="_blank" href="https://survey.stackoverflow.co/2024/technology"><span xml:lang="EN-US" data-contrast="none">2024 Developer Survey</span></a><span xml:lang="EN-US" data-contrast="auto"> by Stack Overflow.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{35}" paraid="332370681"><span xml:lang="EN-US" data-contrast="auto">Versatility -- handling geospatial, time series, JSON and vector database workloads -- and flexibility are two of the main reasons PostgreSQL databases are now more popular than fellow open-source MySQL databases and databases provided by vendors such as Microsoft, MongoDB and Redis.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{41}" paraid="491134092"><span xml:lang="EN-US" data-contrast="auto">With PostgreSQL so popular, and its adaptability enabling users to run workloads that aid AI development, hyperscale cloud vendors AWS, Google, IBM, Microsoft and Oracle all offer PostgreSQL databases that can be used with their AI development tools. Now, more specialized data management vendors are doing the same.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{49}" paraid="633463368"><span xml:lang="EN-US" data-contrast="auto">Three weeks after Databricks acquired Neon, rival </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database"><span xml:lang="EN-US" data-contrast="none">Snowflake purchased Crunchy Data</span></a><span xml:lang="EN-US" data-contrast="auto"> to add a PostgreSQL database. Then in October 2025, </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366633563/Streaming-vendor-Redpanda-buys-SQL-engine-unveils-AI-suite"><span xml:lang="EN-US" data-contrast="none">Redpanda acquired Oxla</span></a><span xml:lang="EN-US" data-contrast="auto"> to likewise add a PostgreSQL database.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{65}" paraid="437426485"><span xml:lang="EN-US" data-contrast="auto">"PostgreSQL has evolved into the great consolidator of the modern data stack by transforming from a traditional relational database into a unified, multi-model engine capable of powering the agentic AI era," McKnight said. "By natively integrating vector search with structured business data, it eliminates the need for fragmented point solutions, reducing development complexity."</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{71}" paraid="1452036302"><span xml:lang="EN-US" data-contrast="auto">In addition, pricing is a factor in PostgreSQL's growing popularity, McKnight continued, noting that PostgreSQL databases often cost less than databases from </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchcloudcomputing/definition/hyperscale-cloud"><span xml:lang="EN-US" data-contrast="none">hyperscale cloud</span></a><span xml:lang="EN-US" data-contrast="auto"> vendors.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{82}" paraid="1610933445"><span xml:lang="EN-US" data-contrast="auto">"As enterprises pivot toward Sovereign AI to maintain data gravity and avoid public cloud lock-in, PostgreSQL has become the strategic foundation for organizations that want a secure, high-performance platform to manage the transactions and vectors required for modern AI at scale," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{88}" paraid="919164422"><span xml:lang="EN-US" data-contrast="auto">Although PostgreSQL databases are gaining popularity as more enterprises invest in AI development, Databricks' Lakebase and </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership"><span xml:lang="EN-US" data-contrast="none">Snowflake Postgres</span></a><span xml:lang="EN-US" data-contrast="auto"> are differentiated from standalone PostgreSQL databases by their integration with broader data management and AI development platforms, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{99}" paraid="1693840052"><span xml:lang="EN-US" data-contrast="auto">Both reduce the need to move data between systems, which can increase development costs and potentially expose data to breaches, and both enable hybrid transactional and analytical workflows that are relevant for AI and real-time analytics workloads.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{109}" paraid="1248643844"><span xml:lang="EN-US" data-contrast="auto">But whether one proves more effective than the other remains to be seen.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{115}" paraid="1253390927"><span xml:lang="EN-US" data-contrast="auto">"Both are pushing PostgreSQL closer to analytics and AI, and the real differences will come down to platform integration and day-to-day operational experience," Pratt said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{121}" paraid="629852949"><span xml:lang="EN-US" data-contrast="auto">In addition to separation of compute and storage, key features of Lakebase include the following:</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="1" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{127}" paraid="1486727951"><span xml:lang="EN-US" data-contrast="auto">Serverless autoscaling that automatically adjusts compute resources to match workload demands, including shutting off when no workloads are running to </span><a rel="noreferrer noopener" target="_blank" href="https://www.computerweekly.com/news/366599472/How-to-stop-AI-costs-from-soaring"><span xml:lang="EN-US" data-contrast="none">eliminate wasted spending</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{138}" paraid="867718339"><span xml:lang="EN-US" data-contrast="auto">Unified governance through the Databricks Unity Catalog, enabling users to manage and secure data across their entire data estate.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="3" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{144}" paraid="1100517225"><span xml:lang="EN-US" data-contrast="auto">Instant database branching so users can quickly create isolated clones of production data to conduct risk-free testing and development work.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="4" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{150}" paraid="348295022"><span xml:lang="EN-US" data-contrast="auto">Point-in-time recovery, a feature that protects against accidental deletions or </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchsoftwarequality/definition/bug"><span xml:lang="EN-US" data-contrast="none">bugs</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <ul style="list-style-type: disc;" role="list" class="default-list"> <li role="listitem" data-aria-level="1" data-aria-posinset="5" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" data-listid="5" data-font="Symbol" data-leveltext="" aria-setsize="-1"> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{161}" paraid="117781018"><span xml:lang="EN-US" data-contrast="auto">Sync tables to automatically synchronize operational data and historical lakehouse context without having to build and manage complex pipelines.</span><span data-ccp-props="{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </li> </ul> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{167}" paraid="1834989908"><span xml:lang="EN-US" data-contrast="auto">Collectively, the features that comprise Lakebase are designed to let users run governed, secure operational data workloads directly on Databricks without having to configure connections between their PostgreSQL database and AI development pipeline or move data between systems, according to a Databricks spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{179}" paraid="206944759"><span xml:lang="EN-US" data-contrast="auto">Meanwhile, instant database branching stands out as perhaps Lakebase's most significant feature, according to Pratt.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{185}" paraid="1338761809"><span xml:lang="EN-US" data-contrast="auto">"Instant branching improves developer productivity by making it easier to test on production-like data without putting production systems at risk," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{191}" paraid="1538658303"><span xml:lang="EN-US" data-contrast="auto">McKnight, however, highlighted </span><a rel="noreferrer noopener" target="_blank" href="https://www.linkedin.com/pulse/decoupled-storage-compute-paradigm-shift-building-modern-kamdar/"><span xml:lang="EN-US" data-contrast="none">decoupled compute and storage</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{202}" paraid="1922075115"><span xml:lang="EN-US" data-contrast="auto">"This fundamental shift directly addresses the long-standing 'architectural bottleneck' by facilitating serverless autoscaling and limiting resource contention between demanding analytical workloads and live operational applications," he said.&nbsp;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <h2>Looking ahead</h2> </div> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{214}" paraid="683076"><span xml:lang="EN-US" data-contrast="auto">With Lakebase now generally available, one of Databricks' focal points is to make it easy to operate a large number of databases at scale, according to the spokesperson.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{220}" paraid="719851116"><span xml:lang="EN-US" data-contrast="auto">Ease-of-use is a wise focus for Databricks, according to McKnight.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{226}" paraid="595465481"><span xml:lang="EN-US" data-contrast="auto">Databricks has historically appealed to technical experts while rival Snowflake has targeted business users. To broaden its appeal, McKnight advised Databricks to improve Databricks Serverless, a fully managed service that removes infrastructure management tasks, and its Databricks One user interface.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{232}" paraid="922340491"><span xml:lang="EN-US" data-contrast="auto">"By evolving its Serverless and Databricks One initiatives into a true zero-administration environment, Databricks can appeal to business analysts who want the architectural efficiency of a lakehouse without the traditional engineering overhead," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{238}" paraid="673756392"><span xml:lang="EN-US" data-contrast="auto">An additional area of focus could be </span><a rel="noreferrer noopener" target="_blank" href="https://www.techtarget.com/searchitchannel/news/365532532/Cloud-cost-management-takes-center-stage"><span xml:lang="EN-US" data-contrast="none">cost control</span></a><span xml:lang="EN-US" data-contrast="auto">, McKnight continued.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{249}" paraid="1018231783"><span xml:lang="EN-US" data-contrast="auto">"To neutralize Snowflake, Databricks must … prove that it can provide a lower total cost of ownership while bridging the AI return on investment gap with production-ready, operational templates," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{2122e677-dec0-413f-8564-2b52b6f50fee}{255}" paraid="1824299946"><span xml:lang="EN-US" data-contrast="auto">Pratt, meanwhile, suggested that Databricks expand efforts to converge operational and analytical workloads to fuel AI initiatives, including providing practical guidance and reference architectures that help customers </span><a rel="noreferrer noopener" target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail"><span xml:lang="EN-US" data-contrast="none">move from pilots to enterprise-wide production</span></a><span xml:lang="EN-US" data-contrast="auto">.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{11}" paraid="447530315"><span xml:lang="EN-US" data-contrast="auto">"The next chapter is adoption, helping customers turn convergence into production applications that deliver real-time decisions," he said.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:278}">&nbsp;</span></p> </div> <div> <p paraeid="{1caa322e-0772-464b-9c46-3f256cf96d10}{17}" paraid="939591930"><span xml:lang="EN-US" data-contrast="auto">Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:210,&quot;335559740&quot;:276}">&nbsp;</span></p> </div> Resulting from the $1B acquisition of Neon, the database built for AI workloads -- including separate compute and storage -- is now integrated with the vendor's broader platform. https://cdn.ttgtmedia.com/rms/onlineimages/disaster_recovery_a379640336.jpg https://www.techtarget.com/searchdatamanagement/news/366638723/Databricks-launches-PostgreSQL-Lakebase-to-aid-AI-developers Thu, 05 Feb 2026 11:35:00 GMT <p>As winter's chill blankets much of the U.S., Snowflake continues to drop new capabilities that simplify developing agents and other advanced applications.</p> <p>On Tuesday during Snowflake Build London, a user event in the United Kingdom, the vendor launched Cortex Code, Semantic View Autopilot and the native integration of <a href="https://www.techtarget.com/searchdatamanagement/news/366625068/Snowflake-acquisition-of-Crunchy-Data-adds-Postgres-database">Snowflake Postgres</a> in its AI Data Cloud, among a spate of other capabilities.</p> <p>Cortex Code is an agent that enables users to generate code for building pipelines and applications while applying an enterprise's security and governance controls. Semantic View Autopilot is an AI-powered service that automates creating and governing the semantic views that give agents proper context. And Snowflake Postgres is a PostgreSQL database that Snowflake acquired in June 2025.</p> <p>In addition to the new features, Snowflake on Feb. 2 unveiled a $200 million partnership with OpenAI that makes <a href="https://www.techtarget.com/searchenterpriseai/news/366622996/Whats-new-and-not-new-with-OpenAIs-latest-reasoning-models">OpenAI models</a> natively available within Snowflake's Cortex AI development environment. In addition, it includes plans for collaborating to build and deploy customized AI capabilities.</p> <p>Collectively, the partnership and new capabilities are important advances for Snowflake, according to William McKnight, president of McKnight Consulting. In particular, he noted the value of eliminating costly and complex data pipelines by natively embedding Snowflake Postgres in the vendor's AI Data Cloud and Cortex Code's understanding of an enterprise's data environment.</p> <p>"Snowflake, in this trove of announcements, wins&nbsp;the&nbsp;year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform," McKnight said.</p> <p>Sanjeev Mohan, founder and principal of analyst firm SanjMo, similarly called Snowflake's latest slew of features significant for <a href="https://www.techtarget.com/searchdatamanagement/feature/Whoop-pushing-AI-limits-powered-by-Snowflake">the vendor's customers</a>.</p> <p>"Snowflake already innovates fast, and the pace has picked up," he said. "Collectively, they give customers more optionality. And there was a big emphasis on skills, helping users codify their complex processes. That's a big benefit."</p> <p>Based in Bozeman, Mont., but with no central headquarters, Snowflake is a data management vendor that has added AI development capabilities over the past few years in response to surging interest from customers in AI. Build London marks the third event in the last eight months at which Snowflake has unveiled a multitude of new AI capabilities, following <a href="https://www.techtarget.com/searchdatamanagement/news/366625010/AI-tools-highlight-latest-swath-of-Snowflake-capabilities">Summit last June</a> and <a href="https://www.techtarget.com/searchdatamanagement/news/366634007/Snowflake-delivers-slew-of-AI-tools-introduces-new-ones">Build last November</a>.</p> <section class="section main-article-chapter" data-menu-title="Driving development"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Driving development</h2> <p>Snowflake's aim is to enable customers to create a connected data estate that can be trusted as a foundation for building and deploying AI and analytics applications, according to Christian Kleinerman, the vendor's executive vice president of product who spoke during a virtual press conference on Jan. 28.</p> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> Snowflake, in this trove of announcements, wins the year in data so far and [furthers] its transition from a specialized data warehouse to a comprehensive AI and application platform. </figure> <figcaption> <strong>William McKnight</strong>President, McKnight Consulting </figcaption> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Despite enterprises <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">increasing their investments in AI development</a> and vendors such as Snowflake and rival Databricks attempting to simplify building cutting-edge tools by providing development frameworks, most AI initiatives <a target="_blank" href="https://www.pmi.org/blog/why-most-ai-projects-fail" rel="noopener">never make it past experimentation</a> and into production.</p> <p>Poor data foundations and improper alignment with governance policies are among the main reasons that the failure rate remains so high.</p> <p>Each of the three main capabilities Snowflake unveiled on Tuesday are designed to help customers create a connected, trusted data foundation for AI and analytics.</p> <p>AI-powered code generation capabilities are not uncommon. However, tools that align natural language-generated code with governance and security policies from the outset of the development process are uncommon. And when enterprise-grade governance and security policies are applied to code late in development, the AI-generated code often doesn't align with an enterprise's governance and security standards, and the project never makes it past the pilot stage.</p> <p>Domo recently <a href="https://www.techtarget.com/searchbusinessanalytics/news/366637892/Domo-adds-App-Catalyst-to-platform-to-aid-AI-development">launched App Catalyst</a>, an AI-powered code generator that integrates governance and security from the outset of a project. Now, Snowflake is doing something similar with the release of Cortex Code.</p> <p>Cortex Code, now part of Snowflake's Cortex AI development suite, understands user data, governance and operational semantics to give it context for creating code, and maintains an enterprise's governance and security standards to ensure that the code is enterprise-grade. Using the tool, data and AI teams can create production-ready applications far more efficiently than when they write code on their own.</p> <p>"The most significant announcement we're making at Build is we're introducing Cortex Code," Kleinerman said.</p> <p>While Cortex Code aids AI development by simplifying code generation, Semantic View Autopilot automates the creation of <a target="_blank" href="https://tdwi.org/articles/2023/07/13/arch-all-importance-of-the-universal-semantic-layer-in-modern-data-analytics-and-bi.aspx" rel="noopener">a semantic layer</a> so that <a href="https://www.techtarget.com/whatis/definition/metadata">metadata</a> and metrics are consistent across an organization and data can be discovered and trusted to inform analytics and AI applications. Similarly, running Snowflake Postgres natively within Snowflake's AI Data Cloud rather than externally through an integration advances development by simplifying access to unified transactional and analytical data that informs applications.</p> <p>"I like Semantic View Autopilot," Mohan said. "For non-technical users to create agents with Snowflake Intelligence -- Cortex Code is for techies -- really well, there has to a robust semantic layer. That, to me, is the most important of the new items."</p> <p>McKnight, meanwhile, called out integrating Snowflake Postgres into <a href="https://www.techtarget.com/searchdatamanagement/news/366625218/Snowflake-continues-to-add-AI-boost-Cortex-capabilities">the AI Data Cloud</a> as perhaps the most valuable of the new features.</p> <p>"Snowflake Postgres [transforms] Snowflake from a purely analytical data warehouse into a transactional and analytical platform," he said. "Snowflake is not the first to bridge this gap, but it's significant because … it opens entirely new use cases, removes the cost and complexity of [extract, transform and load] pipelines, and enables zero-code migration."&nbsp;</p> </section> <section class="section main-article-chapter" data-menu-title="Beyond the big three"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Beyond the big three</h2> <p>In addition to the launches of Cortex Code and Semantic View Autopilot, and the native integration of Snowflake Postgres in the AI Data Cloud, Snowflake's new partnership with OpenAI is a significant move, according to McKnight.</p> <p>Cortex AI enables users to access numerous AI models, including those from AI21 Labs, Anthropic, DeepSeek, Google Cloud, Meta and Mistral AI.</p> <p>Even OpenAI models were available to users before the new partnership between the AI developer and Snowflake. However, they were only available through <a href="https://www.techtarget.com/searchdatamanagement/news/366619812/Snowflake-adds-OpenAI-models-with-Microsoft-integration">Snowflake's integration with Microsoft</a>.</p> <p>Native availability is a direct integration between a model and the architecture of an AI development environment such as Cortex AI, including its access to data and enforcement of governance and security policies. Unlike other methods of connecting models with development environments such API integrations or plug-ins, no complex configurations are required.</p> <p>In addition to OpenAI's models, models from <a href="https://www.techtarget.com/searchdatamanagement/news/366635815/Snowflake-Anthropic-boost-partnership-with-200M-commitment">Anthropic</a>, <a href="https://www.techtarget.com/searchdatamanagement/news/366637132/Snowflake-boosts-Google-partnership-integrates-Gemini-3">Google Cloud</a>, Meta and <a href="https://www.techtarget.com/searchbusinessanalytics/news/366572276/Snowflake-signals-AI-commitment-with-Mistral-AI-partnership">Mistral AI</a> are natively available in Cortex AI.</p> <p>"Moving OpenAI models natively into Snowflake is a game-changer because it keeps sensitive data entirely within the Snowflake security perimeter, effectively removing the complex governance and data egress obstacles that kill enterprise AI projects," McKnight said. "Instead of complicated engineering, analysts can trigger GPT using simple SQL functions, democratizing high-level AI across the organization."</p> <p>Additional new features Snowflake unveiled during Build London include the following:</p> <ul type="disc" class="default-list"> <li>Expansion of the Snowflake Horizon Catalog to include the open-source Polaris Catalog, which lets customers securely access data in <a target="_blank" href="https://iceberg.apache.org/" rel="noopener">Apache Iceberg</a> tables as well as create, update and manage data stored in Iceberg tables.</li> <li>Open Format Data Sharing to extend Snowflake's zero-ETL capabilities to open table formats Apache Iceberg and <a href="https://www.techtarget.com/searchdatamanagement/news/366542953/Databricks-introduces-Delta-Lake-30-to-help-unify-data">Delta Lake</a>.</li> <li>Snowflake Backups to protect business-critical data from ransomware or disruptions.</li> <li>Updates to Snowflake Notebooks, including an integration with Cortex Code and Experiment Tracking, to make it easy for teams to compare testing results and reproduce top-performing models.</li> <li>Cortex Agent Evaluations so users can trace, measure and audit agent behavior.</li> <li>An integration with Vercel that enables <a href="https://www.techtarget.com/searchcio/feature/Vibe-coding-What-IT-leaders-need-to-know">vibe coding</a> -- AI-assisted code generation using natural language prompts -- to build applications that can be deployed in Snowflake through Snowpark Container Services.</li> <li>An integration with the Brave Search API so users can integrate real-time information from the internet into Snowflake Intelligence, Cortex Code and Cortex Agents to augment an enterprise's proprietary data.</li> <li>New features in Workspaces, Snowflake Notebooks and OpenID Connect aimed at better enabling <a href="https://www.techtarget.com/searchsoftwarequality/tip/Improving-DevOps-collaboration-Challenges-and-tips">collaborative development</a>.</li> </ul> <p>"Some of [the new features] are improvement on existing technologies, but in all instances it's customer-driven innovation," Kleinerman said regarding Snowflake's impetus for developing the capabilities introduced at Build London.</p> </section> <section class="section main-article-chapter" data-menu-title="Competitive standing"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Competitive standing</h2> <p>Two years after making AI development its main priority, Snowflake might have finally caught up to Databricks and other data and AI platform vendors, according to Mohan.</p> <p>After OpenAI's November 2022 launch of ChatGPT significantly improved generative AI technology, Databricks and hyperscale cloud vendors AWS, Google Cloud and Microsoft all quickly reacted. They created environments for customers to build AI tools, including development frameworks and integrations with AI providers such as OpenAI.</p> <p>Snowflake was slower to react, and only fully committed to enabling AI development in February 2024 when <a href="https://www.techtarget.com/searchbusinessanalytics/news/366571855/Snowflake-CEO-Slootman-steps-down-Ramaswamy-takes-over">Sridhar Ramaswamy was named CEO</a>.</p> <p>"They've caught up," Mohan said, noting that Google Cloud similarly had to catch up after being viewed as an innovator of machine learning capabilities with its 2017 release of the Transformer <a href="https://www.techtarget.com/searchenterpriseai/definition/neural-network">neural network</a> architecture.</p> <p>"Google invented the Transformer and then watched the whole world take off with not only OpenAI but Meta with Llama and others," Mohan continued. "But look where Google is now with Gemini. So, it is too early to call winners in AI, and Snowflake has demonstrated that it has caught up after a late start."</p> <p>McKnight likewise noted that with the release of its latest set of features -- particularly the integration of its Polaris and Horizon <a href="https://www.techtarget.com/searchbusinessanalytics/news/252510804/Data-catalogs-fuel-increased-efficiency-speed-to-insight">data catalogs</a> -- Snowflake has fully caught up with its peers.</p> <p>"Snowflake is now arguably ahead of Databricks in its ability to unify transactional applications and analytics, while having simultaneously neutralized the 'lock-in' argument," he said. "By embedding Apache Polaris directly into the Horizon Catalog, Snowflake now offers the same open governance as Databricks' Unity Catalog."</p> <p>Looking ahead to what Snowflake could do next to continue serving its users and perhaps even attract new ones, McKnight named adding <a href="https://www.computerweekly.com/opinion/Better-governance-is-required-for-AI-agents">agent governance capabilities</a> and more cost transparency.</p> <p>"In its highly competitive market, it needs to address agent governance with a layer that governs intent and action, and application-centric costing where instead of seeing costs by warehouse, there is a 'Product View' that bundles the costs of the Postgres instance, the Snowpark Container Services and the Cortex API."</p> <p>Mohan, meanwhile, suggested that Snowflake take steps to unify transactional processing and observational data such as <a href="https://www.techtarget.com/searchcustomerexperience/feature/To-predict-customer-buying-behavior-stop-look-listen-analyze">customer behavior</a>.</p> <p>"I would like them to show how I as a developer can access all my data in a unified manner through a catalog," he said. "Horizon doesn't handle observe data, and I'd like to see all data in one place."</p> <p><i>Eric Avidon is a senior news writer for Informa TechTarget and a journalist with three decades of experience. He covers analytics and data management.</i></p> </section> New features such as an agent-powered code generator and automated semantic modeling simplify developing cutting-edge applications and improve the vendor's competitive standing. https://cdn.ttgtmedia.com/rms/onlineimages/code_g1287248739.jpg https://www.techtarget.com/searchdatamanagement/news/366638535/Snowflake-launches-new-AI-tools-unveils-OpenAI-partnership Tue, 03 Feb 2026 03:00:00 GMT <p>Neither potential employers nor job searchers can avoid AI in the dance that is today's sourcing, recruiting, applying and interviewing. Yet, like all new disruptive technologies, there is a disparity in the degree to which AI is applied within applications. Likewise, there is the inevitable gap between early and late adopters, compounded by business maturity level, user readiness and training, and the perceived ability of AI to address current issues.</p> <p>While vendors were busy either bolting AI features to their products or extending their AI native-built applications over the past year, two of my favorite companies were acquired: Paradox, with its leading AI conversational assistant Olivia, was acquired by Workday, and SmartRecruiters, a longtime favorite, was bought by SAP. Both bring distinctive, proven AI capabilities to these companies' product offerings.</p> <section class="section main-article-chapter" data-menu-title="Considerations for setting purchase criteria"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Considerations for setting purchase criteria</h2> <p>What should buyers look for when seeking AI in talent acquisition products in 2026?</p> <p>There are four levels of AI maturity, varying from <i>assistive AI</i>, which<b> </b>provides suggestions, recommendations, and summaries; <i>copilots</i> that execute tasks when prompted by a human; <i>semi-agentic models</i> that proactively run multi-step workflows with human oversight; and totally <i>autonomous agents</i> that execute end-to-end processes with minimal human intervention. Buyers should be aware of these differences when choosing products. Many vendors claim to have agents but instead deliver copilots.</p> <h3>The ubiquitous chatbot</h3> <p>Chatbots are universal, and many job candidates say they prefer chatting with a bot over talking to a human -- to a point. While early chatbots were often little more than pop-ups that gave people a menu of choices from which to access more information, today AI adds <a href="https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP?Offer=ab_MeteredFormCopyDef_var3">natural language processing</a> to interpret questions and give more specific, tailored responses to people searching for information.</p> <p>A chatbot is a reactive, conversational interface that can answer questions or complete simple tasks only when prompted by a user. Paradox's bot, Olivia, is a case in point. It can manage an entire hiring process for an hourly worker in a series of friendly text conversations, though not without the potential for <a target="_blank" href="https://biztechweekly.com/mcdonalds-hiring-chatbot-olivia-data-breach-exposes-64m-applicants-revealing-critical-llm-security-flaws-in-recruitment-systems/" rel="noopener">security breaches</a> and other risks. StepStone's Mya, SmartRecruiters' Winston, SAP's Joule (which will now benefit from Winston's recruiting specialty), Beamery's Ray and iCIMS' Digital Assistant, which automate self-service, qualification screening and interview scheduling, are just a few among many.</p> <p>You can expect any recruiting system to include a bot that can suggest jobs to candidates, assist them in applying, update their progress through the hiring cycle, schedule and even conduct interviews, and onboard them -- whether or not the bot has a classy name.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/the_good_and_not_so_good_of_ai_recruiting_tools-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/the_good_and_not_so_good_of_ai_recruiting_tools-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/the_good_and_not_so_good_of_ai_recruiting_tools-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/the_good_and_not_so_good_of_ai_recruiting_tools-f.png 1280w" alt="Chart showing pluses and minuses of AI recruiting tools" height="246" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>AI brings major benefits to recruiting but also carries significant risk. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>GenAI: Master content generator</h3> <p>Generative AI, which produces new content, such as text, images and program code, is proving a boon to recruiters. It can create personalized correspondence, engaging job descriptions, as well as offer and rejection letters.</p> <p>GenAI is also useful to candidates for creating resumes -- including nefarious ones who list fake qualifications to match job requirements for which they lack the necessary skills. Just as it is a time-saver for recruiters, it enables applicants to send out many more tailored resumes and cover letters than ever before. On the downside, this often leads to swamping recruiters with mediocre candidates.</p> <p>Examples of GenAI tools include ChatGPT, Gemini and Claude. While AI-generated content should always undergo human review, these are effective labor-saving tools for the talent acquisition process.</p> <p><iframe title="How AI amplifies resume fraud and other job seeker cheating" allowtransparency="true" height="150" width="100%" style="border: none; min-width: min(100%, 430px); height: 150px;" scrolling="no" data-name="pb-iframe-player" src="https://www.podbean.com/player-v2/?i=cymcw-1a126cd-pb&amp;from=pb6admin&amp;pbad=0&amp;share=1&amp;download=1&amp;rtl=0&amp;fonts=Arial&amp;skin=1&amp;font-color=auto&amp;logo_link=episode_page&amp;btn-skin=2baf9e" loading="lazy"></iframe></p> <h3>Copilots: The move to decision support</h3> <p>But then, what are copilots? On the one hand, <i>copilot</i> is specific branding used primarily by Microsoft for its suite of AI-powered assistants. Like ChatGPT and the others mentioned above, Microsoft Copilot is an example of generative AI that searches and delivers information in response to questions. However, unlike general-purpose GenAI tools like ChatGPT, copilots are designed to operate with specific work data in the vendor's ecosystem.</p> <p>For example, iCIMS Copilot is talent acquisition-specific: It generates interview questions for open requisitions for specific roles based on defined criteria, including proficiencies, level of experience, skills, industry and more. Beyond just generating content, iCIMS Copilot can recommend changes in real time as it dynamically marks up users' work. For example, if you ask the tool to make a senior-level job description more junior, it will suggest changes that cater to less experienced candidates.</p> <p>Other AI products branded as copilots include the following:</p> <ul class="default-list"> <li><b>Findem Copilot </b>focuses on automating and personalizing candidate sourcing and pipeline building.</li> <li><b>Paychex Recruiting Copilot, </b>developed in partnership with Findem,<b> </b>uses AI to analyze candidate data and help hiring teams find and match talent faster.</li> <li><b>Talentech AI Copilot</b> assists recruiters and HR teams with job ad creation, screening, generating questions for structured interviews, campaign design, talent pool analysis, HR-related FAQs and more.</li> <li><b>Recruit Copilot</b> is standalone software that<i> </i>focuses on automating resume screening and candidate ranking to accelerate hiring.</li> </ul> <p>Similar functionality is available from many vendors, but these tools might not be called <i>copilots</i>. AI-based assistants by Eightfold, SeekOut and others serve similar purposes, in that they help recruiters write job ads, screen candidates, create interview questions, summarize candidate data, etc. And they are getting smarter: PageUp recently announced Paige, which uses generative and agentic AI to support recruiters, from candidate sourcing to automating routine steps. Like other GenAI tools, it provides on-demand answers to queries, skills matching, resume summarization and content suggestions, but it also -- through agentic AI -- automates repetitive duties (e.g., filling fields, job creation workflows) and suggests next-best actions to speed up hiring.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/how_hr_vendors_will_use_generative_ai-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/how_hr_vendors_will_use_generative_ai-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/how_hr_vendors_will_use_generative_ai-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/how_hr_vendors_will_use_generative_ai-f.png 1280w" alt="Graphic listing how HR vendors will use generative AI." height="342" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Performance reviews and recruiting are two of the more common early applications of generative AI in HR. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <h3>The rise of agentics</h3> <p>An AI agent is software that can understand goals, make decisions and take actions autonomously -- often across multiple steps and tools -- to achieve a desired outcome on the user's behalf. It interacts with its environment, collects data and uses the data to perform self-determined tasks to meet predetermined goals.</p> <p>AI agents are action-oriented and interact with other agents. They analyze the collected data to predict the best outcomes that support the goals and then use the results to formulate the next action they should take.</p> <p>The shift from assistive AI (e.g., suggesting templates or matching candidates) to agentic AI adds the following capabilities:</p> <ul class="default-list"> <li>Agents can interact directly with candidates.</li> <li>They can perform multi-step tasks without explicit human control at every step.</li> <li>They can integrate deeply with CRM, applicant tracking systems (<a href="https://www.techtarget.com/searchhrsoftware/definition/applicant-tracking-system-ATS">ATSes</a>) and other HR systems to orchestrate workflows from end to end.</li> </ul> <p>Some AI agents learn over time; some don't. Learning is a design choice, not a requirement. In general, there are three types: static agents that follow rules but don't update themselves, agents that learn from explicit or implicit feedback, and agents that learn continuously. It's imperative to understand agent behavior in the products you buy.</p> <p>In addition to vendors discussed below, companies with an increasingly strong agentic agenda include SpiderX AI, which focuses on multimodal, autonomous recruiting agents; Cielo, with agents tied to talent acquisition workflows; and Torre.ai, with agentic recruiting from sourcing to placement.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment.png 1280w" alt="Diagram of the steps in the recruitment process" height="353" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>AI plays a role in many steps of the recruiting process, especially job posting, candidate screening and interview scheduling. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Top contenders for 2026"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Top contenders for 2026</h2> <p>There are many ways to evaluate products and vendors, including breadth or depth of functionality, technology, price, global availability, the software's sophistication and complexity, the degrees of support and training provided and the vendor's reputation for integrity or eco-consciousness, among others. Key for any buyer is that the product -- with or without AI -- addresses all of the talent acquisition pain points that the organization faces.</p> <p>Below is a review of products that merit consideration in 2026, chosen because they are from established vendors, they are comprehensive, or, in my judgment, they represent the best of today's leading edge. These products all tend to address the needs of at least three distinct user groups: candidates, recruiters and hiring managers, and already-hired employees.</p> <p>Here is what you can expect from any competitive suite: support for sourcing, job posting, data parsing, candidate shortlisting or ranking, multichannel communications, career site and mobile device chatbots, and intuitive UIs geared to the respective needs of the candidate, hiring manager and recruiter. <a href="https://www.techtarget.com/searchhrsoftware/feature/What-HR-must-know-about-recruiting-analytics">All deliver analytics</a> relevant to their product scope. Expect AI-supported scheduling of interviews and follow-up conversations, along with immediate and totally automatic conducting of interviews without recruiter intervention. Many have extended their capabilities to current employees to meet the growing demand for career growth and mobility.</p> <p>Look for agent-managed workflows and review the vendor's agentic AI roadmap for how it might meet your requirements. The goal here is not to cover every feature or function, but to differentiate products based on points of uniqueness or specific strengths and to present vendors in alphabetical order, not ranked.</p> <h3>Beamery</h3> <p>The Beamery suite covers talent acquisition, internal mobility and redeployment,<b> </b>skills analytics and workforce planning, and predictive talent pipelines. Last summer, Beamery launched its Workforce Intelligence Suite, which includes a Task Intelligence module that breaks down roles into tasks. According to Beamery, layering this capability with skills and market data enables HR leaders to build a digital organizational twin of their workforce, modeling how best to apply AI alongside people, as well as reskill and redeploy workers with precision to build a more adaptive organization.</p> <p>For organizations transitioning to a skills-first approach, Task Intelligence provides the following capabilities:</p> <ul class="default-list"> <li><b>Role-to-task mapping.</b> This function analyzes internal human capital management (HCM) systems, job descriptions and planning inputs to break complex roles into core tasks and the skills required to perform them.</li> <li><b>Enriched task-level intelligence.</b> Combining internal data with real-time labor market signals enables companies to evaluate each task's demand, effort, skill requirements and automation potential.</li> <li><b>Automation opportunity analysis.</b> This function identifies high-effort, high-frequency tasks that are strong candidates for automation or augmentation and estimates time as well as cost savings.</li> <li><b>Reskilling and redeployment pathways.</b> This capability pinpoints business-critical tasks and matches them to available talent with the relevant skills.</li> <li><b>Scenario modeling with a digital organizational twin.</b> Merging task-level data with Beamery's Skills Intelligence and Talent Market Insights modules enables leaders to simulate the impact of workforce changes before they act.</li> </ul> <p>Beamery also uses AI to reveal <a href="https://www.hrdive.com/news/human-skills-gaps-could-threaten-ai-adoption-learning/758016/" target="_blank" rel="noopener">skills gaps</a> and critical role vulnerability, but the ability to combine real-time external labor market insights with internal HR data to forecast skill availability makes Beamery's latest offering stand out.</p> <p>Last summer's announcement also included Ray, an AI assistant that provides contextual recommendations across sourcing, hiring, workforce planning and upskilling.</p> <h3>Eightfold AI Talent Intelligence Platform</h3> <p>Eightfold markets a talent intelligence platform that enables "holistic" talent strategies. Through its proprietary global data set of more than 1.5 billion talent profiles and skills, Eightfold's AI-native technology generates recommendations to help employers decide how and when to build, buy or borrow talent. It employs a skills-based framework that matches people to opportunities, including full-time, part-time, project and gig work. Eightfold uses that data to understand the availability, maturity, relevance, learnability and evolution of skills in specific organizations and throughout the global market.</p> <p>Eightfold's Talent Design uses a skills-based approach to drive decision-making for upskilling, reskilling, hiring, staffing of contractors and attaining diversity, equity and inclusion (DEI) goals. Additionally, Talent Design examines skill adjacency and context to determine what capabilities will be needed in the future as an organization grows. The platform also enables self-learning, data-driven updates that help to ensure consistent, unbiased evaluations of individual capabilities and the ability to learn against globally standardized job descriptions and requirements.</p> <p>The Eightfold AI talent intelligence platform and accompanying suite of applications is available in 155 countries and 24 languages. One of the signature applications, Talent Management, enables employees to find reskilling and upskilling opportunities across courses, mentors and projects based on current skills and career aspirations. The goal of people developing their own skills and assuming responsibility for their career growth is addressed through curated opportunities for continuous learning. In addition, Talent Management includes capabilities for succession planning, helping organizations to better understand the potential of their workforce on a global scale and to guide individual employees toward further learning, skill development and career opportunities.</p> <p>The other main module of the talent intelligence suite, Talent Acquisition, represents the original functionality that Eightfold AI focused on when it was founded in 2016. Eightfold Talent Acquisition provides functions for sourcing, CRM, applicant tracking -- e.g., requisition management, job distribution, offer management, etc. -- interview scheduling and more. Eightfold AI also introduced copilots that use GenAI to improve productivity and the user experience for candidates, employees and recruiters.</p> <p>All in all, Eightfold's semi-agentic intelligence layer acts more as a brain for strong decision-making than an autonomous executor of action. As a bonus, the platform integrates with Workday, SAP SuccessFactors Recruiting, Oracle Taleo, Oracle Fusion Cloud Recruiting, Greenhouse and others.</p> <h3>Employ</h3> <p>Employ was created to consolidate the following recruiting products across different market segments, small business through enterprise:</p> <ul class="default-list"> <li><b>JazzHR</b> typically focuses on the ATS needs of SMBs. It uses basic AI to streamline workflows like resume parsing, knockout questions and simple candidate profile summaries, and it provides automated candidate evaluations tied to job descriptions.</li> <li><b>Lever</b> targets midmarket and growth companies focused on candidate engagement. Its differentiator is blending ATS with CRM automation. AI supports that two-way recruiting cadence.</li> <li><b>Jobvite</b> is positioned as an end-to-end, enterprise-grade recruiting platform. It embeds AI across the entire hiring lifecycle, including sourcing, engagement, screening and interview processes.</li> </ul> <p>These brands operate independently, with separate product lines and customer bases, but they share some core AI capabilities:</p> <ul class="default-list"> <li><b>Candidate fit scoring.</b> All three can employ the Talent Fit feature to compare resumes to job descriptions using <a href="https://www.techtarget.com/whatis/definition/large-language-model-LLM">large language models</a> and produce a fit score and guidance, purportedly without human bias.</li> <li><b>AI-driven screening and automation.</b> Employ is rolling out "AI companions" for screening, interviewing and other functions, which can surface top talent and provide candidate feedback across platforms, starting with Lever and expanding to the others.</li> <li><b>Responsible AI governance.</b> Employ's AI suite uses tools like IBM watsonx.governance for transparency and bias monitoring.</li> </ul> <h3>hireEZ</h3> <p>HCM software vendor hireEZ unveiled EZ Agent, a semi-autonomous agentic recruiting application built into its AI-first talent acquisition platform, to automate sourcing, screening, outreach, scheduling, analytics and other recruiting tasks while keeping human recruiters in control of strategic decisions.<b> </b>In addition to sourcing, search refinement and pipeline rebuilding, the agent can proactively make recommendations and execute workflow while keeping the recruiter in the approval loop for outreach and actions.</p> <p>EZ Agent improves with each interaction, refining parameters and understanding recruiter preferences to enhance outcomes. Operating with contextual understanding enables it to make decisions and execute multi-step hiring processes without constant human input. For instance, it adjusts strategies in real time based on candidate engagement and recruiter feedback, and it actively manages workflows such as sourcing candidates, launching campaigns and screening applicants. Its ResumeSense feature flags inconsistencies in resumes and highlights key qualifications for recruiter review, maintaining both precision and transparency.</p> <p>Other highlights of the hireEZ platform include the following:</p> <ul type="disc" class="default-list"> <li><b>Generative AI for job descriptions.</b> This function creates engaging, inclusive job descriptions tailored to attract diverse talent.</li> <li><b>Integration enhancements. </b>Seamless synchronization with ATS platforms provides two-way data updates, eliminating the need to switch systems during recruiting.</li> <li><b>Text campaigns.</b> hireEZ's texting feature, described as the "dating-app swipe" of recruiting, offers direct, high-stakes communication to stand out in competitive markets.</li> </ul> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/Aqp81W-U3Hg?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> <h3>iCIMS</h3> <p>iCIMS offers an enterprise-grade recruiting platform with embedded, end-to-end AI designed to help organizations hire more efficiently while maintaining transparency, control and compliance. iCIMS AI is built to support human decision-making, with a strong emphasis on responsible, explainable AI use across the hiring lifecycle.</p> <p>Key capabilities include the following:</p> <ul class="default-list"> <li><b>AI-driven candidate matching and ranking.</b> iCIMS AI analyzes candidate profiles across applicants, internal talent and past candidates to surface strong-fit matches while providing recruiters visibility into how recommendations are generated. AI outputs are designed to inform decisions, not replace them.</li> <li><b>Agentic AI sourcing with recruiter oversight.</b> The platform can automatically search internal databases to rediscover qualified candidates while ensuring recruiters retain control over outreach and selection decisions.</li> <li><b>Generative AI to support recruiter workflows.</b> Recruiters can use natural-language prompts to generate job descriptions, interview questions, candidate communications and search queries, helping teams reduce manual work without sacrificing quality or consistency.</li> <li><b>AI-enabled career site optimization.</b> The platform can automatically generate SEO-friendly job content and translate career-site experiences into multiple languages, expanding reach while maintaining consistency across regions.</li> <li><b>Built-in responsible AI framework.</b> iCIMS applies formal <a href="https://www.techtarget.com/searchenterpriseai/definition/responsible-AI">responsible AI</a> principles focused on transparency, bias mitigation, auditability and human-in-the-loop decision-making, helping organizations adopt AI in recruiting while meeting ethical and regulatory expectations.</li> </ul> <p>iCIMS is designed for large and complex organizations that require a scalable recruiting platform where AI improves speed and efficiency without compromising trust, compliance or human judgment. It was ranked No. 1 in ATS market share by Apps Run the World.</p> <h3>Oracle</h3> <p>Oracle is very likely the leader in overall enterprise-wide agentic AI at this writing. The agent roster includes job discovery, job fit advisor and interview management agents for internal mobility and recruiting; team sync advisor, team goals assistant, learning tutor and talent advisor agents in skilling and career development; and employee lifecycle policy analyst, succession planning advisor and payroll run analyst agents.</p> <p>As an example, Oracle Career Coach is an AI-powered capability in Oracle Fusion Cloud Recruiting designed to deliver a more personalized, intelligent candidate experience using agentic AI that works across the Oracle Fusion Cloud HCM suite to analyze candidates' skills, experience and interests -- in both internal and external talent pools -- to surface stronger job matches. This technology is expected to help organizations improve applicant quality, increase candidate engagement and give hiring teams deeper, data-driven talent insights.</p> <p>Oracle introduced the following new agents in the past year:</p> <ul class="default-list"> <li><a title="https://docs.oracle.com/en/cloud/saas/readiness/hcm/25d/recr-25d/25D-recruiting-wn-f40160.htm" target="_blank" href="https://docs.oracle.com/en/cloud/saas/readiness/hcm/25d/recr-25d/25D-recruiting-wn-f40160.htm" rel="noopener">Job applicant screening</a>, which lets users ask questions about an applicant's profile, including feedback responses and scores, lifecycle information, prior actions taken, authenticity detection, assessment and background check information.</li> <li><a target="_blank" href="https://www.oracle.com/news/announcement/oracle-ai-agents-help-hr-leaders-boost-workforce-productivity-and-enhance-performance-management-2025-09-16/" rel="noopener">Interview management</a>, which automates interview scheduling by coordinating interviewer availability, managing calendar invitations, resolving conflicts and sending reminders to candidates and interviewers.</li> <li>Job requisition analyst, which supports hiring managers by answering general and role-specific questions as well as providing guidance during job requisition creation.</li> </ul> <p>The agents all run on Oracle Cloud Infrastructure and are integrated into Oracle Fusion Cloud HCM workflows at no additional cost. Oracle was named the highest-ranked vendor for the extended AI innovations use case in Gartner's 2025 "Critical Capabilities for Talent Acquisition (Recruiting) Suites" report.</p> <h3>Phenom Applied AI</h3> <p>Phenom delivers an AI infrastructure built specifically for HR, covering talent acquisition, talent management and workforce intelligence. The company's approach centers on AI that adapts to each organization's industry context, regulatory requirements and the distinct needs of knowledge workers and frontline teams. It was named a top AI company of 2025 by <i>Software Magazine</i>.</p> <p>Rather than repurposing generic enterprise technology for HR, Phenom has developed its own AI-native infrastructure over the past decade. This foundation powers a unified experience from hire to retire, with proprietary ontologies that account for industry-specific nuances -- recognizing, for instance, that hiring nurses requires fundamentally different approaches than developing engineers or retaining retail associates.</p> <p>The vendor's deeply refined skills ontology helps HR teams identify and fill skills gaps, aids managers in designating successors and driving career progression, and assists employees in discovering personalized advancement opportunities. This intelligence extends into talent acquisition and across the full talent lifecycle: sourcing, screening, onboarding, development, internal mobility and retention.</p> <p>The platform integrates with recruiting workflows, enabling talent teams to match candidates to open roles internally or externally. By automating tasks like candidate sourcing, screening and scheduling, the system can potentially accelerate hiring while improving candidate experiences. Process mining capabilities surface optimization opportunities to improve conversion rates and hiring quality.</p> <p>Phenom X+ Agents represent the company's approach to more intelligent automation. Rather than simply analyzing data or completing tasks, these agents are specialists designed to work alongside HR teams and actively execute detailed workflows across the talent lifecycle. They handle complex processes autonomously while still under human supervision, sourcing passive candidates, conducting initial screenings through conversational AI, managing interview scheduling with real-time calendar coordination and providing recruiters with AI-generated interview guides that help reduce potential biases. After the interview, agents deliver summaries that flag relevant details based on job requirements and can detect inconsistent candidate responses.</p> <p>For high-volume frontline hiring, agents enable end-to-end automation where candidates search for jobs, apply, complete assessments built by Phenom's industrial-organizational psychologists, submit videos and schedule interviews -- all via chatbot or SMS, freeing recruiters to focus on evaluation and final hiring decisions. For knowledge worker roles requiring more customized engagement, agents adapt to provide tailored candidate experiences while giving recruiters AI-driven analysis during evaluation.</p> <p>New capabilities announced in late 2025 demonstrate how Phenom seeks to connect business strategy to talent execution. Enterprise Talent Optimization &amp; Work Redesign uses AI-driven task intelligence to analyze roles at the task level, revealing automation opportunities while connecting employee development to company objectives. Frontline Workforce Lifecycle &amp; Shift Scheduling moves beyond traditional app-based experiences into an agentic, conversational model that handles shift scheduling with built-in compliance, dynamic optimization and internal mobility integration.</p> <p>Its Unified Orchestration Engine introduces adaptive intelligence that handles exceptions in real time, combining decision engines with simulations and human-in-the-loop governance. When automation encounters exceptions -- such as a manager not showing up for an interview, resume-parsing failures or compliance rules needing enforcement -- the agents identify bottlenecks, create alternative pathways and maintain workflow momentum while preserving policy compliance and explainability.</p> <p>This approach reflects Phenom's focus on building an infrastructure that learns each organization's specific patterns and applies that understanding throughout the talent lifecycle. The goal is shifting HR teams from operational tasks to strategic decisions, with AI handling administrative work while recruiters and managers bring their expertise to candidate evaluation and hiring choices.</p> <h3>SeekOut</h3> <p>SeekOut provides an AI-powered talent search engine to help recruiters quickly find and hire the most qualified passive candidates. Recruiters can use natural- language prompts instead of writing complex Boolean logic -- e.g., "find senior Python engineers in Austin with AWS experience." This makes sourcing more intuitive, and the AI creates precise search criteria from job descriptions or descriptions of ideal candidates and matches them to relevant profiles.</p> <p>In addition to the generative AI common in today's applications, SeekOut Spot uses agentic AI combined with human expertise to automate candidate research, outreach, screening and engagement at scale, delivering qualified candidates faster than manual sourcing alone. AI-guided workflows help teams go from job description to search, shortlist and engagement, with intelligent recommendations at each step.</p> <p>SeekOut's AI supports talent mobility by uncovering internal candidates and recommending career pathways. AI insights and workflows tie directly into existing applicant tracking and HR systems.</p> <p>SeekOut's ability to locate and screen tech talent should be a draw for organizations that need technologists. With SeekOut's GitHub search, the app can provide and review code to better evaluate a potential applicant's technical prowess.</p> <h3>Workday</h3> <p>All the major contenders in the HCM market have integrated their own AI capabilities while considering build-or-buy decisions. Workday's decision to acquire Paradox, AI-native HiredScore, and document intelligence platform Evisort enabled it to augment its recruiting, expenses, succession and Workday optimize agents.</p> <p>The recruiting agent, for example, uses HiredScore AI to identify the most qualified candidates from both new applications and past talent pools. Since the Paradox acquisition closed on October 1, 2025, Workday has added two applications to its growing suite of AI offerings: Workday Paradox candidate experience agent and Paradox conversational ATS, both available through Workday for new and existing customers.</p> <p>Bringing HiredScore and Paradox into the Workday ecosystem connects the different stages of hiring into one coordinated process. HiredScore helps recruiters identify and prioritize the best-qualified candidates for a role. Paradox provides a conversational candidate experience to streamline hiring tasks such as answering candidate questions and scheduling interviews. These capabilities then feed directly into the Workday Recruiting module, which manages the final hiring and onboarding. This approach removes the need for disconnected tools and helps organizations manage everyone from frontline hourly workers to corporate professionals in a single system.</p> <p>Another tool, Workday Assistant, offers AI-powered, role-specific support to employees. It provides quick answers to HR questions and is accessible from any device. It also helps employees take faster action, offering company-specific guidance to complete complex tasks.</p> </section> <section class="section main-article-chapter" data-menu-title="Remember that AI is just another tool"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Remember that AI is just another tool</h2> <p>Today's AI-grounded tools cover every aspect of talent acquisition, including recruitment marketing, sourcing, job description management, candidate assessment and pre-screening. They also recommend positions within an organization to qualified applicants, generate offer and rejection letters, perform skills assessments, and provide career and learning assistance and direction -- all with far more sophisticated analytics than was available in the past.</p> <p>As a tool in today's recruiting and hiring products, AI is inescapable, but its forms vary within every chatbot, GenAI, copilot and agentic AI product on the market. And with <a href="https://www.hrdive.com/news/eightfold-ai-lawsuit-job-candidate-consumer-reports/810332/" target="_blank" rel="noopener">compliance, bias, privacy and security risks</a> increasing with each new advance in the technology, remember that the best product for you is not necessarily the one that ticks the most AI boxes.</p> <p>Finally, remember you are not buying a risk-free AI product; it is solely an underlying technology to help address the primary recruiting issues the organization faces. Buyers are responsible for the AI recruiting tool's transparent and ethical use.</p> <p><i>K</i><i>atherine Jones is an independent market analyst and consultant. She was an analyst at the Aberdeen Group and Bersin by Deloitte and partner at Mercer following a career in high-tech companies and higher education.</i></p> </section> AI now reaches every corner of recruiting and talent acquisition. Here's the latest on AI tools, from chatbots to agentic AI, plus details on products from 9 top vendors. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a238006601.jpg https://www.techtarget.com/searchhrsoftware/tip/Top-AI-recruiting-tools-and-software-of-2022 Tue, 03 Feb 2026 00:00:00 GMT <p>We've spent the last few years obsessed with the AI models themselves, but the reality of 2026 is that the model is the easiest part of the deployment process. It's essentially a commodity.</p> <p>Of course, model improvements and efficiencies will continue, but when we look at why 62% of organizations are still hitting a wall trying to get into production, it isn't because the model lacks intelligence. If anything, that's the easy part. They're failing because the <a href="https://www.techtarget.com/searchdatacenter/feature/Infrastructure-for-machine-learning-AI-requirements-examples">surrounding infrastructure</a> and operational requirements are far more complex and dynamic.</p> <section class="section main-article-chapter" data-menu-title="The real friction in moving to production"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The real friction in moving to production</h2> <p>The most significant challenges companies face when moving from development to production are rooted in the complexities of the enterprise environment. These hurdles often involve deep-seated issues with legacy systems that weren't designed to handle the high-velocity data requirements for modern inference. It's the unpredictable nature of <a href="https://www.techtarget.com/searchenterpriseai/feature/9-data-quality-issues-that-can-sideline-AI-projects">live environments where data quality varies</a> and security requirements are constantly shifting that's causing the difficulty. Many projects stall because teams underestimate the volume of custom integration work needed to make a model useful for end users.</p> <p>The following are the top five challenges organizations run into outside of the model itself:</p> <ol class="default-list"> <li><b>Security and compliance.</b> This is the leading roadblock. Enterprise environments have established security protocols and data sovereignty rules that models must adhere to before they can access production data. I would argue that poor data foundations and governance gaps are the primary causes of project failure as we head into 2026.</li> <li><b>Scalability.</b> Systems that function during initial testing often collapse when they must support thousands of simultaneous users. The underlying infrastructure <a href="https://www.techtarget.com/searchitoperations/feature/Meeting-the-challenges-of-scaling-AI-with-MLOps">must be able to support uneven and unpredictable demand</a> while maintaining low latency across the entire enterprise network.</li> <li><b>Data integration.</b> Connecting siloed data to a model is a massive technical hurdle. It's a big reason we're seeing so many announcements from data and analytics providers related to the Model Context Protocol. It's an open standard that provides a universal way to build the plumbing between LLMs and enterprise data without requiring custom code for every integration.</li> <li><b>Change management.</b> The human friction of using AI creates a massive barrier that requires a balance of psychological safety and job reinvention. I'm not going to give the cliché of freeing people up for strategic work. What I'm seeing is that it's more about giving people their time back to finish their to-do lists within a normal workday. When employees see that the new AI tool helps them close their laptops at the end of the workday, they start to find real ways to reinvent their own workflows.</li> <li><b>Deployment automation.</b> Moving a model into production requires a shift in traditional DevOps and container management. IT teams are forced to manage heavy AI containers with granular resource allocation for GPUs and specialized memory limits. It also involves orchestrating a complex web of API connections between models and vector databases, each requiring its own security handshake and latency monitoring.</li> </ol> </section> <section class="section main-article-chapter" data-menu-title="The new operational front line"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The new operational front line</h2> <p>There's a misconception that AI is a playground for AI engineers and data scientists. However, the data tells a different story. While data scientists help develop a model, their involvement drops to 40% when it comes to managing it in production. The heavy lifting falls to IT operations and data engineers, who are involved in more than 60% of deployment and ongoing management. This shift proves that inference is an operational and infrastructure problem, not a research project.</p> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/AI-inference-vs-training-Key-differences-and-tradeoffs">Once you move to inference</a>, compute components and data management systems become the priorities. This is where hardware fractures. While the media focuses on GPUs, a quarter of organizations use CPUs for their primary inference because they're matching the hardware to the cost and latency requirements of the business task.</p> <p>The nondeterministic nature of LLMs means that traditional monitoring is insufficient. A server can be healthy while the model provides noncompliant or inaccurate answers. This gap is forcing IT operations and AI observability to merge into a single control plane focusing on the following functions:</p> <ul class="default-list"> <li><b>Retrieval quality and grounding. </b>This involves monitoring the accuracy and relevance of the data being fed into the model from your knowledge bases to prevent hallucinations.</li> <li><b>Prompt and response auditing.</b> These tasks use real-time filters that ensure personally identifiable information doesn't leak out and malicious injections don't get in.</li> <li><b>Identity and access governance.</b> This manages exactly what data the model is authorized to see based on the identity of the user asking the question.</li> <li><b>Resource and cost management.</b> This centers on monitoring the compute effectiveness of your silicon to prevent surprise overages and optimize token spend.</li> </ul> <p>Now, if you read those bullets and think there are a bunch of personas involved here, you're not wrong, because this could be IT ops, SecOps, DevOps or DataOps. The lines between these departments are blurring as the enterprise moves toward a <a href="https://www.techtarget.com/searchenterpriseai/tip/Simplify-enterprise-AI-integration-with-a-centralized-AI-hub">centralized AI control plane</a>. Successful organizations are moving away from siloed responsibilities and toward integrated operations teams that can manage the intersection of infrastructure, security and data flow simultaneously.</p> </section> <section class="section main-article-chapter" data-menu-title="The path forward for enterprise AI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The path forward for enterprise AI</h2> <blockquote class="main-article-pullquote"> <div class="main-article-pullquote-inner"> <figure> The real win for most organizations comes from recognizing that the ideal model for a task will likely change over time. </figure> <i class="icon" data-icon="z"></i> </div> </blockquote> <p>Building a model is a high-stakes engineering feat that requires specialized talent and millions upon millions of dollars in compute to manage the iterative rounds of training and hyperparameter tuning needed for convergence. The leading companies building these foundation models are doing excellent work. The real win for most organizations comes from recognizing that the ideal model for a task will likely change over time.</p> <p>Success belongs to the companies that prioritize agility and focus on the plumbing required to implement these models effectively. By working with the right partners to build robust security layers, data pipelines and AI observability, organizations can ensure that whatever model they select works at scale without crashing. This operational focus is what ultimately keeps the attention of employees, as the technology moves past the hype and starts helping them save time and money.</p> <p><i>Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.</i></p> </section> AI models are now commodities. It's enterprise challenges like security, scalability and integration are the real barriers to successful deployment. https://cdn.ttgtmedia.com/visuals/digdeeper/5.jpg https://www.techtarget.com/searchenterpriseai/opinion/The-operational-reality-of-enterprise-AI Mon, 02 Feb 2026 04:30:00 GMT <p>Data has become the single most important resource for modern business. It's created by sensors, product functions and many customer actions. It's analyzed extensively, forming the foundation of real-time business decisions. It can teach machine learning (ML) models to help AI systems learn and adapt to changing situations. Data can even be monetized, yielding other revenue streams for enterprises that understand the value of data.</p> <p>But data also presents vulnerabilities for business. Oversight and security are needed to restrict access, implement data sovereignty and ensure that sensitive or personally identifiable information (<a href="https://www.techtarget.com/searchsecurity/definition/personally-identifiable-information-PII">PII</a>) contained within is protected. <a href="https://www.techtarget.com/searchenterpriseai/feature/9-data-quality-issues-that-can-sideline-AI-projects">Data can have quality issues</a> that result in it being biased, limited, incomplete or inconsistent. It also can be costly to collect, especially when data sets are small or specific to an industry or use case.</p> <p><a href="https://www.techtarget.com/searchcio/definition/synthetic-data">Synthetic data</a> is one way organizations are overcoming some of these data challenges. Synthetic data isn't real data. Algorithms and generative AI (<a href="https://www.techtarget.com/searchenterpriseai/definition/generative-AI">GenAI</a>) are used to create data sets that reflect the characteristics and patterns found in real data, but they contain no actual sensitive data or PII. Given this, synthetic data can replace real data, addressing the cost, localization, security, privacy and quality concerns that surround real data.</p> <p>Because of these advantages, synthetic data use is on the upswing. In its "What generative AI means for business" <a target="_blank" href="https://www.gartner.com/en/insights/generative-ai-for-business" rel="noopener">report</a>, Gartner predicted that 75% of businesses will use GenAI to create synthetic customer data by 2026. This is an increase from less than 5% in 2023.</p> <section class="section main-article-chapter" data-menu-title="How synthetic data works"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How synthetic data works</h2> <p>At its most basic, synthetic data is artificially generated data that mimics real-world data. It can be created algorithmically, or <a href="https://www.techtarget.com/searchenterpriseai/tip/How-and-why-to-create-synthetic-data-with-generative-AI">by first training a GenAI</a> or other platform with real data on which the artificial data can be based. Although synthetic data is artificial, it retains the statistical characteristics of the real data used as its foundation, but it doesn't possess any of the sensitive or personal information found in real data. And that's what makes synthetic data useful and appealing: It provides business value while mitigating much of the associated risk in using real data.</p> <p>Consider this example: A financial company is developing software to underwrite mortgages, but the <a href="https://techtarget.com/searchenterpriseai/tip/Explore-the-role-of-training-data-in-AI-and-machine-learning">application needs to be trained and tested extensively</a> to validate its capabilities. The company could use data from real mortgage applicants, but this carries costs and data privacy, security and quality risks. It might not even be possible to obtain an adequate data set at a reasonable cost. By using synthetic data, the company can cost-effectively acquire the needed data without the issues and obligations of real data.</p> <p>In these situations, businesses can use one of the following three approaches to synthetic data:</p> <ul class="default-list"> <li><b>Full data synthesis.</b> As the name implies, this approach doesn't contain any real-world data. The entire data set is generated using patterns and relationships that produce an emulated data set.</li> <li><b>Partial data synthesis. </b>This approach is a <a href="https://www.techtarget.com/searchbusinessanalytics/tip/Synthetic-data-vs-real-data-for-predictive-analytics">mix of real and synthetic data</a>. Some elements of real-world data are retained, but sensitive data elements -- such as PII -- are replaced with synthetic data or additional data elements can be added. The ability to add data elements is useful when data sets must be augmented to include data that hasn't been collected from the real-world.</li> <li><b>Hybrid data synthesis.</b> Here, a data set mixes real and synthetic records. This approach expands a data set and can be useful when real-world data sets are too small or limited in scope to be useful alone.</li> </ul> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/vH1LzDY_t5g?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="How synthetic data is created"> <h2 class="section-title"><i class="icon" data-icon="1"></i>How synthetic data is created</h2> <p>There are numerous techniques available to create synthetic data. The following are some common methods:</p> <ul class="default-list"> <li><b>Statistical algorithms.</b> When data uses well-understood distributions or correlations, it's a simple matter to use statistical algorithms to create synthetic data. For example, <a href="https://www.techtarget.com/whatis/definition/extrapolation-and-interpolation">interpolation</a> can determine new data points between existing ones, while extrapolation can create new data points beyond existing ones.</li> <li><b>AI agents.</b> Increasingly used to model complex systems, <a href="https://www.techtarget.com/searchenterpriseai/definition/AI-agents">AI agents</a> interact with one another in ways similar to real-world behaviors. When built and tuned properly, AI agents can generate data that closely resembles real-world data. This approach is a powerful research tool. For example, infectious disease scientists can use AI agents to synthesize data that resembles the spread of a disease and examine the roles of possible vaccination or other interventions.</li> <li><b>Generative adversarial networks.</b> <a href="https://www.techtarget.com/searchenterpriseai/definition/generative-adversarial-network-GAN">GANs</a> use two networks that compete to produce or modify data in ways that make it impossible to differentiate between real and synthetic data. For example, a generator network creates synthetic data, and a discriminator is the adversary that tells real versus synthetic data. The discriminator feeds back to the generator, creating a loop that can build data that resembles real data.</li> <li><b>Transformers.</b> These models are the basis of small and large language models used in AI and GenAI. <a href="https://www.techtarget.com/searchenterpriseai/definition/transformer-model">Transformer models</a> use encoders and decoders to break down and manipulate data in ways that capture the semantic meaning of data and then generate the most statistically likely outcome. This works for understanding the meaning and context of language, and it can be applied to generating artificial text and other types of synthetic data.</li> <li><b>Variational autoencoders. </b><a href="https://www.techtarget.com/searchenterpriseai/definition/variational-autoencoder-VAE">VAEs</a> are GenAI models intended to produce variations of data used to train the model. Similar to transformers, VAEs use encoders to compress data into less space while capturing meaningful information. Decoders are used to construct new data from the encoded foundation data.</li> </ul> <p>Various tools and platforms exist to produce synthetic data. The following is a sampling of tools:</p> <ul class="default-list"> <li>The Mostly AI platform is used in finance and insurance.</li> <li>Nvidia's Gretel.ai offers APIs to generate, classify and transform synthetic data.</li> <li>Synthea generates synthetic healthcare records.</li> <li>Tonic is used for realistic test data.</li> <li>YData is used for high-quality synthetic data sets.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Uses of synthetic data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Uses of synthetic data</h2> <p>Synthetic data has a range of potential uses across industry verticals, such as the following examples.</p> <h3>Machine learning and AI training</h3> <p>ML models and AI systems depend on enormous amounts of high-quality data for training. Synthetic data can help fill the demand for this high-quality training data, especially in cases where the data is limited, proprietary or costly to obtain. For example, synthetic data is useful for the simulation of rare events -- such as a network attack or fraudulent financial behavior -- where real-world data is particularly limited.</p> <p>Organizations that use synthetic data for ML and AI can <a href="https://www.techtarget.com/searchenterpriseai/feature/How-data-quality-shapes-machine-learning-and-AI-outcomes">develop models and systems that perform better</a> and behave more predictably in unusual cases than organizations that depend solely on real-world data.</p> <h3>Software development</h3> <p>Testing is a central part of the software development lifecycle, and this demands test data that ensures a build's functionality across a range of input conditions. <a href="https://www.techtarget.com/searchsoftwarequality/tip/Guide-to-synthetic-test-data">Synthesized data can meet these demands</a>, creating test data for varies situations while safeguarding real data.</p> <p>Businesses that use synthetic data for software development typically realize better, faster and more consistent testing regimens than those that use manual test scenarios or only real-world data. Broader and more comprehensive synthetic data can help expose more software defects earlier, resulting in a more reliable and trustworthy software release.</p> <h3>Data security</h3> <p>As <a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">regulatory and sovereignty demands expand globally</a> and across industries, the need to safeguard data privacy and security poses increasing risks for businesses. Organizations are less likely to share real-world data for fear of compliance issues, breaches and corresponding business consequences. Synthesized data can generate data that's partially or completely synthetic, enabling simulations and data use while mitigating business risks.</p> <p>Organizations that integrate synthetic data into their data privacy and security environment are more resilient to data breaches, lowering the chances of litigation, fines and sanctions because no PII need be present in the synthetic data. However, synthetic data can still have business value and should be subject to reasonable security methodologies.</p> <h3>Data augmentation and bias mitigation</h3> <p>Synthetic data can be generated to expand existing data or create entirely new data sets that support more diversity and additional data parameters. This enhances the performance, fairness and accuracy of AI systems.</p> <p>Businesses that <a href="https://www.techtarget.com/searchdatamanagement/tip/How-GenAI-created-synthetic-data-improves-augmentation">use synthetic data for augmentation</a> can realize faster data preparation, making the augmented data ready for use faster than manually adding or recapturing additional data. Bias mitigation can ensure acceptance of the AI when bias reduction is required by law. Similarly, bias mitigation is emerging as an AI business differentiator, and synthetic data can result in better user or customer outcomes, building a meaningful AI reputation and brand.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/when_to_use_synthetic_data-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/when_to_use_synthetic_data-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/when_to_use_synthetic_data-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/when_to_use_synthetic_data-f.png 1280w" alt="Table showing four synthetic data use cases" height="347" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Four synthetic data use cases are based on data accessibility and representativeness: edge cases, model validation, data scarcity, and privacy and security. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Monetization of synthetic data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Monetization of synthetic data</h2> <p><a href="https://www.techtarget.com/searchcio/definition/data-monetization">Data monetization</a> is the practice of translating business data into measurable business value. While real-world data is increasingly monetized, the emergence of quality synthetic data also provides monetization opportunities. The financial appeal of synthetic data arises from the following three areas:</p> <ul class="default-list"> <li><b>Synthetic data privacy.</b> Real data frequently contains PII and other sensitive business data that carries the risk of data misuse, leading to regulatory violations and penalties. Real data could be anonymized before it's monetized, but this requires additional processing and costs. Synthetic data is anonymous by default and eliminates the risks of managing sensitive or personal data.</li> <li><b>Synthetic data availability.</b> Synthetic data can be generated quickly and in vast quantities, often on demand. At the same time, it can effectively address bias or other data quality issues. This makes synthetic data appealing when real data is scarce, of limited quality, or when rapid training or prototyping is required.</li> <li><b>Synthetic data cost.</b> Real-world data can be expensive and time consuming to collect, process and anonymize. This makes real data highly proprietary and valuable, potentially making the data too important to monetize directly. Synthetic data can be created quickly and relatively inexpensively. This can yield data that provides value but is far more affordable for potential buyers.</li> </ul> <p>Synthetic data can be monetized to yield direct and indirect business benefits, such as the following:</p> <ul class="default-list"> <li><b>Direct monetization.</b> The selling or licensing of synthetic data to academic researchers, AI startups and any business that can't readily generate time- and cost-effective data internally are examples of direct monetization. Synthetic data can be pre-existing -- already generated to serve the original enterprise -- or it can sometimes be generated on demand in response to buyer queries.</li> <li><b>Training and consulting.</b> Organizations that refine their mastery of synthetic data might be able to couple direct monetization with additional revenue-generating services such as synthetic data training and consulting. This lets the organization bolster sales and licensing of its synthetic data with practical services that can aid other businesses in selecting, using and generating synthetic data.</li> <li><b>Indirect monetization.</b> Using synthetic data to enhance or improve internal operations is a form of indirect monetization. For example, synthetic data that represents potential fraudulent financial transactions can be used to train AI platforms for superior <a href="https://www.techtarget.com/searchsecurity/definition/fraud-detection">fraud detection</a> capabilities. Similarly, synthetic data that reflects wear and tear on vehicles can enhance AI training for <a href="https://www.techtarget.com/searcherp/feature/Predictive-maintenance-Definition-benefits-example-strategy">predictive maintenance</a> across logistics fleets. The business value here isn't direct revenue, but rather value generated through cost savings and product enhancements.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Benefits and challenges of synthetic data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Benefits and challenges of synthetic data</h2> <p>Any enterprise considering a role for synthetic data must consider the tradeoffs involved. This includes the following benefits of synthetic data:</p> <ul class="default-list"> <li><b>Data privacy.</b> With no PII, synthetic data eliminates the risks associated with data breaches and data sovereignty.</li> <li><b>Data volume.</b> Synthetic data can be produced in enormous quantities -- often on demand.</li> <li><b>Data annotation.</b> Synthetic data can be generated with desired annotations, such as <a href="https://www.techtarget.com/whatis/definition/data-labeling">tagging and labeling</a>, eliminating the need for manual tagging.</li> <li><b>Bias reduction and model performance.</b> Synthetic data can provide a broader scope of data which is more representative and inclusive, resulting in fairer data and outcomes. <a href="https://www.techtarget.com/searchenterpriseai/feature/How-to-avoid-overfitting-in-machine-learning-models">Broader data conditions can also reduce overfitting</a> and enhance overall model performance.</li> <li><b>Unique or rare data.</b> Synthetic data can represent rare or potentially dangerous conditions, such as a cyberattack, that are difficult to capture in the real world.</li> <li><b>Cost.</b> Generating synthetic data can be cheaper and faster than collecting real data. This can vastly accelerate prototyping and AI training.</li> </ul> <p>Despite the benefits, however, there are numerous challenges with synthetic data that business leaders must understand and address, including the following:</p> <ul class="default-list"> <li><b>Poor data quality.</b> Synthetic data must follow real data patterns. Less accurate models result when it doesn't. Similarly, synthetic data that fails to capture the nuances typically found in real data can limit the model's ability to learn.</li> <li><b>Validation.</b> Simply generating synthetic data is no guarantee of its usefulness or reliability. Organizations must be able to <a href="https://www.techtarget.com/searchdatamanagement/definition/data-validation">validate</a> that the synthetic data offers a worthwhile replacement to real data.</li> <li><b>Bias amplification.</b> Synthetic data can reduce bias, but when generated improperly, it can worsen or <a href="https://www.techtarget.com/searchhrsoftware/tip/AI-hiring-bias-Everything-you-need-to-know">amplify bias</a>. Synthesized data should be inspected and evaluated carefully for bias, even when all efforts are made to reduce bias.</li> <li><b>Generation complexity.</b> Synthetic data doesn't just appear; it must be deliberately generated using complex methods that use a wealth of real data as the foundation. The sophistication required to create meaningful synthetic data can be difficult and costly to implement.</li> <li><b>Use case limitations.</b> Synthetic data can be broadly applicable but might not be suitable for every use case, such as mission-critical or regulatory-dependent production systems. Consider whether synthetic data is appropriate for each use case and determine whether partial or hybrid data synthesis might yield better outcomes.</li> </ul> </section> <section class="section main-article-chapter" data-menu-title="Responsible governance is the future of synthetic data"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Responsible governance is the future of synthetic data</h2> <p>The role of synthetic data is expanding rapidly. It's no longer an experimental tool but is becoming a foundation for innovation. Companies like Waymo, an autonomous vehicle technology developer, can simulate entire urban areas for vehicle testing. <a href="https://www.techtarget.com/healthtechanalytics/feature/High-value-use-cases-for-synthetic-data-in-healthcare">Healthcare organizations can test treatments or track health concerns</a> at enormous scale without risking patients' medical records. These practical uses will drive the evolution and use of synthetic data into the future.</p> <p>However, the production and use of synthetic data will need to address societal consequences arising from the authenticity and trustworthiness of the data. For instance, what happens when people no longer believe what they see, hear and read? These issues will demand <a href="https://www.techtarget.com/searchenterpriseai/tip/AI-governance-can-make-or-break-data-monetization">attention to governance</a> and transparency, encouraging lawmakers and business leaders to foster proper use of synthetic data while enabling businesses and people to readily distinguish between real and synthetic data.</p> <p><i>Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.</i></p> </section> Synthetic data mimics real data without sensitive information. It offers cost-effective options and competitive advantages for AI training, software testing and data monetization. https://cdn.ttgtmedia.com/rms/onlineimages/ai_g1183318665.jpg https://www.techtarget.com/searchenterpriseai/tip/What-to-know-about-synthetic-data-as-a-business-advantage Sat, 31 Jan 2026 15:30:00 GMT <p>The AI hardware market is evolving rapidly, with companies pushing the boundaries of performance, efficiency and innovation. As the industry grows, these advancements will shape the future of AI applications across various sectors.</p> <p>The following 10 companies are competing to create the most powerful and <a href="https://www.computerweekly.com/news/366559452/Chip-sector-gears-up-for-AI-revolution">efficient AI chip on the market</a>.</p> <section class="section main-article-chapter" data-menu-title="10 top companies in the AI hardware market"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10 top companies in the AI hardware market</h2> <p>The following AI hardware and chip-making companies are listed in alphabetical order.</p> <h3>Alphabet</h3> <p>Alphabet, Google's parent company, offers various products for mobile devices, data storage and cloud infrastructure.</p> <p>Alphabet has focused on producing powerful AI chips to meet the demand for large-scale projects. In December 2024, Alphabet released a new quantum computing chip, Willow. With 105 qubits and the ability to scale up, the <a target="_blank" href="https://blog.google/technology/research/google-willow-quantum-chip/" rel="noopener">Willow chip</a> reduces error in quantum computing faster and more accurately than its predecessors.</p> <p><a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/ironwood-tpu-age-of-inference/">Ironwood TPU</a> is the company's newest product, released in November 2025, designed to support the new age of inference. It scales up to 9,216 chips per pod, making it 24 times more powerful than El Capitan, the world's largest supercomputer.</p> <h3>AMD</h3> <p>AMD is expanding its AI hardware portfolio with new processors and GPUs.</p> <p>AMD released its latest CPU microarchitecture chip design, Zen 5, in January 2025. In January 2026, AMD released its next generation of <a href="https://www.amd.com/en/newsroom/press-releases/2026-1-5-amd-introduces-ryzen-ai-embedded-processor-portfol.html">Ryzen processors</a>, the Ryzen AI Embedded P100 and X100 Series. The P100 Series processors are designed for human-machine interface and industrial automation, featuring four to six CPU cores -- eight to twelve cores planned for later in 2026. The X100 Series scales up to 16 CPU cores for high-performance, compute-intensive tasks, such as advanced autonomous systems and robotics.</p> <p>AMD's <a href="https://www.computerweekly.com/news/366615894/AMD-pushes-GPU-advantage-with-HPC-top-spot">Instinct MI300 Series chip</a>, MI325X, was released in 2024. This upgrade from MI300X has a larger bandwidth <a name="_Hlk202279956"></a>of 6 TBps. The MI350 series, including the MI355X chip, was released in June 2025. The MI355X chip is 4 times faster than the MI300X. These AI GPU accelerators are meant to rival Nvidia's Blackwell B100 and B200.</p> <h3>Apple</h3> <p>Apple Neural Engine, specialized cores based on Apple chips, has furthered the company's AI hardware design and performance. <a href="https://www.techtarget.com/searchmobilecomputing/news/252514399/Apples-M1-Ultra-delivers-more-power-for-creative-pros">Neural Engine led to the M1 chip</a> for MacBooks. Compared to the generation before, MacBooks with an M1 chip are 3.5 times faster in general performance and five times faster in graphics performance.</p> <p>After the success of the M1 chip, Apple announced further generations. As of 2025, Apple has released the <a href="https://www.apple.com/newsroom/2025/10/apple-unleashes-m5-the-next-big-leap-in-ai-performance-for-apple-silicon/">M5 chip</a>. This chip has a 10-core GPU with Neural Accelerators in each core, delivering over 4x the AI performance of the M4 chip.</p> <p>Apple and Broadcom are developing an AI-specific server chip, Baltra. This chip is expected to be released in 2026, but it will only be used internally by the companies to handle inference tasks.</p> <h3>AWS</h3> <p>AWS is focusing on AI chips for cloud infrastructure. Its Elastic Compute Cloud (<a href="https://www.techtarget.com/searchaws/definition/Amazon-Elastic-Compute-Cloud-Amazon-EC2">EC2</a>) Trn3 instances are purpose-built for running AI training and inference workloads. They use AWS Trainium AI accelerator chips to function.</p> <p>The <a href="https://www.aboutamazon.com/news/aws/trainium-3-ultraserver-faster-ai-training-lower-cost">Trn3 UltraServer</a>, released in December 2025, has 144 Trainium3 chips and performs over four times better than Trainium2 UltraServers. The Trainium3 is also 40% more energy-efficient than previous generations.</p> <p>In 2024, AWS released <a href="https://www.techtarget.com/searchenterpriseai/news/366561339/AWS-unveils-new-AI-chatbot-chips-Nvidia-partnership">Graviton4</a>, a 96-core ARM-based processor ideal for a range of cloud workloads, such as databases, web servers and high-performance computing. The fourth generation of <a href="https://www.techtarget.com/searchcloudcomputing/tip/Break-down-the-different-AWS-Graviton2-instance-types">AWS's Graviton processor</a>, which powers EC2 R8g instances, delivers up to 30% better performance and offers three times the vCPUs and memory of Graviton3.</p> <h3>Cerebras Systems</h3> <p>Cerebras is making a name for itself with the release of its third-generation <a href="https://www.techtarget.com/searchenterpriseai/news/366573575/Cerebras-introduces-next-gen-AI-chip-for-GenAI-training">wafer-scale engine</a>, WSE-3. WSE-3 is deemed the fastest processor on Earth with 900,000 AI cores on one unit. Every core has access to 21 petabytes per second of memory bandwidth.</p> <p>Compared to Nvidia's H100 chip, WSE-3 has 7,000 times larger bandwidth, 880 times more on-chip memory and 52 times more cores. This WSE-3 chip is also 57 times larger in area, so more space is necessary to house the chip in a server.</p> <h3>IBM</h3> <p><a href="https://www.computerweekly.com/news/252505661/IBM-unveils-Telum-to-combat-financial-fraud-in-real-time">Telum</a> was IBM's first specialized AI chip, and <a href="https://www.ibm.com/new/announcements/telum-ii">Telum II</a> was released in late 2025. IBM has also set out to design a powerful successor to rival its competitors.</p> <p>In 2022, IBM created the Artificial Intelligence Unit. The AI chip is purpose-built and runs better than the average general-purpose CPU. Based on a similar architecture, IBM released the Spyre Accelerator in 2025. Spyre has 32 AI accelerator cores and contains 25.6 billion transistors over 14 miles of wire. The Spyre Processor enables on-premises, low-latency inferencing for tasks like real-time fraud detection, intelligent IT assistants, code generation and risk assessments.</p> <p>IBM is working on the NorthPole AI chip, which does not have a public release date. NorthPole differs from IBM's TrueNorth chip. The NorthPole architecture is structured to improve energy use, decrease the amount of space the chip takes up and provide lower latency. The NorthPole chip is set to mark a new era of energy-efficient chips.</p> <h3>Intel</h3> <p>Intel has made a name for itself in the AI hardware market with its AI processors and GPUs.</p> <p><a href="https://www.techtarget.com/searchenterpriseai/news/366587618/Intel-launches-Xeon-6-for-AI-data-centers">Xeon 6 processors</a> launched in 2024 and have been shipped to data centers. These processors offer up to 288 cores per socket, enabling faster processing time and enhancing the ability to perform multiple tasks at once.</p> <p>Intel has released the <a href="https://www.techtarget.com/searchenterpriseai/news/366580394/How-Intels-new-AI-Gaudi-3-chip-compares-to-Nvidias">Gaudi 3 GPU chip</a>, which competes with Nvidia's H100 GPU chip. The Gaudi 3 chip trains models 1.5 times faster, outputs results 1.5 times faster, and uses less power than Nvidia's H100 chip. The Jaguar Shores GPU chip, the successor to the Gaudi 3 chips, is still set to launch in 2026. This chip will focus on energy efficiency.</p> <p>In late 2024, Intel released the <a href="https://www.intel.com/content/www/us/en/support/articles/000099574/processors/intel-core-ultra-processors.html">Core Ultra AI Series 2</a> processors. The release included multiple processors under the Core Ultra 200 series, including 200H, 200HX, 200S and 200V. Each series focuses on specific features, such as enhanced security, AI capabilities, performance and energy efficiency. The Core Ultra 200 processor series is designed for desktop and mobile platforms, creating <a href="https://www.techtarget.com/whatis/definition/AI-PC">AI PCs</a>.</p> <h3>Nvidia</h3> <p>Nvidia became a strong competitor in the AI hardware market when its valuation surpassed $1 trillion in early 2023. The company's current work includes its B300 chip, Blackwell GPU microarchitecture and <a href="https://www.techtarget.com/searchenterpriseai/news/366621003/Nvidia-readies-Vera-Rubin-to-replace-Blackwell">Vera Rubin</a>. Nvidia also offers AI-powered hardware for the gaming sector.</p> <p>The <a target="_blank" href="https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing" rel="noopener">Blackwell GPU microarchitecture</a> is replacing the Grace Hopper platform. Blackwell is 2.5 times faster and 25 times more energy-efficient than its predecessors. The Blackwell microarchitecture is designed to increase efficiency with scientific computing, quantum computing, AI and data analytics. The B300 chip series, or Blackwell Ultra, was released in the second half of 2025.</p> <p>Vera Rubin is Nvidia's next-generation GPU superchip architecture, expected to be released in late 2026. It combines the Vera CPU with the Rubin GPU, the successor to Blackwell.&nbsp;</p> <h3>Qualcomm</h3> <p>Although Qualcomm is relatively new in the AI hardware market compared to its counterparts, its experience in the telecom and mobile sectors makes it a promising competitor.</p> <p>Qualcomm's Cloud AI 100 chip beat Nvidia H100 in a series of tests. One test was to see the number of data center server queries each chip could carry out per watt. Qualcomm's Cloud AI 100 chip totaled 227 server queries per watt, while Nvidia H100 hit 108. The Cloud AI 100 chip also managed to net 3.8 queries per watt compared to Nvidia H100's 2.4 queries during object detection.</p> <p>In 2024, Qualcomm released <a href="https://www.computerweekly.com/news/366599273/Qualcomm-unveils-new-Snapdragon-mobile-platform">Snapdragon 8s Gen 3</a>, a mobile chip that supports 30 AI models and has generative AI features, like image generation and voice assistants. Later in the year, the company released the newest version, <a href="https://www.qualcomm.com/products/mobile/snapdragon/smartphones/snapdragon-8-series-mobile-platforms/snapdragon-8-elite-mobile-platform">Snapdragon 8 Elite</a>, which improved AI performance by 45%. The Snapdragon 8 Elite Gen 2 was released in late 2025 and offers 30% more CPU power than the first generation.</p> <h3>Tenstorrent</h3> <p>Tenstorrent builds computers for AI and is led by the same man who designed AMD's Zen chip architecture, Jim Keller. Tenstorrent offers multiple hardware products, including its Wormhole processors and Galaxy servers, which together form the <a target="_blank" href="https://tenstorrent.com/hardware/galaxy" rel="noopener">Galaxy Wormhole Server</a>.</p> <p>Tenstorrent released the <a target="_blank" href="https://tenstorrent.com/en/hardware/blackhole" rel="noopener">Blackhole</a> series, an AI accelerator, in April 2025. It has 16 RISC-V cores and 32 GB of GDDR6 memory per chip. The p100a chip has 120 Tensor Cores and 28 GB of GDDR6. The p150a has 140 Tensor Cores and 32 GB of GDDR6. Both chips operate at up to 300 Watts.</p> <p>Wormhole n150 and n300 are Tenstorrent's scalable GPUs. N300 nearly doubles every spec of n150. These chips are for network AI and are put into Galaxy modules and servers. Each server holds up to 32 Wormhole processors, 2,560 cores and 384 GB of GDDR6 memory.</p> <p><em>Kelly Richardson is site editor for Informa TechTarget's SearchDataCenter site.</em></p> <p><em>Devin Partida is editor in chief of ReHack.com and a freelance writer. She has knowledge of niches such as biztech, medtech, fintech, IoT and cybersecurity.</em></p> </section> Due to rapid AI hardware advancement, companies release advanced products yearly to keep up with the competition. The new competitive product on the market is the AI chip. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a194810146.jpg https://www.techtarget.com/searchdatacenter/tip/Top-AI-hardware-companies Fri, 30 Jan 2026 14:00:00 GMT <p>The right talent management software can streamline tasks and save time. But as the talent management software market grows and AI continues its dominance, buying teams need to get savvier about which products should make their shortlist. They also need to understand some of the pros and cons of the leading vendors and systems before buying a new one or replacing their legacy software.</p> <p>Before getting into specifics on the product choices, it's important to have a clear idea of what talent management is and how it gets automated in software.</p> <section class="section main-article-chapter" data-menu-title="What is talent management?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What is talent management?</h2> <p>Talent management spans an employee's entire lifecycle at a company and includes all the processes, guidelines, policies, tools and systems needed to attract, retain, develop, engage, reward and manage employees. It's typically carried out by the HR department with support from managers and company leaders.</p> <p>Talent management is intended to meet the human capital needs of the enterprise by hiring people with the right skill sets, <a href="https://www.techtarget.com/searchhrsoftware/definition/employee-training-and-development">training</a> them, evaluating their performance and compensating them appropriately.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineImages/position-onboarding.jpg"> <img data-src="https://www.techtarget.com/rms/onlineImages/position-onboarding_mobile.jpg" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineImages/position-onboarding_mobile.jpg 960w,https://www.techtarget.com/rms/onlineImages/position-onboarding.jpg 1280w" alt="graphic of the hiring and onboarding process" height="290" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Onboarding is a critical HR function because it affects several aspects of talent management, including performance management and the employee experience. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="What is talent management software?"> <h2 class="section-title"><i class="icon" data-icon="1"></i>What is talent management software?</h2> <p>The need to attract, reward and engage employees is critical -- but it isn't easy. Talent management software can help automate and simplify those processes.</p> <p><i>Talent management software</i> refers to the systems and applications that digitize the major stages and business processes of the employee lifecycle.</p> <p>Many companies use several HR systems for talent management. For example, they might use a <a href="https://www.techtarget.com/searchhrsoftware/definition/core-HR-core-human-resources">core HR</a> system to track basic employee data, another system for performance management and a third for learning management. However, there are software suites that provide most, if not all, of the talent management functions in one system with a consistent look and feel. Talent management software often includes self-service options for employees and managers that enable them to look up information and make changes based on their permissions without requiring HR's assistance.</p> </section> <section class="section main-article-chapter" data-menu-title="Key talent management software features"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Key talent management software features</h2> <p>Talent management software features are usually categorized by modules, each of which handles a specific function. For example, a compensation module helps manage pay-related functions like raises and bonuses.</p> <p>Another key software feature, AI, continues to grow in importance across the entire talent management system. Vendors are introducing <a href="https://www.techtarget.com/searchhrsoftware/tip/Top-AI-recruiting-tools-and-software-of-2022">new AI features</a> that can simplify and automate some of the most important tasks. The initial push for AI was for use in recruiting, but it's gaining momentum in many other areas of talent management as well, such as performance management, learning and data analytics.</p> <p>The current emphasis on skills-based hiring and employee evaluation could lead to vendors also incorporating skills tracking, development and reporting across their platform.</p> <p>Here's an overview of each talent management system module. Standard functions are often similar across vendors.</p> <p><b>Core HR</b>, the administrative foundation of talent management, stores information about every employee in one database and provides comprehensive reports and dashboards on the data. It also typically offers the employee and <a href="https://www.techtarget.com/searchhrsoftware/definition/manager-self-service">manager self-service</a> options. Some vendors include benefit administration in core HR.</p> <p><b>Employee engagement</b> provides employee surveys and tools for comparing employee satisfaction to that of peer organizations. It can include mechanisms for employees to recognize each other's achievements and receive awards. Some systems also incorporate employee communications into this module.</p> <p><b>Recruiting</b> includes a portal for candidates to apply for open positions and integrates with popular job boards and the core HR module. It also helps identify candidates already in the database who match new job postings.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/hrsoftware-recruitment.png 1280w" alt="graphic of major steps in recruitment" height="353" width="560"> <figcaption> <i class="icon pictures" data-icon="z"></i>Most talent management products include a recruitment module, and the best ones can handle all the major steps of recruiting. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> <p><b>Onboarding</b> enables new hires to complete employment forms and digitally sign documents. It also gives new hires access to onboarding before and after their start date, and it enables HR staff to share additional information about the company with new hires, such as org charts and welcome videos.</p> <p><b>Performance management</b> provides employees and managers with a variety of ways to discuss performance and career plans, including one-on-ones and formal reviews. It also enables users to apply performance data to other functions, such as compensation management and <a href="https://www.techtarget.com/searchhrsoftware/definition/succession-planning">succession planning</a>.</p> <p><b>Compensation</b> helps managers determine salary increases, bonuses, stock options and grants. It incorporates performance data and external data, such as salary rates, for managers to refer to when deciding on salary increases.</p> <p><b>Reporting and analytics</b> combines data from different modules into one report if the system is all-in-one. It also provides the ability to create custom dashboards or use default dashboards, as well as configure reports and dashboards to automatically send to certain users. Companies using multiple systems can acquire a third-party data analytics package that amalgamates data into one system for more comprehensive analysis.</p> <p><b>Learning and development</b> enables users to establish a course curriculum, add content such as articles and videos, and incorporate content from third-party vendors. Tests and quizzes for students might also be available.</p> <p><b>Succession planning</b> helps identify employees who possess the skills and competencies for other positions, in part by incorporating performance-related data, and provides employees with career-planning tools.</p> <p><b>Document management</b> facilitates the sharing of corporate documents with employees and controls permissions for certain documents. This module usually includes capabilities for storing employee and e-signing documents.</p> <p><b>Payroll</b> is sometimes included in all-in-one systems, since HR and payroll work closely together. Also, payroll processes rely on HR data from recruiting, compensation planning and benefits administration.</p> <figure class="main-article-image full-col" data-img-fullsize="https://www.techtarget.com/rms/onlineimages/how_succession_planning_works-f.png"> <img data-src="https://www.techtarget.com/rms/onlineimages/how_succession_planning_works-f_mobile.png" class="lazy" data-srcset="https://www.techtarget.com/rms/onlineimages/how_succession_planning_works-f_mobile.png 960w,https://www.techtarget.com/rms/onlineimages/how_succession_planning_works-f.png 1280w" alt="graphic of steps in succession planning" height="501" width="559"> <figcaption> <i class="icon pictures" data-icon="z"></i>Succession has become increasingly important, but many talent management products don't have a dedicated module for it. </figcaption> <div class="main-article-image-enlarge"> <i class="icon" data-icon="w"></i> </div> </figure> </section> <section class="section main-article-chapter" data-menu-title="Advice for buying teams"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Advice for buying teams</h2> <p>The most critical aspects of a vendor evaluation are deciding whether the product meets the organization's requirements and identifying the features that make the product stand out from its competitors. Buying teams should also consider implementation and licensing costs.</p> <p>When evaluating an all-in-one talent management system, the buying team should consider whether the platform enables users to work with data across different modules. For example, skills data might be valuable in various situations, such as rating employees in performance management, seeking internal candidates to fill open positions and auto-assigning development training to employees.</p> <p>When evaluating vendors that are more specialized, the buying team should consider products' integration capabilities, since users will likely find it valuable to see related data in multiple systems. For example, it might be helpful to see performance management data in the compensation planning tool and then transfer employee compensation changes to the company's core HR module.</p> </section> <section class="section main-article-chapter" data-menu-title="10 of the top talent management software systems"> <h2 class="section-title"><i class="icon" data-icon="1"></i>10 of the top talent management software systems</h2> <p>A plethora of talent management software products are available and can benefit companies in various ways. These 10, shown alphabetically, are among the top software products based on sources such as G2, Capterra, Gartner, Forbes, Software Advice and vendor websites, as well as the author's personal experience. The list includes both all-in-one talent management suites and standalone applications that focus on one aspect of talent management, such as compensation planning.</p> <h3>360Learning</h3> <p>360Learning is a collaborative learning platform that employees can use to share their expertise with others. Features include a learning management system <a href="https://www.techtarget.com/searchcio/definition/learning-management-system">(LMS</a>), a learning experience platform (LXP), tools focused on skill assessments and development, and the ability to create unique communities called Academies. The platform also includes AI tools that can help in situations such as course creation and content searching.</p> <h4>Pros</h4> <ul class="default-list"> <li>The platform combines an LMS and LXP in one system.</li> <li>The software integrates with many other tools to provide learning opportunities in the systems that employees already use on a daily basis, such as sales software.</li> <li>360Learning enables any user to create content and share knowledge.</li> <li>It includes controls for employee sharing.</li> <li>It also includes AI capabilities for use cases like course creation and course translation.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>Advanced reporting and dashboards could be improved.</li> <li>The number of features and configuration options can make it difficult for new users to learn the system.</li> <li>Branding and page layout customization opportunities are limited.</li> <li>The price might be higher than what smaller organizations can afford.</li> </ul> <h3>ADP</h3> <p>ADP focuses on payroll but also offers a full suite of modules that covers the employee lifecycle. The company offers different applications for organizations of different sizes and geographies. For example, RUN Powered by ADP might be a good fit for a small company, while bigger companies would likely use ADP Workforce Now. The pros and cons section below focuses on ADP Workforce Now.</p> <h4>Pros</h4> <ul class="default-list"> <li>ADP is an experienced company with thousands of customers.</li> <li>The system supports the entire employee lifecycle.</li> <li>The system offers API and Secure FTP integrations.</li> <li>Some particular areas of success for ADP include payroll, benefits and related customer support.</li> <li>Companies can outsource payroll to ADP.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>The system is primarily designed for companies working in one country, with the exception of the North American version, which supports American and Canadian employees.</li> <li>Getting a staging or sandbox environment can be difficult and involves an additional cost.</li> <li>User interface customization is limited.</li> <li>The user experience is dated.</li> </ul> <h3>BambooHR</h3> <p>Developed for SMBs, BambooHR offers many features to support the employee lifecycle and a UI that's intuitive for employees and the HR team. The system is easy to integrate with niche products that have prebuilt BambooHR interfaces.</p> <h4>Pros</h4> <ul class="default-list"> <li>The platform is affordable for many companies.</li> <li>The system centralizes, tracks and analyzes data.</li> <li>Integration and maintenance are easy to carry out.</li> <li>Tech support responds quickly.</li> <li>The UI is easy for employees to use and understand.</li> <li>The vendor doesn't require a long-term contract, and customers can cancel at any time before the next payment.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>More configurability for default reports would be helpful.</li> <li>Configuration options are not always granular enough when setting up security roles or modifying the system’s appearance.</li> <li>The platform can get more expensive with add-ons.</li> <li>Companies could outgrow the platform if their needs change.</li> </ul> <h3>Dayforce HCM</h3> <p>Dayforce was designed for global companies that want payroll included in their HR systems. The system also includes the most common HR modules, including recruitment and core HR. In 2024, the company changed its name from Ceridian to Dayforce, the name of its HCM product.</p> <h4>Pros</h4> <ul class="default-list"> <li>The platform enables users to manage a global workforce, with features to support different currencies and country policies.</li> <li>Dayforce Wallet enables employees to receive their paycheck at a time of their choosing, as long as it meets company guidelines.</li> <li>The process for resetting the test environment is simple and efficient.</li> <li>Users can manage all HR tasks in one system, with modules including core HR, learning, time and attendance, and career planning.</li> <li>AI is embedded in the platform and can help simplify processes, answer employee questions and summarize data.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>The system uses position management, which can add a layer of administration for companies that don't require this feature.</li> <li>Dayforce HCM can take a long time to implement feature requests.</li> <li>The UI is acceptable but not very modern.</li> <li>Managing the system is fairly complex.</li> <li>Customer support is often slow and inconsistent in their responses.</li> </ul> <h3>HiBob</h3> <p>HiBob, or Bob, is an all-in-one HR platform aimed at SMBs. The system offers the HR functionality usually present in a comprehensive HR system, including payroll and financial planning.</p> <h4>Pros</h4> <ul class="default-list"> <li>The system is known for its ease of use.</li> <li>The system includes integrated AI that can help with analyzing data, simplifying processes and ensuring users can find what they're looking for.</li> <li>HiBob is a well-rounded system for SMBs.</li> <li>The system offers support for a global workforce, with localization and multiple currency options.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>Some features are country-specific, although the company is working with partners to provide functionality for other countries. For example, the system only provides payroll for the U.S. and the U.K.</li> <li>The system does not offer extensive customizations.</li> <li>The mobile app doesn’t offer the same range of functionality as the web version.</li> <li>Pricing is not available online.</li> </ul> <h3>HRSoft</h3> <p>HRSoft's application enables users to automate performance management, compensation, pay equity and total reward statements. The overall focus of the software is compensation -- including making it easier to allocate pay increases and examine pay equity -- along with employee total rewards. Newer AI features include employee salary change recommendations and data cleaning.</p> <h4>Pros</h4> <ul class="default-list"> <li>Users can refer to performance feedback when carrying out compensation planning.</li> <li>The platform supports multiple currencies.</li> <li>HRSoft also supports complex compensation plans, such as plans by country; one-time increases; and stock, options and bonuses.</li> <li>Employees can view their compensation data in total rewards statements.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>HRSoft is slightly more expensive than some of its competitors.</li> <li>The system can be complex to set up and administer.</li> <li>Interfaces between HRSoft and HR information system vendors aren't always seamless.</li> <li>Carrying out complex analysis using reporting and dashboards is difficult.</li> </ul> <h3>Kudos</h3> <p>Kudos is employee reward and recognition software. It also includes employee surveys and features for sharing photos and information. Companies can integrate the system with many common applications.</p> <h4>Pros</h4> <ul class="default-list"> <li>The software includes multi-lingual support, and users from a wide range of countries can redeem points.</li> <li>Users can give awards to employees based on internal goals that match the corporate culture.</li> <li>The system supports nomination-based recognition, such as employee-of-the-month programs.</li> <li>The UI is easy to learn.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>Users might find it difficult to determine who is the subject of feedback for employee recognition.</li> <li>Reporting and analytics are at a high level, and performing more complex analysis can be difficult.</li> <li>Administering the system might be difficult for smaller companies.</li> <li>The mobile app does not include as many features as the web version.</li> </ul> <h3>Lattice</h3> <p>Lattice's software includes performance management, compensation, development and employee engagement. The system has new AI features, such as the ability to summarize data from one-on-one meetings.</p> <h4>Pros</h4> <ul class="default-list"> <li>The engagement functionality includes many useful tools, such as the ability to survey new hires, calculate employee <a href="https://www.techtarget.com/searchcustomerexperience/definition/Net-Promoter-Score-NPS">Net Promoter Scores</a> and send out pulse surveys.</li> <li>Users can view performance management data when carrying out compensation planning.</li> <li>The software integrates with popular messaging tools, which can make public recognition easier to carry out.</li> <li>The Grow module helps HR, managers and employees set development goals and manage ongoing employee and manager feedback.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>The numerous performance review options available to employees and managers can make the process more complicated.</li> <li>The UI is not always intuitive, especially for new users.</li> <li>Custom reports can be difficult to create.</li> <li>Naming conventions might not meet a company's needs and sometimes can't be edited.</li> </ul> <h3>SAP SuccessFactors HXM</h3> <p>SAP SuccessFactors HXM is a suite that offers all the functionality needed for talent management under the umbrella of human experience management (HXM). The software is targeted at large organizations. It's configurable and has a consistent look and feel across the platform.</p> <h4>Pros</h4> <ul class="default-list"> <li>The performance management and compensation modules are well integrated.</li> <li>The system is all-in-one and includes advanced reporting and dashboards.</li> <li>The platform supports many currencies and languages.</li> <li>SAP supports more customization options than many other vendors.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>The software was built by acquiring several applications and includes multiple databases.</li> <li>Administering the suite can be cumbersome because acquired modules require their own configuration. For example, users will need to set up security roles in different places depending on the applicable modules.</li> <li>The platform is complicated to maintain from an administrative perspective.</li> <li>The platform is more expensive than some other options.</li> </ul> <h3>Workday HCM</h3> <p>Like SAP, Workday is one of the biggest vendors in the <a href="https://www.techtarget.com/searchhrsoftware/How-to-choose-an-HR-software-system">HR software</a> market. Workday offers a comprehensive and configurable SaaS talent management system for U.S. and international users.</p> <h4>Pros</h4> <ul class="default-list"> <li>Complementary modules are available for finance, planning and analytics.</li> <li>The UI is intuitive for employees.</li> <li>The system's integrated platform enables HR system administrators to manage the entire system from one place.</li> <li>Workday supports multinational and large organizations with complex needs.</li> </ul> <h4>Cons</h4> <ul class="default-list"> <li>Implementing a large system like Workday takes more time than implementing midmarket software.</li> <li>The process of customizing and implementing new features can be expensive.</li> <li>The system can be rigid, requiring the system administrator to approve and configure many changes, such as adding a question to a list of candidate interview questions.</li> <li>Some users find it difficult to navigate within the system.</li> </ul> <p>Although no system will fully remove the complexity of talent management, taking the right approach to choosing software can help HR create a positive candidate and employee experience.</p> <p><i>Eric St-Jean is an independent consultant with a particular focus on HR technology, project management and Microsoft Excel training and automation. He writes about numerous business and technology areas.</i></p> </section> Learn about some of the best talent management products, the advantages and disadvantages of each system, and important considerations for implementing the software. https://cdn.ttgtmedia.com/rms/onlineimages/telecommunications_g1247980427.jpg https://www.techtarget.com/searchhrsoftware/tip/5-best-talent-management-software-systems Fri, 30 Jan 2026 09:00:00 GMT <p>In the last few years, AI developments have compelled businesses, governments and consumers alike to take a stance on AI and its place in their workplaces, products, laws and daily lives. While some entities are slower than others to adopt and adapt, it's incumbent on business leaders to face the inevitable ripple effect AI is having in their organizations and markets.</p> <p>Meanwhile, other technologies, such as quantum computing and physical AI, are at various stages of development, waiting for the next breakthrough to usher in enterprise adoption. While these technologies might not reach maturity in 2026, executives should keep them on their radar.</p> <p>Here, we pull together the trends around AI and emerging technologies that experts expect to see in 2026.</p> <section class="section main-article-chapter" data-menu-title="Emerging tech builds on last year's advancements"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Emerging tech builds on last year's advancements</h2> <p>Most business leaders have heard of -- and perhaps even deployed -- iterations of IoT, robotics and extended reality within their organizations. But recent developments in AI, connectivity, sensors and spatial computing are enabling the next stage of these technologies, with improvements in data collection, processing, mobility and security.</p> <p>As for the more future-looking technologies, quantum and neuromorphic computing show potential for tasks that require complex processing, pattern recognition and data analysis. The need for extensive data center power -- largely due to the massive uptick in AI consumption and cloud platforms -- is driving interest in small modular reactors that use conventional nuclear fission to generate electricity. And brain-computer interfaces? Well, they're not quite science fiction anymore.</p> <p><b>Read more here:</b></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/Emerging-technologies-to-watch">Emerging technologies to watch in 2026</a></p> </section> <section class="section main-article-chapter" data-menu-title="Physical AI takes center stage, while agentic AI matures"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Physical AI takes center stage, while agentic AI matures</h2> <p>If 2025 was the year of learning about agentic AI, 2026 will be the year businesses dive into the nuances of implementing AI agents, such as cost optimization and governance. In McKinsey &amp; Company's <a target="_blank" href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai" rel="noopener">report</a>, "The state of AI in 2025: Agents, innovation and transformation," the firm found that 39% of organizations had started experimenting with AI agents, while 23% said they were scaling agentic AI systems in their companies. Use cases will continue to advance, and so will questions about governance, security and training.</p> <p>Expect to hear more about physical AI, too, as the technologies enabling humanoid robotics, autonomous vehicles, delivery drones and the like evolve.</p> <p><b>Read more here:</b></p> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/AI-topics-that-enterprise-leaders-need-to-know">AI topics for 2026 that enterprise leaders need to know</a></p> <div class="youtube-iframe-container"> <iframe id="ytplayer-0" src="https://www.youtube.com/embed/iN4uObCQ1W8?autoplay=0&amp;modestbranding=1&amp;rel=0&amp;widget_referrer=null&amp;enablejsapi=1&amp;origin=https://www.techtarget.com" type="text/html" height="360" width="640" frameborder="0"></iframe> </div> </section> <section class="section main-article-chapter" data-menu-title="The heightened importance of AI governance and ethics"> <h2 class="section-title"><i class="icon" data-icon="1"></i>The heightened importance of AI governance and ethics</h2> <p>AI isn't unique in experiencing the push and pull of ethical standards, governance and regulation. But the stakes are high, as countries and organizations struggle to balance AI innovation with the appropriate safeguards. Ultimately, implementing ethics and governance into AI systems is more than checking a box to meet a framework or comply with a regulation. Responsible organizations will take steps in the coming year to formalize ethical AI oversight that promotes transparency, explainability, accountability and trust.</p> <p><b>Read more here:</b></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/Leading-AI-with-ethics-The-new-governance-mandate">Leading AI with ethics: The new governance mandate</a></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/AI-regulation-What-businesses-need-to-know">AI regulation: What businesses need to know in 2026</a></p> </section> <section class="section main-article-chapter" data-menu-title="AI influences business -- and national -- priorities"> <h2 class="section-title"><i class="icon" data-icon="1"></i>AI influences business -- and national -- priorities</h2> <p>While countries tackle AI regulation with different strategies, more are realizing the importance of localizing AI systems and data within national borders. Governments are implementing sovereign AI regulations to ensure AI infrastructure, data and security align with national interests.</p> <p>At the same time, businesses are increasingly integrating AI systems into workflows, strategizing and project management. AI can help with administrative tasks, scenario analysis and project scaling. But employees can still be hesitant to adopt AI, despite organizational pushes to use AI.</p> <p><b>Read more here:</b></p> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends">AI and machine learning trends to watch in 2026</a></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/AIs-business-future-Whats-to-come-in-the-next-5-years">AI's business future: What's to come in the next 5 years</a></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/How-AI-is-transforming-project-management">How AI is transforming project management in 2026</a></p> </section> <section class="section main-article-chapter" data-menu-title="Generative AI needs to prove its ROI"> <h2 class="section-title"><i class="icon" data-icon="1"></i>Generative AI needs to prove its ROI</h2> <p>A Wharton Human-AI Research <a target="_blank" href="https://ai.wharton.upenn.edu/wp-content/uploads/2025/10/2025-Wharton-GBK-AI-Adoption-Report_Full-Report.pdf" rel="noopener">survey</a> released in October 2025 found that 82% of enterprise leaders use generative AI weekly, with the majority actively measuring its ROI for productivity and profit. In many cases, GenAI offers more accessible use cases with its natural language interactions and low barrier to entry. Expect to see new ways to implement GenAI, as advances in underlying technology and improved tool integrations drive product development. Enterprises will be closely monitoring their returns on those GenAI investments.</p> <p><b>Read more here:</b></p> <p><a href="https://www.techtarget.com/searchenterpriseai/feature/The-future-of-generative-AI-Trends-to-follow">The future of generative AI: Trends to follow in 2026</a></p> <p><a href="https://www.techtarget.com/searchenterpriseai/tip/Top-generative-AI-tool-categories">Top generative AI tool categories for 2026</a></p> <p><a href="https://www.techtarget.com/whatis/feature/AI-content-generators-to-explore">AI content generators to explore in 2026</a></p> <p><i>Jennifer English is editorial director for TechTarget's AI &amp; Emerging Tech group.</i></p> </section> As we welcome 2026, AI continues to lead the charge in business innovation. Expect to see more about governance, physical AI and integration with emerging technologies. https://cdn.ttgtmedia.com/rms/onlineimages/ai_a311883856.jpg https://www.techtarget.com/searchenterpriseai/feature/Spotlight-on-AI-and-emerging-tech Thu, 29 Jan 2026 09:00:00 GMT 60 webmaster@techtarget.com