As enterprises focus on developing AI tools to inform decisions and automate business processes, Qlik on Tuesday announced a set of analytics, data engineering, and governance capabilities aimed at helping customers successfully build and deploy agents and other AI applications.
Even though investment in AI development continues to increase, most AI initiatives are abandoned before being deployed in production. According to Sam Pearson, the vendor’s chief technology officer, Qlik has identified data issues, including lack of access to relevant proprietary data and data quality, as one of the reasons pilots fail.
To help customers overcome some of these challenges, Qlik’s latest analytics capabilities include agents that combine structured and unstructured data to provide contextual responses to queries and prompts, agents that monitor important data to uncover insights, and agents that can build machine learning models.
Additionally, new Qlik data engineering capabilities are designed to help data teams easily and quickly deliver trusted data to AI applications, and new governance tools aim to ensure data is available to inform analytics and AI tools.
The new feature was revealed at Qlik Connect 2026, the vendor’s user conference in Kissimmee, Florida.
“These announcements move Qlik closer to becoming a managed data-to-decision platform, extending analysis to action and moving beyond generating answers with AI,” said Michael Ni, analyst at Constellation Research. “The real story is not just another AI assistant. By connecting trusted data products, analytics, data engineering, and action with Qlik, customers can operationalize AI with more control and less assembly effort.”
David Menninger, an analyst at IDC Software Research, similarly said that Qlik’s new features address customer needs by focusing on reliability and governance, as well as built-in agent capabilities.
“This is an appropriate response to current market demands,” he said. “Built-in agents started out as simple Q&A-type assistants, natural language interfaces for accessing data. Today, Qlik offers agents for a variety of tasks related to data and analytical processing.”
King of Prussia, Pennsylvania-based Qlik is a longtime analytics vendor that has evolved into a data platform vendor featuring data integration, data quality, and AI development capabilities. In February, the vendor made Agent Experience, a suite of agent AI-powered analytics and AI development tools, generally available on Qlik Cloud.
Power your AI
While many companies are still struggling to benefit from AI initiatives, many data management and analytics vendors have shifted their focus in recent months from developing features that speed up and simplify building agents to finding ways to improve the quality and relevance of the data that informs AI tools.
The real story isn’t another AI assistant. By connecting trusted data products, analytics, data engineering, and action with Qlik, customers can operationalize AI with more control and less assembly effort.
michael neeConstellation Research Analyst
So far in 2026, Databricks, MongoDB, and Teradata have released new features aimed at improving the data retrieval process. Additionally, vendors such as GoodData, Pentaho, and ThoughtSpot have introduced features designed to provide contextually relevant data to agents and other AI tools.
Qlik similarly adds and updates features to improve the applications our customers are building, enabling them to keep their employees informed and their business processes more efficient.
“Everyone is interested in AI lenses…and they’re asking, ‘How can you help me achieve my goals with AI?'” Pearson said. “If you look at everything we do, that’s the common denominator.”
The vendor’s agent analytics experience is designed to provide customers with context-aware AI capabilities that reveal reliable and traceable insights.
Qlik Answers is a natural language interface that has been available for some time to query structured and unstructured data, and is now the entry point for agent analytics. Additionally, Qlik now provides agents to perform analytical tasks such as monitoring data for new insights, creating data workflows, triggering workflows using natural language, and building machine learning models to make predictions.
Meanwhile, Qlik’s Model Context Protocol server allows customers to connect third-party AI capabilities to Qlik’s environment to provide agents and other AI applications with the context they need.
Beyond analytics, Qlik’s new data engineering capabilities are designed to help customers create and feed trusted data into AI and analytical tools that inform decision-making, while reducing manual effort and making it faster and easier to operationalize data than before.
Features include Declarative Pipelines, which allows engineers to create and evolve pipelines using natural language, AI Assistant in Talend Studio (not yet available, but expected to be released later this year), which allows developers to use natural language to perform tasks, and the general availability of Open Lakehouse Streaming, which allows users to integrate event data with batch and change data capture workloads.
Finally, new trust and governance features address the quality of data used to inform AI and analytics tools.
These include the repositioning of data products as managed assets that can be used to inform AI workloads (reusable tools such as metrics, models, and dashboards), a data contract layer that allows teams to define what data products are expected to be delivered, data product agents that allow users to create data products using natural language, and data quality agents that ensure data trustworthiness.
While helping customers achieve their AI goals is a fundamental objective of Qlik’s product development plans, Pearson said part of the impetus for building the specific features introduced in Qlik Connect was based on user feedback.
“We’re getting a lot of feedback from our customers,” he said, noting that the vendor has fundamentally redesigned Qlik Answers to improve the quality and accuracy of responses based on observations from users. “But part of this is also our vision, and part of this is obvious, because the way we develop software is very telling of what happens in the rest of the software industry.”
In addition, Qlik held a meeting last summer to discuss which parts of the platform were most valuable in order to determine where to focus its product development plans, Pearson continued.
“This is at the core of our vision, and we are now in a position to help our customers deploy turnkey AI solutions on top of all the data they can trust,” he said. “This is a fundamental rethink of what Qlik usage looks like, and this is just the first wave of that.”
With many companies struggling to generate enough trust to use their AI tools, Menninger said Qlik’s new trust and governance features are perhaps the most valuable feature set for users. Meanwhile, the company’s agents are not unique among data management and analytics vendors, but the amount of agents it is developing to take on data-related tasks is valuable, he continued.
“The breadth of embedded agents and their applicability along the entire data and analytics spectrum is impressive,” Menninger said. “Qlik will probably be near the front in terms of built-in agent capabilities.”
Ni similarly said that while Qlik is not the first company to launch a suite of agents, it offers agent AI capabilities that address what enterprises need to advance their own AI initiatives.
“Qlik may not be winning the AI arms race in terms of size or leadership, but it may win more than its share in the enterprise battle because it understands the real problem: AI doesn’t fail in demos, it fails in production,” he said.
next step
Following the release of a number of agents as part of Qlik’s latest analytics, data engineering and governance capabilities, Pierson said the vendor plans to release more agents in the coming months.
Qlik’s early agent AI capabilities focused on fundamental benefits such as increasing efficiency and surfacing insights. The vendor’s next set of agents perform more in-depth analysis such as predictive modeling.
“We plan to ship 20 more agents. This is the year this product goes into mass production,” Pearson said.
Beyond the addition of agents, Qlik’s product development plans include improvements to the semantic layer to help users discover more data relevant to their AI initiatives, he continued. Additionally, Pierson noted that as AI ecosystems (interconnected networks with capabilities from different vendors) become more pervasive, it will be important for Qlik to be a trusted data provider with agents that demonstrate differentiated capabilities.
“That’s going to be a winning formula,” he said.
Ni noted that Qlik has demonstrated an understanding of where customers are having problems, and suggested that the vendor’s next product development plans will focus on moving beyond providing information and taking action on behalf of users.
“Qlik needs to build capabilities that support decision-making itself, not data or dashboards,” he said. “That means building systems that not only recommend actions, but also execute, learn, and optimize outcomes over time.”
Menninger similarly advised Qlik to focus on developing tools that help users not only generate insights, but act on those insights.
“Qlik and other companies should continue to focus on trust and conduct,” he said. “Increasingly rich testing, evaluation, and observability tools will further establish confidence in agent capabilities, and the translation of analytical insights into action is still in its infancy.”
Eric Avidon is a senior news writer at Informa TechTarget and a journalist with more than 30 years of experience. He is responsible for analysis and data management.
