Latest Qlik tools target helping users achieve AI goals

Powering AI

With many enterprises still struggling to benefit from their AI initiatives, numerous data management and analytics vendors have shifted their focus in recent months away from developing capabilities that speed and simplify building agents to finding ways of improving the quality and relevancy of the data informing AI tools.

The real story is not another AI assistant. It is Qlik connecting trusted data products, analytics, data engineering, and action so customers can operationalize AI with more control and less assembly work.

So far in 2026, Databricks, MongoDB and Teradata launched new capabilities aimed at improving the data retrieval process. In addition, vendors including GoodData, Pentaho and ThoughtSpot have introduced features designed to better provide agents and other AI tools contextually appropriate data.

Qlik is similarly adding and updating capabilities to improve the applications customers are building so they can be trusted to make employees better informed and business processes more efficient.

"Everyone is interested in an AI lens … and they're asking, 'How can you help me achieve my goals when it comes to AI?'" Pierson said. "When you look at everything that we're doing, that is a common thread."

The vendor's agentic analytics experience is designed to provide customers with contextually aware AI capabilities that surface trustworthy and traceable insights.

Qlik Answers, a previously available natural language interface for querying structured and unstructured data, is now the entry point for agentic analytics. In addition, Qlik now provides agents that perform analytics 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 enables customers to connect third-party AI capabilities with Qlik's environment to give agents and other AI applications the context they require.

Beyond analytics, Qlik's new data engineering capabilities are designed to enable customers to create and deliver trusted data to decision-informing AI and analytics tools while reducing manual work so that data can be operationalized faster and easier than in the past.

Features include Declarative Pipelines so engineers can create and evolve pipelines using natural language, an AI assistant for Talend Studio -- not yet available but planned for release later this year -- that enables developers to use natural language to carry out tasks, and the general availability of Open Lakehouse Streaming so users can unify event data with batch and change data capture workloads.

Lastly, new trust and governance capabilities address the quality of the data used to inform AI and analytics tools.

They include repositioning data products -- reusable tools such as metrics, models and dashboards -- as governed assets that can be used to inform AI workloads, a data contract layer where teams can define what data products are expected to provide, a Data Product Agent that enables users to create data products using natural language and a Data Quality Agent to ensure that data can be trusted.

While helping customers reach their AI goals is the underlying objective of Qlik's product development plans, the impetus for building the specific features introduced at Qlik Connect was partly based on user feedback, according to Pierson.

"We do get a lot of customer feedback," he said, noting that the vendor fundamentally rearchitected 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 software has developed … is very telling with what is going to happen with the rest of the software industry."

In addition, Qlik held a meeting last summer to discuss which parts of its platform are most valuable to determine where to focus its product development plans, Pierson continued.

"That was the kernel of vision that is now putting us in position to help customers put a turnkey AI solution on top of all this trusted data," he said. "This is a fundamental reimagining of what it's going to be to use Qlik, and this is just the first wave of that."

With many enterprises struggling to engender enough trust in their AI tools to make them usable, Qlik's new trust and governance capabilities are perhaps the most valuable set of features for its users, according to Menninger. Meanwhile, though its agents aren't unique among data management and analytics vendors, the volume of agents it is developing to take on data-related work is valuable, he continued.

"The breadth of built-in agents and their applicability along the entire data and analytics spectrum is impressive," Menninger said. "It probably puts Qlik near the front of the pack in terms of built-in agent capabilities."

Ni similarly noted that Qlik, while not the first to launch an array of agents, is delivering agentic AI capabilities that address what enterprises need to move forward with their own AI initiatives.

"Qlik is not winning the AI arms race on size or being first, but it may win more than its share of enterprise battles because it understands the real problem, which is that AI doesn't fail in the demo, it fails in production," he said.

Next steps

Following the launch of numerous agents as part of Qlik's latest analytics, data engineering and governance capabilities, the vendor is planning to launch more agents in the coming months, according to Pierson.

Qlik's initial agentic AI capabilities focused on basic benefits such as improving efficiency and surfacing insights. The vendor's next set of agents will perform deeper analysis, such as predictive modeling.

"We're going to be shipping another 20 agents -- this is the year when this stuff is getting put into production," Pierson said.

Beyond additional agents, Qlik's product development plans include improving its semantic layer to help users further discover relevant data for their AI initiatives, he continued. In addition, Pierson noted that as AI ecosystems -- interconnected networks featuring capabilities from various vendors -- become more ubiquitous, it will be important for Qlik to be a trusted provider of data with agents that demonstrate differentiated capabilities.

"That's going to be a winning formula," he said.

Ni, who noted that Qlik is showing an understanding of where customers are having difficulty, suggested that the vendor's next product development plans focus on going beyond providing information to taking action on behalf of users.

"Qlik has to build out capabilities to support the decision itself -- not the data, not the dashboard, but the decision," he said. "That means building systems that don't just recommend actions, but execute, learn and optimize outcomes over time."

Menninger likewise advised Qlik to focus on developing tools that help users go beyond insight generation to acting based on those insights.

"Qlik, and others, should continue to focus on trust and action," he said. "More and better testing, evaluation and observability tools will further establish trust in agentic capabilities. And we are still early in converting analytic insights into actions."

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.