Qlik has generally made available both a conversational interface, dubbed Qlik Answers, and a Model Context Protocol (MCP) server for its data integration and analytics platform.
These latest additions to the Qlik Cloud platform are intended to make analytics data more accessible to both humans looking to analyze data and artificial intelligence (AI) agents that need to safely access curated data, says Drew Clarke, executive vice president for product and technology at Qlik.
In addition to providing a conversational interface for the Qlik Analytics Engine, there is also now a Discovery Agent that continuously monitors key metrics to surface meaningful anomalies and trends that enable organizations to respond faster to issues and opportunities.
Qlik later this year also plans to add additional agents to manage data pipelines and address data quality stewardship tasks along with new AI tools and assistants that will be capable of accessing data via its MCP server.
Collectively, the capabilities enable Qlik to reliably make trusted data available for analysis and more accurate reasoning via the application programming interfaces (APIs) that Qlik exposes, says Clarke. That approach makes it possible to ensure that the right data is being exposed to AI agents that are reasoning across large datasets, he adds.
That capability is critical because if organizations are going to trust the output generated by an AI agent, they need to know the underlying data used to generate it is reliable, notes Clarke. “It reduces emotional anxiety,” he says.
It’s not clear at what pace organizations are applying AI to analytics, but a recent Futurum Group report projects the global data intelligence, analytics, and infrastructure (DIAI) market to grow at a 17% compound annual growth rate through 2028 off a base of $541.1 billion in 2026 and exceeding $1.2 trillion by 2031.
AI development and operations are forecasted to increase (24%), while demand for tools needed to observe data will see a similar spike (22%) in 2026. There will be increased demand next year (19%) for data management tools that operate at the semantic level to provide a higher level of abstraction above the raw data stored in, for example, a data lake. In comparison, demand for data integration tools and storage platforms will grow at a slower 12% and 11% rate, respectively, in 2026, according to the report.
As data management evolves in the age of AI, there will be a fundamental shift away from manual data engineering workflows as more IT teams embrace automated extract, transform and load (ETL) pipelines, also known as Zero-ETL. In effect, data engineering teams are evolving into shepherds of data that is increasingly being used to drive AI applications and agents.
The pace at which organizations will make that transition will naturally vary, but as more AI agents are deployed, the need for better methods to curate and govern data is only going to become that much more obvious. The challenge, of course, will be finding a way to put the tools and platforms needed to achieve that goal in place before organizations are overwhelmed by thousands of AI agents.
