AI didn’t kill BI.
It made trust more important.

As Forrester’s Boris Evelson put it in the Business Intelligence Platforms Wave: generative and agentic AI aren’t replacing BI — they’re leveling the playing field, because most vendors are now building on the same underlying large language models (LLMs).

The differentiator isn’t who bolted on AI first. It’s whose AI you can actually trust to act on your data.

And I think that’s what changes the story — and that’s the real shift happening in BI right now.

THE MARKET IS BEING REDEFINED

Gartner’s 2026 Magic Quadrant for Analytics & Business Intelligence Platforms didn’t just reshuffle a few dots on a chart. It reflects how significantly the market is evolving — around agentic AI, governed semantic layers and AI-augmented decision support.

Vendors are increasingly being judged on whether their AI agents can autonomously plan, reason and act across the data-to-insight workflow, not just answer a query and hand back a chart.

BARC’s Data, BI & Analytics Trend Monitor 2026  makes the same shift visible from another angle. Data Quality Management has reclaimed the #1 spot as the most important trend in the field, with Data Security & Privacy at #2.

GenAI, decision intelligence and agentic AI are all climbing rapidly in importance. But they’re climbing on top of trust and governance — not instead of them.

In other words, the market isn’t only asking:
– How agentic is your AI?

It’s increasingly asking:
– Can I trust what your agent just did?

WHAT I’M SEEING IN THE MARKET

What strikes me is that vendors seem to be moving in two directions at once.

First, AI is being embedded directly into BI products — helping users find insights, author analyses, generate narratives and interact with data using natural language.

At the same time, we’re seeing more vendors add support for open standards such as MCP. The idea is compelling: rather than forcing users to interact only through a vendor’s own AI, connect the AI tools they already use to a governed data layer.

That raises the most important question: If AI can access enterprise data from anywhere, what makes that access trustworthy?

WHERE QLIK FITS

Against this backdrop, I think Qlik has an interesting way of framing what matters in AI: Trust, Context and Freedom. Those three pillars map closely to the questions I think the BI market increasingly needs to answer as AI becomes more autonomous.

Trust — Can you trust the data and the actions taken on it? Qlik points to data quality, governance and lineage as foundations for AI that can be understood and audited.

Context — Can AI understand the relationships in your data, rather than simply generate a plausible answer? Qlik’s Associative Engine gives AI agents a way to explore those relationships and helps ground their reasoning in the underlying data.

Freedom — Can you choose how and where you use AI? Qlik’s open platform and MCP support are designed to let customers connect governed data and analytics to the AI tools and models they choose, across cloud, on-premises and hybrid environments.

What I like about this framework is that it puts AI in perspective. The AI itself isn’t necessarily the differentiator anymore. It’s the Trust, Context and Freedom around the AI that determine what it can actually do with enterprise data.

Qlik’s argument is essentially that we’re closer to agentic AI than we might think:

• Trust: you already have trusted data in your Qlik applications.
• Context: the Qlik Associative Engine gives AI a richer understanding of that data.
• Freedom: Qlik’s open platform and MCP make that data and context available to the AI tools you want to use.