You’re Closer to Agentic AI Than You Think

At Qlik Connect, one of the big messages we’re putting in front of customers is this: you’re closer to agentic AI than you think.

I believe that because a lot of our customers already have more of the foundation in place than they may realize. If you have been working to improve data quality, strengthen governance, connect data across the business, and move analytics beyond reporting into real decision support, you are already building the conditions agentic AI needs to deliver value.

That is what makes this moment interesting.

At Qlik Connect, we’re expanding our agentic experience by announcing new agents for prediction, automation, and analytics development.

The goal is simple: help teams move from questions to decisions, and from decisions to action, inside a more complete, governed flow.

• Qlik Answers is the starting point, bringing together structured analytics and unstructured content in one governed experience.

• Discovery Agent helps teams surface anomalies and important changes earlier, so they can investigate sooner and act faster.

• Predict Agent brings forward-looking insight into the flow, helping people understand what is likely to happen, why it matters, and what they may want to do next.

• Automate Agent helps close the gap between analysis and execution by triggering workflows in downstream systems.

• Analytics Agent helps accelerate analytics development work.

• And MCP Server brings Qlik’s context-rich analytics into the third-party assistants teams are already using.

Put all of that together, and it starts to look like a more complete agentic experience: detect, investigate, predict, and act.

To me, that is the real point. Agentic AI only becomes valuable when it is built on trusted data, real business context, and enough confidence to act on what the system gives you.

I saw a recent example that made this point really clearly. A $50 million real estate deal was delayed because both sides used the same LLM to ask opposite questions. One asked if they were paying too much. The other asked if they were accepting too little. The model said yes to both, and in both cases it sounded convincing.

That is where things break down.

Each answer looked reasonable on its own. The problem only showed up when you looked at both together, added the missing context, and thought about what would happen if people acted on incomplete reasoning, frustration, delays, and ultimately an expensive mistake.

That is the gap Qlik closes. We bring the context, the relationships, and the analytical framework that help decisions hold up in the real world, not just in isolation. That is what turns AI from an interesting output into something an enterprise can actually trust and act on.

And that brings me back to the point we’re making at Qlik Connect. Trusted data is not optional. Context is not optional. Governance is not something you add later. It is the whole game.

So when I look at where customers are right now, I do not think the story is, “someday you’ll be ready.” I think for a lot of Qlik customers, the story is much simpler than that.

You’re closer to agentic AI than you think.