AI is not a pilot anymore. In 2026, it is the operating agenda. And if you’re leading a business or an IT project right now, you’re probably getting the same two questions.
First: “When do we see real outcomes?” Second: “Can we trust what we’re getting?”
Those are fair questions. They’re the right questions.
Because the truth is, the model is rarely the problem. The hard part is everything around it. The data. The access. The silos. The controls. The reality of enterprise systems.
We see this in our recent Agentic AI Study with Enterprise Technology Research (ETR). Ninety-seven percent of large enterprises report funding is in place. Only 18% say they are fully deployed. And the top barriers are not surprising. Data quality and data access.
Qlik makes data work for AI, so customers can move faster and trust the outcomes, by unifying data, building in trust and governance, scaling performance cost-effectively, and staying flexible as the market evolves.
AI investment is accelerating, and expectations are rising with it. But value at scale is still rare. Boston Consulting Group reports that only 5% of enterprises are achieving AI value at scale. The exact number will vary. The pattern doesn’t.
Models are widely available. Tools keep improving. What still holds teams back is the foundation. Data quality. Data access. Integration. Governance. That’s where most programs slow down, or stall.
Agentic AI raises the bar because agents act
Agentic AI makes this even more urgent, because agents act. They do not just answer a question. They trigger workflows. They influence decisions. They move at machine speed.
So, the standard has to change. Leaders need outputs that are useful, yes. But also accurate, explainable, and governed. Otherwise, you are not scaling AI. You are scaling risk.
That is why the conversation is shifting from “Can we build it?” to “Can we prove it, trust it, and run it at scale without creating a cost or risk problem?”
When you listen closely, customer needs are remarkably consistent:
“Help my data work together across what I already have.”
“Help me trust what I’m seeing, and what the AI is doing.”
“Help me scale without economics becoming the obstacle.”
“Help me evolve without lock-in.”
In AI, change is guaranteed. Your foundation has to keep up.
How we deliver: Achieve, Accelerate, Adapt
We organize our strategy around three outcomes customers are demanding. We align all our innovation and delivery to help them achieve, accelerate, and adapt AI, with confidence and at scale.
Achieve AI with trusted, contextual data and insights
If your data does not work together, AI will not work at scale. That means connecting data across systems, clouds, and platforms. It also means governance that is practical. Governance that supports speed and accountability at the same time.
That is what makes AI usable in the real enterprise, not just in a demo. It is also why 75% of the Fortune 500® use Qlik. Customers rely on us because we meet them where they are and help them build confidence in their data as the foundation for AI.
And the clearest explanation often comes from customers themselves. Fujitsu put it plainly:
“The ability to ask a question and get a trusted, contextual answer, across structured reports, unstructured content, and automated workflows, is exactly the kind of capability we’ve been waiting for.” Yuzuru Fukuda, Corporate Executive Officer, Senior EVP, Enterprise Division CEO
That is what making data work for AI looks like. Trusted answers, with the context and controls enterprises need to act.
Accelerate AI with fast, cost-efficient performance
Here’s a reality leaders run into fast. AI is not just a capability story. It is an economics story.
If the cost model breaks when usage grows, adoption stalls. People pull back. Confidence drops. Then the program slows down, even if the technology is impressive.
Performance and cost are not technical footnotes. They are leadership issues.
We see this in measurable outcomes. Steinemann has achieved over 90% forecast accuracy, with under 1% error in logistics. Integra Financial Group projects $1M in savings per year.
Those are not “interesting pilots.” That is what it looks like when AI becomes repeatable.
Adapt AI with an open ecosystem built to evolve
Finally, adaptability. This is where a lot of AI strategies get boxed in.
Because AI strategy is not one decision anymore. It is a series of decisions over time. Tools improve. Architectures shift. Regulations evolve. Your business priorities change.
Customers need a foundation that can keep pace. That means open. It means flexible. It means designed for the enterprise as it exists.
And the proof is in the outcomes. Touchpoint saved six months of deployment time. ADOC moved from taking weeks to make and act on decisions, to doing it in days.
That is what it means to adapt AI. You do not start over every time the environment changes. You build on a foundation designed to evolve.
A clear commitment, grounded in customer reality
AI value will not be won by organizations that chase the most tools or the loudest claims. It will be won by those who get the fundamentals right, data working together, trusted context, performance that scales, and freedom to evolve.
That is what making data work for AI means, and it is the commitment we bring to customers every day.
For the technical view of how we deliver this, read Drew Clarke’s companion post, it breaks down the product and architecture choices that make “making data work for AI” real in practice.
In this article:
AI