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Gartner has named Qlik a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions, our seventh time being recognized as a Leader in this Magic Quadrant. As AI becomes operational, data quality matters more than ever. We’re past the phase where AI just produces outputs. AI is starting to initiate, route, and act across real workflows.

For decades, organizations have worked to use data to make better decisions and drive better outcomes. Data has become the lifeblood of the business, and AI now has the power to unlock it in new ways. The paradigm is shifting, from dashboards and visual interfaces to AI driven experiences. But too much data is still stuck in silos, incomplete, and inaccurate. Many analytics workflows remain manual, which slows time to value, limits insight quality, and raises cost.

You’re in the cockpit of a small plane, cruising a few thousand feet in the air. Then, out of nowhere, the airspeed dips and an alarm rings out. The nose drops, and you're in a full-out stall by the time instinct kicks in. You pull back on the yoke, trying to steady the plane, stop the descent and patch things up midair. But that’s exactly the move that seals your fate, sending you into a deeper spiral.

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.

Our customers’ worlds are complex, and for good reason. It’s multi-cloud. It’s SaaS plus on-prem. It’s Snowflake, Databricks, AWS, Azure, Salesforce, and more. Underneath every one of those choices is the same constraint: data must be accessible, stay current, and stay controlled. The hard part is getting trusted data where it needs to be, when it needs to be there, with the controls to use it responsibly.

January 28th marks International Data Privacy Day, a day to raise awareness about the importance of privacy and promote best practices. Privacy has long been about maintaining control of your data, in particular where it goes, and who can see it, and what it’s used for. Two topics under this theme, data residency and data sovereignty, come up in almost every customer conversation at the moment. While closely aligned, there are key differences.

If you have ever asked three teams for the definition of the “same” metric and gotten three different answers, you have already met one of the most expensive, least talked about problems in modern data. It is not a lack of data. It is a lack of shared meaning. As analytics and AI spread across more tools, clouds, and teams, business context often fails to travel with the data. A metric defined one way in a dashboard gets redefined in a notebook.

Over the past two years, generative AI has transformed how we create, learn, and interact. But a more profound shift is already underway—one that changes not just how we work but who (or what) does the work itself. We are entering the era of agentic AI, where systems don’t merely answer questions—they reason, decide, and act on our behalf.

Somehow, another year has passed and we’re back again to deliver our Trends Outlook for 2026. As Qlik’s first milestone of the year, it’s a busy but exciting time that hugely energizes me after the holiday break. We weren’t short of inspiration. The world has given us a far from a blank canvas on which to illustrate our Trends and in particular, conversations around AI have firmly evolved from future-gazing to wrestling with the formula for harnessing its power.

Human Resources is rich in data but often struggles to make it accessible and actionable. As a profession we also live in the ‘grey’ – of course recognizing that People are by very definition different, unique and often require individual approach.