Systems | Development | Analytics | API | Testing

One of the things I have learned spending time with enterprise data and analytics teams is that insight without proximity to action is only half the job. You can build a beautiful dashboard, surface a critical pattern, or flag a risk in real time, and still have the insight die on a slide before it ever changes what happens next. The gap between "we know this" and "we did something about it" is one of the most persistent problems in enterprise software.

It’s clear that we are in the early innings of an unparalleled shift in how knowledge work gets done across the board. If you pull forward the changes we’ve already seen from teams who have adopted agents in software development and apply them to broader categories of knowledge work, you can see how these patterns will lead to a fundamental rethinking of the relationship and responsibilities between humans, software, and data.

The appetite for real-time data continues to grow. Across industries, the ability to act on data as it arrives is increasingly central to how leading organizations compete, from IoT and fraud detection to event driven analytics and AI agent architectures. Streaming data is no longer a specialist workload. It is becoming a core requirement. I am excited to announce that streaming ingestion is generally available in Qlik Open Lakehouse, part of Qlik Talend Cloud.

I spend a lot of time with customers and partners, and the pattern is consistent. Everyone wants the benefits of AI, faster decisions, more automation, better productivity. But the thing that slows them down is not the model. It’s the data underneath it. Not just any data, but trusted data to drive trustworthy business outcomes. As soon as you move from AI that explains to AI that influences workflows, ambiguity stops being an inconvenience. It becomes a liability.

Just a month into the year, and a few weeks since the launch of Qlik Trends 2026, we’ve already seen just how fast the AI landscape can evolve. The emergence of Claude Cowork and Moltbook reflect the two ends of the spectrum when it comes to agent collaboration. After taking a breath to digest Dan Sommer’s fascinating webinar – check it out if you haven’t already – I’ve been reflecting on which trends are set to make the most impact this year.

Qlik’s lessons learned from developing systems in a locked-down military-grade data zone at the 2025 NDIA Hackathon In early September, developers from across the country arrive at George Mason University’s Fuse facility with laptops, notebooks, and one big unknown: how do you build a defense-grade analytics solution in just 72 hours in a simulated air-gap environment.

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.