In today’s product landscape, analytics isn’t just about showing insights. It’s about moving data into the tools where work actually happens, so that users can make informed decisions in context. Users expect analytics to integrate with their systems, whether that means sending a lead to a CRM, creating a Jira ticket, or triggering a workflow downstream. As product owners, that means one thing: your analytics experience should enable seamless data flows across systems.

Gartner has once again named Qlik a Leader in the Magic Quadrant for Data Integration Tools, a position we have held for a decade. In that time, the landscape around data integration has shifted. Hyperscalers are moving up, large vendors are tightening their stacks, and acquisitions are reshaping customer choice. For CIOs and CDOs, that consolidation changes the question. It is less about who sits where in the quadrant, and more about how much control you still have over your own data and AI strategy.

Most AI analytics projects are failing to deliver on their promises, and the cause isn’t what you might expect. This creates widespread project failures and undermines confidence in AI-driven analytics. What are the problems with AI analytics and how can organizations address them?

When you embed traditional BI tools, you work with platforms originally designed for internal analysts who expect to explore data directly. Embedded capabilities came later, and while these tools expose APIs, every variation in experience requires development work. The challenge is that traditional BI tools aren’t built for the full spectrum of embedded use cases. Most embedded analytics implementations must serve several distinct user types inside your customers’ organizations.