The state of analytics 2026: Operationalizing AI insights and shifting to embedded analytics

Why AI-powered analytics still aren’t  delivering, what needs to change, and what product leaders are doing next

In this original research from Sisense and UserEvidence, 267 product leaders reveal a growing gap between AI ambition and real-world results. While 48% say they trust AI insights, teams still spend 40% of their time validating them, which slows decisions and limits impact.

Despite widespread adoption, analytics remain difficult to access and even harder to operationalize. Integration challenges, fragmented data, and limited trust in AI outputs continue to stall progress.

The result: delayed innovation, stalled AI initiatives (with 29% stuck in pilot), and 65% of teams still making decisions without referencing available data.

The takeaway? AI alone isn’t enough. To drive real product impact, analytics must be embedded, accessible, and operationalized directly within the products and workflows where decisions happen.

Leading organizations are shifting to AI-powered, embedded analytics approaches that reduce friction, integrate seamlessly, and deliver trusted insights in the flow of work.

Get the full report to explore:

• Why 69% of teams say analytics still aren’t easily accessible, and how that impacts decision-making
• What’s keeping 29% of AI initiatives stuck in pilot
• How integration challenges are delaying product innovation
• Why embedded, AI-driven analytics is becoming the new standard