What analytics teams build, what end users need, and how AI is reshaping both

Why builders and end users often want different things from AI-powered analytics, and what product teams are doing next

In this original research from Sisense and UserEvidence, 331 product and engineering practitioners reveal a surprising disconnect: 80% of builders believe their analytics capabilities meet end-user expectations, yet nearly one-third of end users say data is still difficult to access, and over half want AI capabilities builders aren’t prioritizing.

As organizations race to embed AI into their products, many teams are building the wrong experiences. Builders prioritize AI-generated summaries, while end users overwhelmingly want to ask questions of their data in natural language and receive personalized insights directly in the products they already use.

The takeaway? Great analytics isn’t defined by how much AI you build. It’s defined by whether users can easily find answers, take action, and make decisions without leaving their workflow.

Purpose-built embedded analytics platforms help close that gap by making it easier to deliver trusted, personalized AI experiences that users actually want.

Get the full report to explore:

• Why 69% of builders consider their analytics advanced—but end users still report major frustrations
• Where builders’ AI priorities diverge from what end users actually want
• What’s slowing teams from shipping AI-powered analytics faster
• Why embedded analytics is becoming the preferred experience for both builders and end users