Despite growing trust in AI, product teams still struggle to operationalize insights, delaying innovation and decision-making
Sisense, a leader in AI-powered embedded analytics, released its 2026 State of Analytics report. The study of 267 product leaders reveals a widening gap between AI ambition and real-world results, as teams struggle to operationalize insights within workflows.
Despite increased adoption of analytics and AI tools, accessibility remains a major barrier, with 69% of respondents stating that analytics are not easily accessible, and 65% admitting they’ve made business decisions without consulting available data.
Despite increased adoption of analytics and AI tools, core challenges persist. Accessibility remains a major barrier, with 69% of respondents reporting that analytics are not easily accessible across their organization, and 65% admitting they’ve made business decisions without consulting available data due to access challenges.
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Additionally, while 48% of leaders trust AI insights, teams still spend 40% of their time validating them. Integration complexity also remains a barrier, with 29% of AI initiatives failing to move beyond the pilot stage.
Key Insights
AI Trust is Conditional: Significant time is lost to human oversight due to concerns about accuracy and data quality.
Accessibility Barriers: Analytics still largely require specialist intervention, limiting democratization of data.
Integration Bottlenecks: Technical hurdles delay time-to-market and limit the ability to deliver differentiated experiences.
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The Future of Operational Analytics
Product leaders expect analytics to move into the products themselves. 43% of respondents anticipate analytics will be embedded directly into business applications, while 24% see conversational interfaces as the future primary access point.
“Organizations have made significant progress in adopting AI, but adoption alone doesn’t drive value,” said Andrew Loomis, VP Customer Success at Sisense. “The real challenge, and opportunity, is operationalizing those insights inside the products and workflows where decisions happen. That’s where embedded, AI-powered analytics becomes essential.”
