Data, AI & Analytics Trends Across Organizations in 2026
Fragmentation is stalling AI ambitions, and enterprises are rethinking their foundations
80% of data teams spend more time preparing data than generating insights.
Despite heavy investment in data platforms and AI, fragmentation and semantic drift remain the default operating state.
“Fragmentation is not a tooling issue alone. It is a structural challenge rooted in decades of siloed systems, embedded logic inside BI platforms, and a lack of unified ownership.”
This report, conducted by CIO Dive Studio, part of Informa TechTarget, reveals where current approaches fall short, and why independent semantic layers are emerging as a critical foundation for scalable, governed analytics and AI.
Inside the report, you’ll learn:
Why nearly 99% of enterprises struggle to define business metrics consistently across analytics tools
How semantic inconsistency drives this productivity gap
Why cost volatility, not raw query spend, is the real concern for CIOs and CDOs operationalizing AI at scale
How AI adoption is exposing governance and observability weaknesses, with 87% of leaders demanding visibility into how AI uses data
Why popular approaches like data virtualization, custom builds, and vendor-tied platforms improve access—but fail to deliver durable semantic consistency
How independent semantic layers are shifting from architectural theory to mainstream adoption, and where execution still breaks down
These findings point to a clear conclusion: scalable analytics and trustworthy AI require a shared semantic foundation that travels consistently across tools, platforms, and models.
Read the full report