The missing role in every enterprise AI strategy: The analytics engineer
Executive Take
Hiring an analytics engineer before the next AI rollout is a governance investment, not a headcount nicety the piece's real point is that CIOs who treat metric definitions as an engineering afterthought will keep watching pilots succeed and production deployments fail for the same unaddressed reason.
Executive Summary
A CIO.com contributor argues enterprises lack a dedicated "analytics engineer" role to govern metric definitions, causing AI outputs to contradict dashboards. Citing dbt Labs' 2024 survey (14% of data professionals say goals are clear) and Foundry's 2026 State of the CIO study (fewer than half have formal AI success metrics, only 19% report meeting ROI goals), the piece ties ungoverned data to stalled AI scaling.
Why It Matters
Technology and AI leaders should care because the article names a structural cause ungoverned semantic layers for a failure pattern (AI numbers contradicting dashboards) that erodes executive trust in AI regardless of model quality; it's a data-architecture staffing gap, not a model problem.