Data governance is the first step to moving AI from pilot to production

TechRadar notes that while 88% of firms now use AI in at least one function, only 7% have fully scaled it, and many pilots stall because the underlying data lacks a unified context Moving AI from pilot to production starts with the data. Marketing teams see the gap most clearly when a pilot that relied on a clean, narrow dataset meets the complexity of an enterprise stack with multiple metric versions and inconsistent field names.

The article argues that the core issue is not model quality but the absence of a “knowledge layer” that encodes canonical definitions and business rules across warehouses. Such a layer sits atop existing data infrastructure, avoiding costly replacements while giving AI a governed starting point for interpreting metrics.

Static definitions alone are insufficient; the layer must also accommodate dynamic context such as recent budget changes or new promotions. Collaboration between marketing and data leaders is essential to keep definitions current and to embed governance into analytical workflows.

Our read: The immediate barrier to production-grade AI is data semantics, not compute. Enterprises that invest in a robust, governed knowledge layer will reduce model hallucinations and accelerate the transition from isolated pilots to reliable, organization-wide AI deployments.