What are common AI readiness gaps?
The findings we see most often: data lives in silos across different systems, different formats, with no integration layer. Teams have adopted AI tools individually with no coordination, so you get five different copilots and zero shared learning. Data quality is overestimated because it works fine for dashboards but falls apart when AI needs it to be structured, clean, and consistent.
Governance policies either do not exist or exist only on paper. Nobody has defined who owns AI decisions, what data can be used, or how models get approved. And there is no clear owner for AI initiatives at the firm level, so projects stall waiting for someone to take responsibility.
These gaps are not failures. They are the starting point for a focused investment. Once you know where the gaps are, you can fix them in weeks instead of guessing for months.