What are common AI readiness gaps?
The gaps we see most often: data lives in silos across different systems and formats with no way to connect them. Teams have adopted AI tools on their own with no coordination, so you get five different copilots and zero shared learning. Data quality looks fine for dashboards but falls apart when AI needs it 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 nobody owns AI at the firm level, so projects stall waiting for someone to take responsibility.
These gaps are not failures. They are the starting point. Once you know where the gaps are, you can fix them in weeks instead of guessing for months.