AI readiness is a governance question, not a model question

The teams that struggle with AI didn't pick the wrong model. They skipped the guardrails. Almost every AI failure we're called into traces back not to the technology but to what was missing around it: unclear data, no decision rights, no documentation, no plan for what happens when the system is wrong.

"AI readiness" gets treated as a technical question, which model, which vendor, which integration. Those are the easy parts now. The hard part, and the part that determines whether the thing holds up, is governance. Is your data organized and trustworthy enough to act on? Who owns the decision when the model and a human disagree? What's documented well enough that someone other than its builder can run it? What's the obligation, privacy, compliance, grant reporting, and how is it tracked?

This is what week two of a Sprint actually scores. Not the sophistication of the model, the readiness of the ground it stands on. A regulated institution we worked with didn't need a more advanced system; it needed the governance and documentation that would let the system it had hold up under scrutiny. That work is unglamorous, and it is the difference between an AI initiative that survives an audit and one that quietly gets switched off.

Ambition without guardrails is the most common failure mode we see. The model is no longer the hard part. The guardrails are.

Score your own readiness, data, decision rights, governance, in ten questions. Take the Scorecard