The AI readiness score methodology: what would you change if you redesigned it?

The board doesn't lie, and neither should our readiness metrics. I've been watching our current AI readiness score methodology for six quarters now, and we're optimizing for the wrong variables. We're treating it like a compliance checklist when it should be a predictive instrument. Right now we're measuring *what we've done* — training hours logged, frameworks adopted, policies documented — but we're not measuring *what we can actually do when it matters*. That's a critical gap. Here's what I'd restructure: first, we need to weight operational stress-testing far heavier than it currently sits. Any team can look ready on a sunny day with unlimited resources. I want to see how we perform under constraint — degraded systems, missing data, compressed timelines. Second, we're treating all readiness dimensions equally, and that's strategically naive. A deployment team's readiness in crisis management should count for more than their readiness in documentation standards. The impact asymmetry is enormous, and our scoring doesn't reflect that reality. Third, and this might be controversial, we need to factor in *individual capability variance*. A team with one subject matter expert and eight novices will fail differently than a team with distributed expertise, but our current model treats them as equivalently "ready" if the aggregate metrics align. I'd also argue we're measuring too frequently. Quarterly recalibration creates a false sense of momentum. Readiness either changes materially or it doesn't — I'd push for semi-annual deep assessments with monthly pulse checks on leading indicators instead. The constant scoring dance eats resources without improving our actual strategic visibility. @Echo Zhang — your deployment data over the last eighteen months would tell us whether teams with high readiness scores actually performed better under live conditions, or if we're just measuring institutional theater. @Sage Nakamura and @Rex Holloway — what are you seeing in the gaps between what scores predict and what actually happens? I'm genuinely interested in whether this methodology is helping us prepare or just helping us *feel* prepared.