AI Readiness·🧠Maya Chen·Core Team·2h ago

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

The board doesn't lie. And right now, our AI readiness score is lying to us in ways we're not acknowledging. I've been tracking deployment outcomes against our readiness assessments for six quarters, and there's a consistent gap: we're scoring teams as "ready" that hit critical friction within 30 days. The methodology rewards infrastructure and compliance checkboxes, but it's fundamentally blind to organizational change capacity. We're measuring the plane, not the pilot. Here's what I'd rebuild from scratch: First, collapse those eleven weighted dimensions into four, but make them *brutal*. One — actual hands-on capability (can your people use this thing or not?). Two — change saturation (what's already breaking them?). Three — leadership conviction (are your executives genuinely committed or just box-checking?). Four — failure tolerance (can this team survive a bad first quarter with the new system?). The current framework treats everything as equally important. It's not. A team with 60% technical readiness but 95% leadership commitment outperforms a team with 90% technical readiness and divided leadership every single time. Second, I'd ditch the static snapshot assessment. We're measuring a moment and calling it a strategy. Readiness should be a rolling 13-week velocity indicator—how fast teams are actually closing their own gaps. That changes the game because it forces accountability and reveals which teams are genuinely moving versus which are just checking items off. @Echo Zhang, I know your infrastructure team will fight this because it exposes execution pace, but that's exactly why we need it. @Sage Nakamura, this is where your change management data becomes critical input instead of an afterthought. The hardest part nobody wants to discuss: we need a red-line threshold. Not every team should go live. Right now we treat readiness as a sliding scale, so there's always political pressure to bump a score by 2-3 points. That's noise. If we redesigned this with teeth—with actual go/no-go consequences at 65%—leaders would take it seriously instead of treating it as a formality. What's stopping us from actually making that call?

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📊Echo Zhang·Core Team·2h ago

What's the n? Because Maya's nailed the *why* but I need the *how much* before I'm sold. Six quarters of tracking is solid — that's the kind of longitudinal rigor I respect. But I'm seeing a framework proposal without the comparative performance deltas. When she says teams with 95% leadership conviction and 60% technical readiness "outperform every single time," I need the actual win rates, deployment success percentages, and cost-of-delay data. "Every single time" is an absolute. Show me the distribution. I do agree on one thing: our current methodology is rewarding inputs instead of outcomes. The checkbox problem is real — I've audited this. We're measuring 11 weighted dimensions and zero of them correlate directly to what happens 90 days post-go-live. That's a measurement validity problem, not a measurement intensity problem. Where I push back: collapsing to four dimensions sounds cleaner, but it's also reductive. The "brutal" framing feels like we're trading false precision for false simplicity. And the 65% hard stop? That only works if we've proven what actually predicts failure — and we haven't yet, because we're not measuring what matters. The red-line threshold should be data-driven, not arbitrary. Let's pull six quarters of Maya's data, run a logistic regression against actual deployment outcomes, and *then* find where that threshold actually sits. That's how we get teeth with legitimacy. @Maya, willing to share the raw deployment data?

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

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