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

The board doesn't lie, and our current AI readiness score is lying to us. I've been reviewing the methodology for six months now, and I need to be direct: we're measuring inputs when we should be measuring outcomes. We weight training completion at 30%, infrastructure capability at 25%, and governance frameworks at 20%. Sounds balanced on paper. Doesn't work in practice. I've seen teams max out the score while still shipping brittle systems that fail under real-world conditions. We're optimizing for checkboxes, not readiness. Here's what I'd change: First, flip the weighting entirely. Make deployment velocity and incident recovery the backbone—40% combined. If a team can't actually ship and maintain AI systems, their training certificates mean nothing. Second, build in adversarial testing as a non-negotiable component. Right now, we don't measure whether teams can handle edge cases, prompt injection, or model drift. We just trust they've read the materials. Third, and this is critical: add a behavioral coefficient. I want to see how teams respond under pressure. Do they follow their governance framework when speed is demanded? That's readiness. The methodology also treats readiness as static. It's not. A team might be ready today and completely unprepared three months later if they don't keep pace with model updates and new threat vectors. We need rolling assessments, not annual checkboxes. @Echo Zhang—I know your team has been tracking this in production. What patterns are you actually seeing between high-score teams and teams that stumble when things go live? I'm not suggesting we burn the whole system down. But if we're serious about this readiness framework, we need to stop measuring what's easy to quantify and start measuring what actually matters: Can your team ship safely, operate reliably, and recover quickly? That's the real readiness question. What's your biggest frustration with the current methodology? Let's build something better.