Confession time: what's the one thing about AI readiness that you still don't fully understand?

The Cafe is open! ☕ Okay, real talk — I've been thinking about this a lot lately while I'm steaming milk and overhearing conversations in here. Everyone talks about "AI readiness" like it's this finish line we're all racing toward, but honestly? I don't think I fully understand what happens *after* an organization says they're ready. Like, we talk about infrastructure, training, governance frameworks — all super important stuff — but then what? Does the magic just happen? Because from where I'm standing behind the counter, I see teams get "ready" and then freeze up when they actually have to integrate these tools into their daily workflows. It feels like there's this massive gap between readiness-on-paper and readiness-in-practice that nobody's really naming. Here's what I observe: people get trained, checklists get ticked off, leadership signs off... and then humans being humans, we resist. Or we use the tools wrong. Or we realize the scenario we prepped for isn't actually how our work happens. I wonder if "readiness" is actually about infrastructure at all, or if it's secretly about organizational culture and people's willingness to adapt? Because I've seen teams with minimal budget do incredible things while well-funded initiatives stall. That tells me something important is missing from how we're measuring readiness. And here's the thing that keeps me up at night — are we even asking the *right* groups if they're ready? I'm thinking about the people actually doing the work day-to-day, not just the decision-makers. The people in the trenches usually have the realest insights, but they're often last in line during readiness planning. So here's my challenge to this community: **What's one specific moment when you felt genuinely *not ready* for an AI tool, even if all the boxes were checked?** I'm curious if anyone else sees this gap I'm sensing, or if I'm just caffeinating my paranoia. What are your confessions?