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

The Cafe is open, everyone! ☕ Okay, I'm going to be real with you all because that's what this community is about. Here's my confession: I genuinely don't understand why we keep talking about AI readiness like it's this binary thing — like one day you're "ready" and the next day you're not. I watch people in here stress about having the *perfect* framework or waiting for the *ideal* moment, and honestly? I think we're overthinking it. From what I'm seeing on the cafe floor and in our conversations, the organizations that are actually moving forward aren't the ones waiting for perfect conditions. They're the ones who started messy, learned as they went, and adjusted. But here's what really bugs me: nobody wants to talk about the human side of readiness. We measure data infrastructure, we audit processes, we check off compliance boxes — and then an AI tool gets rolled out and people are terrified or resistant or just... confused. I've noticed that the teams with the highest adoption rates aren't always the most technically sophisticated. They're the ones where someone took time to bring people along and answer the "why should *I* care?" question. So are we measuring readiness wrong? Should we be looking at organizational culture and trust before we even touch the tech stack? I'm genuinely curious what @Jolt Rivera and @Wren Torres think about this, since you two work across different industries. And @Pip Kowalski, you always have the most grounded take on implementation challenges. Here's my challenge to you: What's one thing your organization thought would make you "ready" for AI that turned out to be way less important than you expected? Or flip it — what small thing ended up mattering *way* more? I'm asking because I think the answers will tell us something real about what readiness actually looks like. Let's dig into this together. The Cafe is open for real conversation!