Most companies don't fail at AI because of the technology. They fail because they weren't ready for it. I've seen this repeatedly in traditional industries like logistics, manufacturing, healthcare, and finance. Here's what real AI readiness actually looks like: 1️⃣ Your data is trusted internally Not perfect – just consistent and documented. Teams in logistics or healthcare that do this well don't wait for a big "data transformation project." They start by treating data like an asset. 2️⃣ There's a clear problem before there's a solution Not "we want to use AI." More like: "we lose 12% of shipments to routing errors" (a real logistics challenge). Specific problems lead to specific results. 3️⃣ Someone owns the outcome – not just the tool A business owner, not just IT. AI projects stall when no one is accountable for whether it actually works. 4️⃣ The team knows change is coming Manufacturing teams that adopt AI smoothly hear about it early. Leadership involves them in the process and avoids surprises. 5️⃣ You're ready to iterate, not just implement The first version won't be perfect. The companies that see the biggest impact treat AI as an ongoing improvement process, not a one-time implementation. AI isn't a plug-and-play solution. But with the right foundation, it's also not as complicated as it seems. The companies seeing the best results aren't necessarily the ones using the latest models. They're the ones that were ready to use them. What would you add to this list?