The AI Advantage team spent a week testing Claude Cowork, and in this video, I break down what worked and what didn't in our testing. If you've been wondering exactly how you should be using Claude Cowork, this is the video for you!

A lot of teams are excited about “AI agents” because it sounds like work will finally run itself. Then reality hits, pilots drag on, trust stays low, and we quietly retreat back to chat prompts. The gap is not intelligence, it is operational readiness. ------------- Context ------------- Across the AI world right now, agentic AI is moving from demos into real organizational workflows. Many teams are experimenting with agents for IT tickets, customer support, reporting, and internal coordination. On the surface, the technology looks capable. Underneath, progress often slows. In practice, this usually looks like a clever prototype that performs well in controlled environments but struggles the moment it touches real data, real edge cases, or real accountability. What felt promising in a sandbox becomes risky in production. So the initiative stalls, not because it failed, but because no one is confident enough to let it scale. What is striking is that the bottleneck is rarely model quality. Instead, it is ambiguity. Unclear decision rules. Undefined escalation paths. Inconsistent processes. Agents are being asked to act inside human systems that were never designed for clarity. That mismatch creates friction. Agentic AI does not just expose technical gaps. It exposes organizational ones. When we see pilots stall, we are often looking at unresolved human decisions, not unfinished AI work. ------------- The Autonomy Ladder ------------- One common pattern behind stalled agents is skipped steps. Teams jump from “AI can suggest” straight to “AI should act,” without building confidence in between. A more sustainable approach is an autonomy ladder. At the lowest rung, AI drafts, summarizes, or organizes information. Next, it recommends options and explains its reasoning. Then it performs constrained actions that require approval. Only after evidence builds does it earn the right to execute end-to-end actions independently. When we skip rungs, every mistake feels catastrophic. When we climb deliberately, mistakes become feedback. The difference is not technology. It is expectation management.

Sometimes you’ll get the win. Other times you’ll get the lesson. That’s it. Not everything turns into a highlight. Not every effort turns into a payoff. But none of it is wasted. Because lessons turn into better decisions. Better decisions turn into better execution. Better execution turns into wins. Most people quit in the lesson phase because it feels like losing. It’s not. It’s just part of the process. So this week: Try the thing. Have the conversation. Make the offer. See what happens. Adjust. Go again. Quick check-in: What’s one uncomfortable action you will commit to taking this week?