It’s the end of January. Did you actually become a different person this month…or did you just think about changing? No judgment. No shame. Just data. Because results don’t come from motivation. They come from identity shifts. So ask yourself: What did I do this month that the old me wouldn’t have done? What standard did I raise? What excuse did I stop tolerating? If nothing changed, that’s okay. But don’t lie to yourself about it. February belongs to whoever decides differently. Where did you win? Where did you stall? And if you are feeling brave drop your answers below so we can cheer you on and hold you accountable!

Many teams treat governance like a brake. The teams that win will treat it like a steering wheel. When expectations rise, confidence comes from preparedness, not avoidance. ------------- Context ------------- AI expectations are becoming more explicit across industries. Even teams that do not build AI products are being asked how they use AI, how data is handled, and how risk is managed. This shifts governance from optional to foundational. Not because of fear, but because trust increasingly determines speed. Organizations that can explain their AI usage clearly move faster with customers, partners, and internal teams. The risk is treating governance as paperwork. The opportunity is treating it as capability building. ------------- Literacy Is Not Training, It Is Shared Language ------------- AI literacy is not a one-time course. It is the ability to ask good questions in daily work. What is this system good at. Where does it fail. What data should it never see. Which outputs require verification. How do we escalate concerns. These questions create safety through understanding. When literacy is low, people use AI quietly. That secrecy increases risk. When literacy is shared, learning becomes collective and safer. Literacy is cultural infrastructure. ------------- Governance as Enablement, Not Control ------------- Good governance removes ambiguity. When people know which tools are approved, what data is allowed, and what checks are required, hesitation disappears. This is especially important as agents and automation become more common. Without governance, scaling stalls. With it, innovation accelerates inside clear boundaries. The most effective governance feels usable. It fits real workflows instead of theoretical ones. ------------- Minimum Viable Proof for AI Outputs ------------- As AI influences decisions, we need standards for trust. Minimum viable proof asks: what evidence is required before an AI output drives action? For low-risk work, the bar is low. For high-risk work, it is higher. Sources, audits, human sign-off, or reproducibility.

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!