The Difference Between AI Hope and AI Strategy

In this episode of The Science of Excellence, I sat down with Carlee Wolfe, AVP of Talent and Organizational Effectiveness at Hyatt Hotels. She also serves as a member of the Baltimore Workforce Development Board.

We talked about why most AI strategies are just "implement and hope," how to move from tool thinking to systems thinking, and why competing on efficiency alone is a race to the bottom.

These 5 insights stood out:

• Hope Is Not an AI Strategy
• Stop Solving One Problem at a Time
• Experiment Before You Scale
• Lead with Curiosity, Community, and Care
• You Can't Win by Cutting

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1. Hope Is Not an AI Strategy

In Carlee's Words: "How are you thinking really intentionally about what your future workforce looks, sounds, feels, operates like versus, well, I hope that we're learning some skills and then I hope they use the tools. It's not that companies are overtly saying our measure will be hope. It's more the absence of what are we really trying to shift and how do we measure success around that."

Most companies aren't explicitly choosing hope as a strategy, they just simply land there by default.

The classic move is launching an AI 101 class and assuming people will figure it out from there. Carlee pushes teams to start from the other end, assuming they know AI 101 and asking what would you want them to do next and what mindset shift are you trying to build. If you can't answer that, you're operating on hope. It's the difference between intention in a new environment and doing what you've always done while expecting different results.

‍2. Stop Solving One Problem at a Time

In Carlee's Words: "When you walk into a gym there's all the machines. Front of the gym. One solution. I'm gonna work on my biceps, one machine. Then there's the back of the gym. People are climbing walls, doing jump rope, throwing medicine balls. It's open and dynamic. You could do holistic fitness with just one medicine ball. The thinking is so different because you're not using it in one direction, one solve. How do we get out of app thinking into systems thinking?"

Most organizations still think in terms of one tool for one problem. The shift is thinking in systems where one tool solves dynamically across the business.

Take performance reviews. Tool thinking says buy an app that solves it, while systems thinking asks: what's the real outcome we're after? Hyatt wants managers to give specific, confident feedback, so they're exploring building a GPT that helps managers prepare for coaching conversations, practice delivery, and curate talking points. Instead of completing the task for the manager, it’s building the skill. That's the difference between solving a single problem and creating something that compounds over time.

3. Experiment Before You Scale

In Carlee's Words: "We could go buy that off the shelf, but we said let's experiment. Let's see what we can learn and do right now. We could do that over the next couple months and there's value to gain. I also don't have to buy the particular AI product right now, but I know we have something internally we could build ourselves. That just wouldn't have been possible before. We would've needed an engineer to build code for that."

You don't need to always wait for engineering anymore, the prototype can now be the product.

Buying has its place, but the instinct to jump straight to a polished, scalable solution is worth questioning. Hyatt started small within the team, learned what worked, then expanded. Unless you're a complete junior, you've been trained to only ship things that look finished, which is no longer necessary. You can learn along the way and still deliver value. This also shifts how teams solve business challenges locally instead of waiting for a centralized solution that assumes everyone has the same problem.

4. Lead with Curiosity, Community, and Care

In Carlee's Words: "Curiosity is about what if, what's that, have we tried that, what don't I know today. Community is about understanding connections and building support because things are changing quickly and people need each other. And care is understanding the needs of folks on your team. You might need something different than the other person. Leaders understanding those differences and leaning into support is huge."

In a time of uncertainty and constant change leaders need curiosity, community, and care as their foundation.

Hyatt cares for people so they can be their best, which extends to how they approach AI. Hyatt's AI strategy is rooted in adding capacity and capabilities. They're not sending the message of "eliminate 10 things from your day." Care also means personalizing support. Some employees respond better to feedback in the morning, some prefer formal settings over informal. Leaders need to understand those differences instead of treating everyone the same. Community matters just as much right now. Carlee taps into conferences, networks, and peers at other companies to exchange information on what's working. Nobody has this figured out alone, and the leaders making the most progress are the ones actively building those connections and learning from what others are exploring.

5. You Can't Win by Cutting

In Carlee's Words: "Everybody always knows you can't win by cutting. We just want to forget that because it's hard not to pick the thing that feels measurable and actionable. Instead of saying AI saves us a million dollars so let's take away those tasks, why aren't we looking at a financial model that says if we move these tasks out, saves us a million, why aren't we giving people a hundred thousand of that? And instead of losing jobs, are you thinking about opportunity to redeploy, to continue to innovate and add value for your customers?"

If every company competes on efficiency, everyone commoditizes themselves. Nobody wins in the medium term.

Most organizations have a long backlog of innovations they never got to. AI enables that work, but only if leaders choose innovation over cuts. Carlee sees it as a leadership imagination problem. Committing to your board that you've introduced AI and added efficiencies without actually changing anything is just hope by another name. The harder, more exciting work is dusting off those real questions about where the company could go next. That requires redeploying talent instead of announcing another round of layoffs.

Until next time,

Vince

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