Scaling AI wins from pilot to payoff

Hi there,

We’ve all seen it happen. A company’s innovation team pulls off an impressive AI pilot. The demo gets a round of applause in the boardroom, everyone feels good about being on the cutting edge, and then… nothing. The project quietly fizzles out, never making a real difference.

This is the innovation theater trap. It’s easy to get a small-scale AI experiment working. The real work starts when you try to scale it into something that actually helps your business. A successful pilot isn’t the finish line. It’s the starting gun.

The problem with pilots

Too many companies treat AI pilots like science experiments. They give a small team a small budget to prove a concept in a controlled environment. Once the team gets a win, the project is considered a success, and everyone moves on.

But this approach misses the point entirely. It ignores the messy reality of integrating new technology into existing workflows, getting people on board, and making sure it can handle real-world demands.

Thinking bigger from day one

If you want to avoid the pilot trap, you have to think about scale from the very beginning. It’s less about the tech and more about the mindset.

First, start with a real problem, not a cool tool. It’s tempting to grab the latest AI model and look for ways to use it. Don’t. Instead, find a genuine business challenge and then figure out if AI is the right way to solve it. A clear purpose is the foundation for any project that lasts.

For example, in my book, I share a case study with JLL, the global real estate giant. Their challenge wasn't just to "use AI," but to solve a real, long-standing business problem: extracting valuable insights from their massive workforce. That clear focus was the key to their success.

Next, build for integration, not isolation. Think about how this new tool will connect with the systems you already have. An AI solution that can’t talk to your other software is just a fancy gimmick. Plan for how it will fit into your existing tech stack from the start.

Finally, design for your people. The best AI model in the world is useless if your team finds it confusing or untrustworthy. You have to involve the people who will actually use the tool in the design process. Your technology should adapt to your team, not the other way around.

A practical path forward

So how do you move from a successful test to a solution that works across the company? That's why in my book, I offer a simple, four-step framework I call LEAP: Locate, Evaluate, Action Plan, and Progress.

Once you’ve proven the value of a pilot, the first step is to standardize and document the process. This makes it much easier to replicate your success. Then, create a phased rollout plan. Trying to do everything at once is a recipe for disaster. A gradual rollout lets you gather feedback, make improvements, and build momentum.

I break this down into four simple phases:

• Phase 1: LEAP (“Birth”): This is the initial three-month cycle where you develop a prototype and a plan.

• Phase 2: Validation (“Crawl”): The next three months are focused on testing and validating the solution with a small group of users.

• Phase 3: Improvement (“Walk”): This phase is all about iterating and improving the solution based on user feedback.

• Phase 4: Full Validation (“Run”): In the final phase, you scale the solution across the organization.

Most importantly, you need to measure what’s working and what isn’t. Set clear goals and track your progress. Use that data to learn and adapt as you go.

Forget trying to predict the future perfectly. The companies that win are the ones built to spot opportunities and adapt quickly. That’s how you create an immunity to luck, where you’re ready for whatever comes next. The market rewards those who build and act. By moving past the hype and focusing on real impact, you can make sure your AI initiatives deliver.

Much Love,
Matt

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