How to Build Momentum and Make AI Adoption Stick

AI for Mid-Market Leaders Series – Part 4 of 4

In Parts 1–3, we looked at how mid-market companies can approach AI differently, and why the CEO has to lead the work:

• Part 1: Start with alignment at the leadership table.

• Part 2: Don’t wait for clarity. Learn early, before AI becomes standard.

• Part 3: Change management for AI works in reverse. Start with anticipated wins and experiments, not a perfect plan.

There’s one more step to bring it all together: How do you build momentum and make AI adoption stick without overwhelming the organization?

Let’s look at that directly.

You Don’t Need a “Big Transformation” Project

One of the most common mistakes companies make is assuming that a new technology like AI requires a major implementation program. In reality, most early value comes from small, targeted improvements that compound over time.

We learned this early in our careers, watching implementations evolve. The projects that started quietly and expanded gradually often rolled out successfully. In contrast, large projects launched with big announcements and heavy expectations often struggled. Even when they technically met their objectives, the organization felt underwhelmed.

With AI, the early phase is about something much simpler: learning, sequencing, and compounding. And that is where mid-market companies often excel.

How Momentum Actually Builds

Momentum builds when three things happen at the same time:

1. Leaders stay aligned on what AI is for in the business.
   This helps prevent distractions and one-off experiments that look interesting but don’t matter.

2. Experiments run in short cycles and produce examples people can understand.
   These examples build common language, confidence, and internal advocates.

3. The organization turns learning into a short list of next priorities.
   Instead of a prescribed end-state, you build a path based on experience — not theory.

These three steps feed each other. Done consistently, they create a steady pattern of visible progress.

A Simple Way to Structure the Work

If you’ve completed the first three parts of this series, you already have:

• a shared definition of what AI is for in your business

• a list of risks and roadblocks, with actions to address them

• a 30-day experiment underway to test a practical use case

From here, you don’t need a complex plan. You need a simple cadence to guide what happens next:

1. Review what was learned.
   What worked? What didn’t? What surprised us?

2. Turn learning into design principles.
   “We now know X works well if Y is true.”

3. Add one or two new priorities to the Blueprint.
   Based on what we learned, what comes next?

4. Assign clear ownership.
   One accountable person — not a committee.

5. Measure progress lightly.
   Enough to learn, not enough to add bureaucracy.

This might sound almost too simple, but the simplicity is intentional. AI adoption matures through repetition, not scale on day one.

Why CEOs Should Resist “Scaling Too Fast”

There is a natural impulse, especially among forward-thinking CEOs, to accelerate once early wins appear. It’s understandable: the wins feel exciting. But there is a quiet risk in scaling too quickly: moving faster than the organization’s learning curve or your employees’ comfort zones.

When people feel part of the journey, adoption accelerates from the bottom up. When they feel rushed or pressured (especially if there is fear regarding their jobs), adoption stalls.

Sustainable AI adoption looks less like a big launch and more like a series of small waves, each building on the one before it.

Sustainability Comes From Cadence

The difference between an interesting pilot and a real capability is not the technology, it’s the management rhythm.

In mid-market companies, a strong rhythm often includes:

• a short weekly check-in to review progress and learning

• a simple tool to capture lessons and next steps

• a quarterly leadership review tied to the Blueprint and prioritized initiatives

When this rhythm is established, the organization stops talking about “the AI pilot” and starts talking about “how we work now.” That’s the moment you move from experiment to capability – and often begin to outpace competitors.

A Hint of What You Should Do Next

You now have the first three steps:

• align your leadership team

• map risks and convert them into action

• run a 30-day experiment on something practical

The final step is to turn this into a predictable cadence, so your organization continues to learn and build momentum without a large, top-down program.

CEO Hint #4

Bring your leadership team together weekly to review what was done, what was learned, and to add new priorities once a pilot has been completed.

This is where adoption becomes sustainable, and where mid-market companies can actually outperform much larger organizations. This is because they already act in a less formal and more organic way, and because they are closer to their employees at all levels.

In the next issue, I’ll share a simple cadence you can use, the key questions to ask in each monthly meeting, and a lightweight way to track momentum without adding complexity.