We recently wrapped our tenth practice rollout — a milestone worth a short reflection rather than a victory lap.

Ten engagements have given us enough perspective to see patterns that aren't always obvious at the beginning of an AI implementation. The most important one is simple: successful adoption rarely starts with the biggest possible transformation.

The practices that saw the fastest results weren't necessarily the ones with the most ambitious AI plans. They were the ones willing to start narrow, identify a specific operational bottleneck, and build around it.

Start With One Workflow

It's tempting to look at AI and ask how many processes can be automated at once.

In practice, that approach can create more problems than it solves.

A single workflow that is well designed, tested, and adopted by the team can create more value than a broad implementation that touches everything but changes very little day to day.

Starting small also gives teams something important: evidence.

When staff can see a measurable improvement in one part of their workflow, it becomes much easier to evaluate where automation could make sense next.

Adoption Is Part of the Implementation

Technology is only one part of the equation.

A new system still has to fit into how people actually work. If a process requires too many changes, adds unnecessary steps, or isn't clearly better than what came before, even a technically capable solution can struggle to gain traction.

The strongest implementations we've seen treat adoption as a design problem from the beginning — not something to address after the technology is already in place.

What We're Taking Forward

Our tenth rollout doesn't change our approach. It reinforces it.

Start with the problem. Choose the smallest useful intervention. Make sure the team can actually use it. Measure what changes. Then decide whether there's a reason to go further.

We'll continue sharing what we learn as we go — not just the polished outcomes, but the practical lessons underneath them.

The goal isn't to implement more AI. It's to make each implementation genuinely useful.