Who Owns This?

There's something critical to successful AI pilots that often gets overlooked: clarity about who owns what.

Who makes sense of the results? Who helps others learn from it? Who is accountable for what happens next?

Identifying a champion is a good start, but it doesn't answer those questions.

Ownership isn't a single thing. Running a pilot, evaluating it, enabling others to adopt it, and deciding whether it should scale are different responsibilities. They might sit with one person or several, but what matters is that they’re explicit.

When they aren't, pilots can easily drift. Learning then stays trapped and capability doesn't build.

What Ownership Actually Means

Every effective pilot has clear accountability across four distinct pieces.

Someone runs the pilot. This is the person closest to the work. They use the AI tool in real conditions, test it against a real task, and iterate when the first attempt doesn't land.

Someone evaluates the outcome. This person determines whether the pilot delivered value that matters - not whether the tool produced output, but whether decisions are better, time is being redirected to higher-value work, or quality and risk are improving in meaningful ways. This requires success criteria agreed upfront and assessed honestly.

If it works, someone enables learning beyond the pilot. Others need to be able to learn from it. That means sharing what helped and what didn't, being available when colleagues try it themselves. This is how capability spreads rather than staying personal.

Someone owns the decision about what happens next. Scale it. Adapt it. Stop it. Or keep it contained because it only makes sense in a specific context. That decision needs to be informed by learning, not enthusiasm.

In small pilots, one person might cover more than one of these roles. The point isn't separation. It's clarity.

Setting Ownership Before the Pilot Starts

Define what success looks like. Not vague aspirations, but concrete signals. Time saved on a specific task. Fewer errors in a defined process. Improved quality in a particular output. Make this visible to everyone involved.

Name who owns what. Who runs it. Who evaluates it. Who helps others learn. Who decides what happens next. Get agreement and make it visible.

Set review points upfront. Pilots need an end, even if that end is a decision to continue. Without this, pilots either run indefinitely without scrutiny or fade quietly without learning.

Build peer learning in from the start. If this works, who else would benefit? Bring them close early so learning spreads as it happens rather than being retrofitted later.

When Ownership Needs to Shift

Sometimes the person who starts a pilot isn't the right person to scale it.

Working out how AI can support a complex task is different from teaching others, documenting approaches, or supporting use at volume. When ownership needs to change, make the handoff explicit. Capture what's been learned and be clear about what the new owner is accountable for.

What Leadership Owns

As a leader, you probably don't own running the pilots. But you do own the conditions that make ownership work.

Clarity about what matters. Removing obstacles. Protecting focus. Making space for the work. Amplifying success so learning travels.

Three well-owned pilots will deliver more than ten loosely defined ones.