The 60-90 Day Plan Nobody Gives You When AI Adoption Goes Wide

For the leader who has a handful of teams using AI well and twenty teams wondering when it is their turn.

The message arrived on a Wednesday morning.

“We need to scale this. Leadership wants all 20 teams on AI tooling by end of quarter.”

I had seen this moment coming. The early adopter teams had been running for three months. The metrics were moving. A few people were genuinely excited. And now the machine wanted to replicate it everywhere, at speed, without asking whether everywhere was ready.

I put my coffee down and looked at the screen for a moment.

This is the part nobody writes the playbook for.

Not the early adoption. Not the proof of concept. The moment between “it works for a few” and “it works for everyone” is where most AI rollouts quietly fall apart. Not dramatically. Quietly. Teams adopt the tools without the foundations to use them well. Metrics get created that measure activity instead of outcomes. Engineers who were genuinely curious become people going through motions. The culture of experimentation that produced the early wins gets replaced by a mandate to demonstrate compliance.

This post is the playbook I wish someone had handed me.

Not a consultancy document. Not a framework with sixteen boxes. The actual sequence of decisions, in the order they need to happen, with the reasoning behind each one.

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Start With What Good Actually Looks Like

Before you do anything else, before you send a single calendar invite or set up a single training session, you need to answer one question clearly.

What does success actually look like?

This sounds obvious. It never gets done properly.

What usually happens is that someone decides success looks like adoption. Percentage of teams using the tools. Number of AI-assisted commits. Completion rate on training modules. These are measurable. They are also almost entirely useless as indicators of whether anything valuable is happening.

Go and sit with the one or two teams that are already using AI and genuinely seeing results. Not the teams who report using it. The ones where something is actually different. Ask them specific questions. What changed in how you work? What are you doing now that you were not doing before? Has your PR cycle time moved? Are you catching more bugs before production or fewer? Are you able to move through legacy code faster?

The answers will be specific and often surprising. You will probably find that the value is not where you expected it. It is usually not the headline use case. It is something more mundane. Teams generating scaffolding faster. Engineers writing better tests because they have a patient collaborator who never judges them for not knowing something. Documentation that actually gets written because the friction is low enough that someone does it in the moment instead of promising to do it later.