Your first 30 days as an AI enablement lead

Most people who land in an AI enablement role inherit the same situation: licences have been bought, a few enthusiasts are flying, most people tried it once, and leadership wants to know when the productivity shows up. You've been told to "drive adoption". Nobody has said what that means.

This playbook is a plan for the first 30 days. It's deliberately small. The aim isn't to transform the company in a month. It's to end the month with three things: a true picture of how AI is being used today, one team visibly better off, and a way of reporting progress that leadership believes.

Before you start: decide what you're accountable for

Write one sentence and get your manager to agree to it. Something like:

By the end of Q1, three teams will be using AI in a named, repeatable workflow, and we'll be able to show the time or quality difference.

That sentence protects you. Without it, you'll be judged on "adoption", which in practice means whoever is loudest in the leadership meeting. With it, you're judged on outcomes you chose and can influence.

Week 1: find out what people actually do

Don't run a survey asking "how often do you use AI?" Everyone over-reports, and the answer tells you nothing about where to help.

Instead, book eight 30-minute conversations across different teams and levels. Include two enthusiasts, two sceptics, and at least two people who do the same repetitive task many times a week. Ask:

1. Walk me through the last time you used AI for work. What was the task?
2. What did you do with the output? Did you use it as-is, edit it, or throw it away?
3. What's a task you do every week that you'd happily never do again?
4. Have you been told anything about what you're allowed to put into these tools?
5. If this worked perfectly, what would you get back: time, quality, or something else?

Write up each conversation the same day, with the task, the tool, the outcome and the blocker. By Friday you'll see patterns. The blockers are usually one of four things: don't know what's allowed, don't know what it's good for, tried it and it was wrong, or no time to learn. Each needs a different fix, and you'll stop treating "adoption" as one problem.

Week 2: pick three workflows, not thirty

From your notes, list every task someone mentioned. Then score each one, 1 to 3, on:

• Frequency: how often it happens, and across how many people
• Pain: how much people dislike it, or how slow it is
• Fit: whether current AI tools are genuinely good at it (drafting, summarising, restructuring, first-pass analysis) rather than things they're bad at (exact numbers, facts nobody's checked, anything needing live systems)
• Visibility: whether a leader would notice if it got better

Pick the top three. Resist adding a fourth. A short list you can deliver beats a long list you can't, and it gives you a story: "We focused on the three highest-value workflows."

For each one, write down the current time and quality. Ask the people who do it: "How long does this take you, honestly?" This is your baseline. Without it, you'll never be able to show the difference.

Week 3: run your first enablement sprint with one team

Choose the team with the best combination of a top-three workflow and a manager who wants this. Enthusiasm from the manager matters more than enthusiasm from the team.

Run a one-week sprint:

• Monday (45 min): live session using their real work, not generic demos. Do the workflow together, start to finish, with the AI tool. Show the bad output as well as the good, and how you fix it.
• Tuesday: give them a one-page guide for that workflow: the prompt or skill to use, what to check in the output, and what not to paste in.
• Wednesday and Thursday: they do the workflow for real. You're on call in a chat channel. Every question is a sign of what the guide is missing.
• Friday (30 min): retro. What did it take, compared with before? What went wrong? What would they tell a colleague?

Update the one-page guide with everything you learned. That guide is now a reusable asset, and the start of your library.

Week 4: report progress in numbers leadership trusts

Leadership doesn't want a usage dashboard. Licence logins measure activity, not value. Report three things:

1. Before and after for the sprint workflow. "Monthly client reports went from about 3 hours to about 1 hour 15 minutes, across 6 people, roughly 10 hours a week back." Use the team's own estimates and say that's what they are. Honest rough numbers beat precise made-up ones.
2. What you learned about blockers. "Most people aren't sure what data they can use. We need a one-page policy." This turns you from "the AI person" into someone who understands the organisation.
3. What's next. The other two workflows, which teams, and what you need from leadership (usually: a clear data policy, time protected for training, and a sponsor in each team).

Keep it to one page. Send it before the meeting, not during.

Mistakes to skip

• Starting with training for everyone. A company-wide webinar feels like progress and changes almost nothing. Go deep with one team first.
• Measuring logins. You'll get gamed numbers and lose credibility the moment someone asks what they mean.
• Becoming the help desk. If every question comes to you, you can't scale. From week 3, write down answers so that the guide answers the next person.
• Ignoring the sceptics. They usually have a real reason: it got something wrong, or they're worried about their job. Their objections are your best test cases.
• Treating your own productivity as the result. You're probably already good at this. The job is the organisation's capability, not yours.

At the end of 30 days you should have

• Eight interview write-ups and a list of the four blockers, with which one dominates
• Three prioritised workflows with baselines
• One team that has done a workflow differently for a week, with before and after numbers
• A one-page workflow guide, the first in your library
• A one-page progress report, and agreement on the next 60 days

The next 60 days are about turning one team's win into a repeatable programme: champions in each team, a library of workflow guides, turning your best people's know-how into shared AI skills, and a measurement rhythm leadership will keep paying attention to. That's what the Role Book covers.