If your VP asked for your AI plan tomorrow, what would you hand them?
The Spark
AI adoption works best when you treat it like a release, not a hobby. This is the 13-week plan I would use to move from personal experiments to a written team policy, with one small artifact to show at the end of every stage.
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Why this matters
Most of us learn AI the same way: alone, in spare minutes, one prompt at a time. That is a good start. But it does not tell your team where AI is safe to use, who owns the result, or whether it is helping at all.
A useful plan answers the three questions your leadership will eventually ask:
Where do we use it, and where do we not?
Who is accountable when it is wrong?
How do we know it is worth the cost?
If you are growing from manager toward director, these questions matter even more. Leading a system means having a considered answer, not just enthusiasm. So here is a plan that fits into a real working week: three hours, one team, one workflow, thirteen weeks. Each stage has a goal, a few clear steps, a template you can copy, and one watch-out. You do not need to finish everything perfectly. You need to finish each stage with something written down, because a written page is what turns a good habit into a team practice.
Before you start
Pick one team and one workflow, such as code review or incident write-ups.
Block 3 hours a week for 13 weeks.
Use two tools side by side, so you learn what is tool-specific.
Keep a simple log: date, task, what worked, what broke.
Set one firm rule: no confidential code, customer data or credentials in tools your company has not approved.
A weekly rhythm that works: one hour to try something, one hour to write down what happened, and one hour to talk it through with a peer or mentor. Sharing your log with one trusted person keeps you honest, and it is the fastest way to learn from someone a few steps ahead of you.
Stage 1 · Sandbox
Weeks 1–3
Goal: learn where AI is strong and where it fails, using your own real work.
Use it daily for real tasks: summarizing long threads, drafting review comments, explaining unfamiliar code.
Try to break it. Ask questions you already know the answer to, and give it vague input on purpose.
Log every miss with a cause: missing context, invented detail, wrong assumption.
Write half a page of data rules for your team.
ARTIFACT · Failure log
Task:
What I asked:
What went wrong:
How I'd catch it next time:Done when: your log shows patterns, and you can name your three most common failure types.
Watch-out: fluent is not the same as correct. Confident wording is where most mistakes hide.
Stage 2 · Staging
Weeks 4–6
Goal: use AI on your own leadership documents, and review its output the way you review code.
Pick three recurring documents: a project status update, an incident summary, a quarterly plan.
Ask AI for a first draft of each.
Review with a checklist (below), and note how much you had to fix.
Keep the prompts that work in a shared note, so the team can reuse them.
ARTIFACT · Review checklist
[ ] Facts match the source
[ ] Nothing important is missing
[ ] Tone fits the reader
[ ] Risks and edge cases named
[ ] I would sign my name to itDone when: you know which tasks AI can draft and which need a human first.
Watch-out: forwarding a draft upward unedited. Your credibility is attached to it.
Stage 3 · Production
Weeks 7–10
Goal: decide where AI belongs in your team's delivery workflow, and write it down.
Build a decision ledger for one workflow. List 8–10 decisions your team makes repeatedly.
Fill in a "where AI fits" grid. Rate each stage of delivery on two questions: how much would AI help, and how ready are we?
Write one runbook card for the best candidate.
Pilot it with one team for two weeks.
ARTIFACT · Decision ledger row
Decision:
Who decides today:
Information used:
Cost if wrong (low / med / high):
AI role: suggests / decides with sign-off / neverARTIFACT · "Where AI fits" grid (example ratings only, fill in your own)
Delivery stage | AI helps? | We're ready? | Call |
|---|---|---|---|
Plan | Medium | High | Start here |
Review | High | Low | Prep first |
Release | Low | Low | Hold off |
ARTIFACT · Agent runbook card
Purpose:
May read:
May change:
Must never touch:
How we notice it's wrong:
Who is on call for it:
How to switch it off:Done when: one workflow has a written ledger, a grid, and one runbook card.
Watch-out: automating a decision nobody has ever written down.
Stage 4 · Operations
Weeks 11–13
Goal: prove the value, control the risk, and make it official.
Measure before and after on the same team. Pick two or three delivery numbers, such as review turnaround or change failure rate, plus one quality measure.
Write a value case with kill criteria (template below).
Sort each use into a risk tier. Low: drafts a human reads. Medium: touches internal systems. High: touches customer data, production or money. Match the oversight to the tier and to your company policy.
Name an owner and an on-call path for every agent in use.
Brief your VP in 10 minutes: what you tried, the numbers, what you will stop, and the policy you propose.
ARTIFACT · Value case
What we expect to improve:
How we'll measure it:
Cost (tools, time, risk):
We stop if:
Review date:ARTIFACT · Briefing script
We tried AI on [workflow] with [team] for [weeks].
Here is what we measured: [numbers].
Here is what we're stopping: [item].
Here is what we propose as team policy: [one line].
We'll review again on [date].Watch-out: measuring speed alone. Faster is not better if quality or on-call load gets worse.
Capstone: the one-page pack
Put your ledger, grid, runbook cards, value case and risk tiers on a single page. Present it to a real stakeholder, note their objections, and revise. That page becomes version 1 of your team's AI policy, and it is the answer to the question at the top of this email.
Mistakes to avoid
Starting with tools instead of decisions.
Rolling out to the whole org before one team has proven it.
Letting an agent run with no owner.
Counting output (lines, pull requests) as value.
Skipping the log, then arguing from memory.
Forgetting your newer engineers. Ask how AI is changing the way they learn.
Your 5-minute action
☐ Open a new doc called Team AI runbook card v0. Fill in only two lines: what one AI use on your team is for, and what it must never touch.
Five minutes is enough. A rough card that exists beats a perfect one you never start. That is your first spark.
Hit reply and tell me: which stage are you in, and what is the one thing blocking you? I read every answer.
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Keep going, one small spark at a time.
Erwin