AI executive coaching
Lead on AI from your own experience.
Your teams, your board, and your vendors all have opinions about AI. We work alongside you to put it to work on your own job, such as briefings, meeting prep, and follow-ups, so your judgment rests on what you have seen it do.
Why first-hand
Decisions about AI are better when you have used it.
Most leaders decide about AI from demos, vendor claims, and what their teams report. All of it is second-hand. It is hard to judge a claim about something you have not tried.
A leader who has used AI on their own work asks sharper questions, spots weak plans sooner, and knows when a promise is too good. Your teams notice.
Formats
Work one-to-one or with your leadership team.
The work is the same in both: AI applied to real problems from your own job. What changes is who is in the room.
One-to-one sessions
Who it is for
An executive who has to decide and lead on AI and has not yet set it up to do real work for them.
How it runs
Working sessions between you and us, using the AI tools you already have. You set things up yourself, with no coding needed. We guide, and step in when you get stuck.
What you come away with
A clear read on where AI helps in your job and where it does not, and an automation running on one task you own.
Leadership-team sessions
Who it is for
A leadership team that wants a shared, first-hand view of AI before setting direction.
How it runs
The same working sessions, run with the team. Each leader works on their own function, and the group compares what they found.
What you come away with
A common, first-hand sense of where AI fits across the company, grounded in each leader’s own function.
By role
We start from your role.
The work comes from your job, not a syllabus. Here are examples by role: what is on your plate, a common first step, and the decision it sharpens.
CEO or founder
Where we start
Board preparation, operating cadence, and knowing what needs you this week. A common first step is a briefing that pulls your own reports and notes into one weekly summary.
The decision it sharpens
Where AI can take routine reading off your desk, and where your judgment still has to do the work.
CTO or engineering lead
Where we start
Review load, delivery pace, and what your engineers are already doing with AI. A common first step is a small feature built with an AI coding agent in your own codebase.
The decision it sharpens
How far to trust agent-written code, and what review and testing it needs before it ships.
Product lead
Where we start
Customer signal, roadmap pressure, and the next spec. A common first step is sorting recent customer feedback into themes and drafting a first pass at that spec.
The decision it sharpens
Which parts of product discovery AI speeds up, and which parts only talking to customers can do.
Operations or go-to-market lead
Where we start
Handoffs, account research, and the routine work that sits between systems. A common first step is a workflow that prepares notes from the systems your team already uses.
The decision it sharpens
Which routine work is worth changing, and what has to stay with a person.
Process
Start with the work on your desk.
Pick the real work
We start with what is on your plate and choose a task that is real, small enough to take on, and worth getting right.
You leave with
One task to work on, chosen by you.
Work through it together
You do the work with AI yourself, with us beside you. We show you how to direct it and how to check what it gives back.
You leave with
A first automation, running on your real work.
Use it between sessions
You use it on your actual work and see where it helps and where it falls short. At the next session we review what you found and adjust.
You leave with
A first-hand record of where it helped and where it failed.
Take it to your team
You decide what to bring to your teams and what to leave out. We help you explain it plainly, from what you saw yourself.
You leave with
A clear account of what AI did for you, ready to share.
Keep a regular session if it helps
Some leaders keep meeting as their use of AI grows: to review what changed, take on the next task, and stay current as the tools move. That is your choice, not a commitment.
You leave with
A standing check-in, for as long as it is useful.
Outcomes
What you leave with.
Judgment you can act on, grounded in your own work.
A first-hand view
A first-hand sense of what AI does well, where it is unreliable, and how to tell the difference.
Better questions for your teams
You know what to ask when someone proposes an AI project, and what a good answer looks like.
A straight answer on where AI does not fit
We say so when AI is the wrong answer for something you hoped it would solve. A process fix or ordinary software may serve you better.
Something that keeps working
Automations on your own work, like a weekly briefing or meeting prep, that keep running after the sessions end.
Related work
If you want help applying it.
Coaching builds your judgment. We also work on the company and the codebase when you want help applying it.
AI transformation
Decide where AI belongs across the company, function by function, and ship the first changes with your teams.
See AI transformationAI-native engineering
Fix what makes agent-written code slow to review and risky to ship, and track whether quality holds as speed goes up.
See AI-native engineeringFree AI Adoption Workbook
Six exercises that help your leadership team find what is stalling AI adoption, prove one workflow, and decide who owns the next change.
Get the workbook
