Fractional AI Leadership
Your team can build. AI still needs an accountable leader.
When AI work spreads across initiatives without one accountable leader, good teams still stall short of production. I lead across architecture, delivery, governance, and measurable results.
Book a callWhat this is
I lead your AI capability across initiatives, teams, standards, governance, and investment decisions. I set the architecture, install the delivery discipline, and stay hands on where it counts. A monthly retainer, from about $7k per month.
Player coach. I lead, and I still build. The starting scope is a defined leadership charter, roughly one day per week. I own the capability without requiring a full time executive hire, and I am accountable for whether your team ships at all.
The shape of this engagement
Scope. The capability itself. What gets built, in what order, to what standard, and whether the investment is returning anything.
Delivery. Your teams build. I lead them, set architecture and delivery standards, and stay close enough to the implementation to know whether it will hold. A player coach, not an advisor with a deck.
Complete when. Your organization owns the capability and no longer needs me in the seat.
Not this if you have one defined thing to build and no team to lead. That is Project Delivery.
The gap this closes
Your engineers can build. That is usually not in question. What is missing is the layer above the building: someone deciding what is worth building across initiatives, what production requires of it, how it gets reviewed and released, and who owns it once it is live.
Without that layer, good engineers produce good prototypes, and the prototypes pile up. The pattern repeats, and it looks like an AI problem. It is a delivery problem, and delivery is a discipline you can install.
That is a narrower skill than building, and far fewer people have it. It is why this seat costs what it does.
The record behind the seat
I led a 50 engineer organization across three continents to 13 consecutive on time releases while cutting defects by more than half. Today I design and operate production AI systems myself.
That is the combination this engagement requires: delivery leadership with enough technical depth to set direction, challenge weak assumptions, and know whether the work will hold in production.
We start by agreeing on the leadership charter, the first production outcome, and how it will be measured.
See the full record →How I lead
I set the architecture and standards, and I write them down. I help leadership decide what is worth building and how it will be measured. I put a review and release cadence in place so shipping is routine instead of heroic. I take the hard pieces myself when that is the fastest way through. I work with the team you have, and my job is to leave them better at this than they were.
Map to Prod runs the same way at this depth as at every other. The difference is that your team runs it, and I am accountable for whether they can.
On what I will build, and what I will not
Technology should make people more valuable, not replace them or watch them. That principle decides which work I take and how I lead a team through it.
In practice: metrics judge the system, never the person. No surveillance tooling. A human stays in the loop wherever the call requires judgment. If the goal of a piece of work is to strip the dignity out of somebody's job, I will say so plainly and I will not build it.
This is the seat where that shows up most, because leading a team means setting what the team is pointed at.
Start with a call
Bring the team you have and the capability you are trying to build. If a full time hire is the right answer, I will tell you that.
Book a callThe call is free. Bring the problem and leave with a straight answer.