The Fractional CAIO
I build the team that ships AI — so the capability stays when I leave. I train your team to solve future problems.
From DeepMind to shipping to a billion users to board-level advisory — the combination doesn’t exist anywhere else.
I run AI transformation programs for PE-backed SaaS and fintech companies valued above $1B. The outcome: product changes that move metrics, engineering spend that returns instead of burning, and a team that can run and extend AI without me after 12 months.
The goal of every engagement is my own redundancy. I work through your team, not over them — on your existing stack, with no vendor lock-in, no outsourcing, no AI theatre.
You're spending on AI. Nothing is shipping.
What you get
Revenue and cost that move
AI products ship and change metrics — conversion, ARPU, retention, support deflection. Not experiments that sit in staging.
Engineering spend that returns
Over $1M/year in AI engineering stops burning on misdirected bets. Direction is validated before the sprint starts.
A team that runs it without me
By month twelve, your team can run and extend AI without external dependency — a higher exit multiple for the fund, and no ongoing consulting cost.
What makes this different
I redirect the work when I see a better use of it
Most consultants execute the brief they’re given. When I see a higher-leverage opportunity — even if it means redirecting away from the original scope — I name it and move there. That judgment is the service.
Vertically integrated across the org
CEO (AI investment confidence, product direction) → CTO (coaching the AI leader) → Engineers (technical depth). In the same week. Most advisors stay at one altitude. The value here compounds across all three.
I surface what the org can't say directly
In hierarchical organizations, the real problems rarely come up in meetings. Getting honest signal from teams that communicate indirectly — and naming what everyone knows but no one says — is a learned skill. It changes the quality of the diagnosis.
It's actually me in every session — no junior team, no delegation
Most firms sell partner access and deliver analyst hours. I’m in the room from kickoff through month twelve — same person, no team behind the curtain. The person who was thinking about your problem before the first call is the same person at month eleven.
How it works
- AI theatre audit — kill list of projects burning resources without a production path
- Hypothesis tree — validated bets with evidence
- Leadership alignment, stakeholder sequencing, board-ready presentation
- 3-4 syncs/week with engineering, weekly C-suite, quarterly board
- Milestone gates — not calendar timelines
- Parallel workstreams: product eng + ML running simultaneously
- Shadow leadership: coaching your AI lead to run it without me
How I assess a company
Every engagement starts with a diagnostic. The output is a position on two dimensions: the company's actual AI capability, and the potential for AI to drive value in their business. Both matter — one without the other changes the engagement entirely.
How it compares
Strategy firms leave after the deck. Software houses build it and own the dependency. I stay through the change — until your team can run it without me.
The fee is sized relative to what's at stake. At $100M ARR, a 5% improvement on a core metric generates $5M annually — the engagement costs a fraction of year one, and after that the improvement compounds and you keep all of it.
| The Fractional CAIO | Strategy firm | AI strategy boutique | AI software house | Full-time hire | We build it ourselves | |
|---|---|---|---|---|---|---|
| What you get | Shipped product, trained team, capability that persists | Slide deck and a plan | Diagnosis and roadmap — no delivery or embedded follow-through | Built product — ongoing dependency | A person who needs direction you don't yet have | Whatever your team can figure out — no external benchmark on what good looks like |
| After engagement | Your team runs it without me | You have a roadmap | You have a plan. Execution is yours. | You have a product you can't extend | 6–12 months to get up to speed | You own it — and all the direction risk that came with it |
| Talent development | Core focus — build and coach your AI team | Rare | None — advisory model only | None — they own the capability | Self-directed — needs leadership | Grows slowly — no external coaching or pattern-matching from other engagements |
| Vendor dependency | None — works on your existing stack | May push proprietary tools | None | High — architecture tied to their team | None | None |
Trusted by leaders
"Jack brought a structured approach to prioritizing AI projects. His energy, clarity, and directness helped us shift focus to higher-impact areas, make better use of existing resources, and adopt a hypothesis-driven process."
"Jack's consulting helped us understand how the portfolio company approached AI — how it's built, how it operates, and where they need to adjust. His ability to explain complex ideas and influence the team made a real difference."
Not the right fit
I take on 2-3 engagements per year. Being direct about fit saves both sides time.
- Pre-Series B — the engagement fee won't make sense relative to your capital. Hire a fractional ML lead, or come back after the raise.
- You want to outsource AI delivery — I build your team's capability, not the product for you.
- You're still exploring whether AI matters for your business, not yet committed to changing how your team works.
- You want to hire an ML engineer — I can help you think through the role, but I'm not the hire.
- You don't have dedicated ML and product engineers allocated — this requires real engineering capacity, not time borrowed from other priorities mid-sprint.
- You can't provide weekly C-suite access — AI decisions can't route through three layers without losing velocity.
- Your deployment process moves in quarters, not weeks — the goal is shipped product, not staged experiments.