Fractional Chief AI Officer

Separate the signal from the noise.

Embedded AI leadership for mid-market manufacturers — and the private equity funds that own them. The judgment to tell a real advantage from a demo.

EST. REFERENCE POINT MFG · MID-MARKET · PE CALIBRATED TO YOUR FLOOR
The gap

Everyone is exploring AI. Almost no one is ready to run it.

Manufacturing has the widest distance of any sector between intent and execution. The pilots multiply; the production systems don't. Physical stakes make a wrong call expensive — a bad answer on a factory floor stops a line or hurts someone.

That distance is the entire job. I close it — owning the strategy, the governance, and the first use cases into production, accountable for the outcome, not the slide deck.

Exploring or considering AI 98%
Feel ready to run it at scale 20%

// the 78-point spread is the readiness gap.
// industry surveys, 2026.


Two engagements, one funnel

Where the judgment goes to work.

01 / Operating

Embedded Fractional CAIO

Executive AI leadership on a retainer — the accountability and decision rights of a Chief AI Officer, at a fraction of the cost and none of the search. I sit inside the leadership team, own the roadmap, and get the first one or two use cases into production within governance you can defend to a board or a regulator.

  • AI strategy, build-vs-buy, and vendor calls
  • Governance under EU AI Act, SOC 2, NIST AI RMF
  • Predictive maintenance, quality, scheduling, ops
  • Board and investor reporting
02 / Capital

AI Survivability Diligence

For sponsors evaluating a deal: not "can we use AI on this target," but "does this target's advantage survive an AI-enabled market." A clear read on disruption risk, model and data maturity, and the post-close AI value plan — delivered on the deal clock, in language an investment committee can act on.

  • Disruption-risk read on the thesis
  • AI & data maturity assessment of the target
  • Regulatory and governance red flags
  • Post-close value-creation plan

These aren't separate practices. Diligence at the fund feeds embedded work across the portfolio — one relationship, recurring underneath.


Who it's for

Built for two buyers who share a problem.

Mid-market manufacturers

Roughly $20M–$500M in revenue, where AI is strategic but a full-time CAIO is premature and the CTO is already stretched. You have budget and intent. What's missing is someone who can stand on the floor, read the stack, and own the outcome.

PE & capital-markets funds

Sponsors and operating teams who need a defensible AI read before they sign — and a repeatable playbook to create value after they close. Industrials are dense with these targets, and M&A is back at the top of the value agenda.

Why this works

Technical enough to be believed. Strategic enough to be useful.

Both rooms

Hands-on with the engineers on ML, model training, GenAI, and agentic workflows — and fluent with the business stakeholders who have to fund and live with the result. The translation layer is the value.

Regulated rigor

Years in financial services, healthcare, and government, where a governance failure is a real cost, not a slide. That discipline travels straight into manufacturing's physical stakes.

MIT-grounded

Methodology anchored in MIT's New Manufacturing research — so the survivability read rests on something an investment committee will recognize, not on vibes.

Field notes

Insights

All insights →
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Find out what's real before you spend on what isn't.

A short conversation to pressure-test where AI actually moves the number for your business — or your next deal.

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