A fractional Chief AI Officer who ships.

A fractional Chief AI Officer is a senior executive who owns a company's full AI agenda part-time rather than as a full-time hire. That means AI strategy tied to the P&L, the governance and risk posture, the portfolio of use cases and the spend behind them, plus accountability to the board for what the investment returns. The effective ones embed and make decisions; they don't advise from the outside.

Me. There is no firm, no bench of consultants, no account manager, and nobody junior sent in my place. You meet me, you hire me, and I do the work with your teams. The honest trade-off is capacity: I take a small number of engagements at a time, so if the calendar is full I'll say so rather than putting someone else on it.

A consultant delivers a recommendation and leaves. A fractional CAIO holds the seat: they make the call, own the roadmap and the budget, sign off on what ships, and answer for the result. In my case the difference goes further, because I also architect and build the first production systems rather than handing them to an integrator.

Worth saying first, because it changes the maths: I hold both seats. Twenty-five years as a CTO, and the AI agenda as a CAIO, filled by one person. That is not a way to replace your CTO, your VP of Engineering or your Director of IT. It is the opposite. They already have a full job, and the usual failure is adding a second one labelled AI on top of it; I take that off their plate and work alongside them. The practical effect is speed and a single salary, because the strategy call and the architecture call happen in the same head on the same afternoon. The range runs from AI discovery workshops with the business, through implementing AI inside marketing, finance or operations, down to the agents, the architecture, the governance and training your engineers to run it. The underlying split: a CTO owns the entire technology function, while a CAIO owns AI across the whole business including functions that don't report to engineering, along with the AI-specific risk and governance. There's a full comparison at /chief-ai-officer-vs-cto.

Fractional fits when AI matters strategically but doesn't yet justify a $300K–$650K executive, when you need someone in the seat this month rather than in two quarters, or when you want the operating model built correctly before you hire permanently. Go full-time when AI is the core of the product, the AI organization is large enough to need daily management, or a regulator expects a named accountable executive on staff.

Phase one: a complete AI inventory, a shadow-AI audit, a data readiness read, and a ranked portfolio of automations with a business case each. Phase two: AI policy, a governance charter, risk classification against NIST AI RMF and the EU AI Act, and the first automation moving from plan into a live function. Phase three: that automation in production with a measured result, a rolling roadmap, and a board pack covering what was funded, what was killed and what it returned. How long each phase takes depends on the size of your estate and how many functions are in scope, so I scope it after the inventory rather than promising a calendar up front.

Tool licences are not an AI program. If nobody can produce a list of every AI system touching customer data, if pilots keep stalling short of production, or if AI spend is rising without a number attached to it, the gap is ownership rather than tooling. That gap is exactly what this seat fills.

Both, because separating them is how programs stall. I build the auditable AI inventory, write the acceptable-use policy, stand up the review board, and map one control set across the frameworks that apply to you: ISO/IEC 42001, the NIST AI RMF, the EU AI Act and OWASP's Top 10 for LLM Applications on the AI side, and SOC 2 Type I and II, ISO 27001, HIPAA, GDPR, CCPA and PCI DSS on the security and privacy side, plus sector rules like HITRUST, FERPA or COPPA where they apply. Then I wire the review gates into the delivery pipeline so governance is something engineering passes through rather than something it routes around.

Pricing is published and tiered by commitment: a fixed-fee AI Diagnostic from $20,000, then AI Advisory ($10K/mo, about one day a week), Fractional CAIO ($18K/mo, about two days), or Interim/Embedded CAIO ($30K/mo, three or more days). Most fractional engagements run 6–18 months and end in a planned handover to an internal owner.

The opposite, and it is written into how the work is scoped. A consulting firm bills more the longer it is needed, so the engagement quietly becomes the product. Here the deliverable is your independence. Once the strategy is set and the process runs day to day, the work turns to handover: if it needs people you don't have, I recruit them into your organization and interview every candidate myself; if the people are already there, I train them until they can hold it. Then the retainer stops. The line back to me does not, because a field this unsettled is not one anyone should read alone, and you shouldn't have to re-hire someone to ask one hard question.

Companies looking for a cheap way to bolt a chatbot onto an existing product, or for a name to put on a governance document nobody intends to follow. This is senior, hands-on ownership for companies where the AI decision moves the valuation.