What a Fractional Head of AI Actually Does

Nobody knows what a head of AI does.

When companies can't describe the role, they either skip it entirely or make a $400K hire they're not ready for, and both are expensive mistakes.

It lives in the gaps

AI strategy doesn't belong to engineering, product, or IT; it falls through the cracks between all three, and that's exactly where a fractional head of AI operates.

Strategy means making concrete build/buy/postpone decisions, not writing a slide deck about AI's potential. I worked with a company that had three teams independently evaluating the same LLM vendors with no shared criteria and no one tracking the results. The fractional role stopped that and turned it into a single decision with a clear owner.

Roadmap feedback means sitting in planning sessions and asking questions the room isn't asking: what happens when this model's context window fills up? What's the fallback when the API goes down? What does this feature cost to run at scale? Product teams making AI investments for the first time don't know what they don't know, and that's the gap.

Project outlining means turning "we want to build an AI thing" into a scoped project with a definition of done, a success metric, and someone accountable. Without this, AI projects die in pilot purgatory: they work well enough to keep funding but never reach production.

Tools and frameworks means someone has to actually know the difference between when to use a fine-tuned model versus RAG versus a simple prompt chain. That decision has real cost and architecture implications and shouldn't be made by committee.

Someone needs to own decisions with long-term consequences.

Making the team capable

The goal isn't to be the only person in the room who understands AI.

Team training done right isn't vendor-led "AI for everyone" sessions where everyone leaves with a ChatGPT account and no idea what to do with it. It's targeted work on how these tools actually behave: where they fail, how to build workflows around their limitations, and how to evaluate new tools without getting sold.

Specs start accounting for model limitations and latency, not just feature requirements. Engineers evaluate output quality systematically instead of copying prompting patterns from Reddit. Marketers and ops people build their own Claude workflows on their actual work. Support leads define when AI handles a ticket and when it escalates.

The benchmark I use: after training, a team member evaluates a new AI tool independently and explains to a non-technical stakeholder why a given approach will or won't work. Building a basic prompt chain without help is assumed.

Good training makes itself obsolete, and that's how you know it worked.

The work nobody talks about

Two things come up in every engagement and get almost no airtime in the "AI strategy" conversation.

Internal process mapping: before you build anything, you need to understand what's actually happening in your current workflows. Where are the real bottlenecks? Where are humans doing work that doesn't require human judgment? This isn't glamorous, but it's where the leverage hides. I've seen companies spend six months planning AI features for a process that had a simpler fix upstream. The mapping catches that.

Context management and RAG: most companies hit the same wall. The AI doesn't know anything specific to their business. Retrieval-augmented generation solves that, but implementing it well means real decisions about data structure, retrieval quality, latency, and what "good enough" looks like for your context. A customer support AI with access to the right knowledge base handles 60-70% of tickets without human review. The same AI without it handles almost none. The difference between those outcomes is architecture, not the model you choose.

Get these wrong early and you pay for it for years.

Why fractional before full-time

A full-time chief AI officer costs $300K-$500K in salary before equity, and for most companies that's not the right first move, because you don't yet know what kind of AI leader you actually need.

The engagement starts with a discovery call to map current AI usage, then an audit that produces a 90-day roadmap. After that it's embedded partnership: quick wins by day 30, measurable results by day 90. Most engagements produce enough signal in that window to know whether you need a full-time hire, a different kind of hire, or more fractional time.

Good early choices compound in ways that are hard to see at the start. A team that evaluates AI tools well makes better purchases. Support systems built on clean knowledge architecture get cheaper per ticket as volume grows.

You end up building the full-time role, if you need one, around real evidence instead of a job description someone copied from LinkedIn.