What a fractional Chief AI Officer does in the first 90 days

The decision

Most companies we speak to already use AI. Someone in marketing has a subscription. Finance is testing a forecasting tool. A developer has wired a chatbot into support. What almost none of them can say is who owns the decisions about what comes next: which pilots get funded and which get stopped, what data is allowed to leave the business, and how anyone will know whether it paid.

That is the job a Chief AI Officer does. Larger companies are increasingly creating the role. Most mid-sized companies can't justify a full-time executive for it, and most don't need one. So the decision this memo covers is narrower: whether to put someone in the seat part-time, and what they should have produced after 90 days.

The definitions

A fractional Chief AI Officer is a senior executive who owns AI strategy, vendor decisions, governance and board reporting for a set number of days a month.

The difference from a consultant is ownership. A consultant delivers a recommendation and leaves. The seat stays accountable for whether the recommendation worked.

The seat is not a developer, not an IT manager, and not a salesperson for a particular tool. If the person in the seat also sells you the build, the build-versus-buy reasoning has to be written down where you can review it. We come back to that below.

What changes in 90 days

Days 1–30: the inventory and the roadmap. First, every AI tool, subscription and pilot already running: who uses it, what data it touches, what it costs. Then a short roadmap: the three or four places where AI removes an identifiable cost line or a recurring delay, ranked by value and by how hard each is to do safely. Inventories almost always turn up tools nobody approved, and usually at least one pilot that should be stopped.

Days 31–60: the rules. An AI use policy staff will actually follow — which tools are allowed, what data goes where, who approves a new one. A data-residency position: for businesses under POPIA or GDPR, whether client data may leave your infrastructure at all, and where a model running inside your own environment is the answer. And a standard set of answers to the security questions your clients and auditors will ask, so each one isn't a fresh project.

Days 61–90: the first system and the first scorecard. One item from the roadmap in production, scoped so it can be measured from the day it goes live. And a one-page board scorecard: what is deployed, what it costs, what it has returned, what comes next, and who owns it.

What the scorecard shows

Take an illustrative one-page scorecard, the kind the seat hands the board at day 90. Five columns: what is deployed, what it costs a month, what it has returned, what comes next, who owns it. Four rows.

Row one, a drafting desk for the sales team: deployed, costed, return measured as hours a week not spent on first drafts, owner the commercial lead. Row two, a forecasting pilot in finance: deployed, costed, return "not yet measured", next step "set a baseline or stop", owner the finance lead. Row three, a document-review tool in operations: deployed, costed, return measured as turnaround days, next step a spot check on accuracy, owner the operations lead. Row four, a marketing subscription found during the inventory: nobody using it, cancelled.

A board reads that in a minute. It can see the one thing that is paying, the one thing nobody has measured, and the one thing that was quietly costing money. What the page deliberately does not carry is a strategy narrative. The narrative is the conversation; the page is the evidence.

How we run the seat

We work in days per month, not hours. The seat attends one leadership meeting a month and reports to the board each quarter. Everything is written down: the inventory, the roadmap, the policy, the scorecard. If the engagement ends, the company keeps the documents and the systems.

Builds are quoted separately. Where we build, the reasoning for building rather than buying goes into the roadmap before any quote exists, and the client is free to take the build to anyone. A part-time AI lead from a firm that also delivers has a conflict of interest; the only honest way to handle it is in the open.

We run this model inside a multi-million-dollar e-commerce marketplace: the dashboards its leadership reads every week, and the desks where AI drafts and the team approves. The seat is that job, done for a company that doesn't yet have anyone doing it.

What it doesn't do

It doesn't replace your CTO or your IT team. It gives them a decision-maker for AI and a set of rules to work within.

It doesn't make AI safe by itself. A policy nobody reads is paperwork, which is why the scorecard exists: it shows whether the rules are being followed and whether the systems are paying.

And 90 days doesn't transform a company. It produces an inventory, a set of rules, one working system, and a way to measure the next one. That is enough to know whether the seat is worth keeping.

The next decision, and who owns it

The CEO owns it. The question isn't whether to have a Chief AI Officer. It's whether anyone in the business can answer three questions today: what AI are we already paying for, what data is it touching, and what has it returned? If nobody can, the seat is empty — whoever you eventually put in it.

Where to start

Write down every AI tool, subscription and pilot you can think of, with a name next to each. The gaps in that list are the first month's work.

If it would help to do it with someone, the free 15-minute audit is where every Automaiq engagement starts — bring the list, however incomplete. No pitch. If what you need is the seat rather than a build, say so on the call.