What is a fractional AI department?

What the department consists of

At Helix, a fractional AI department is two steps after the diagnostic: Adopt and Partner. Four parts do the work:

• Adoption of the tools you already own: setting up and training each team on Microsoft 365 Copilot, Gemini for Google Workspace, ChatGPT or Claude, writing the AI use policy, and reviewing licence spend. We don't resell licences, so we recommend whichever tool fits.
• Builds only where those tools fall short: a memory layer (a second brain) or agents in an AI operating system, when a job can't be done by the tools you own within about six months.
• Approval gates: anything that sends, publishes or spends waits for a named person to approve it; an action type earns autonomy only after a sustained low override rate.
• An operator and an owner: our team, accountable for adoption and return, and a trained owner on your team for every system we ship. This is the part the $297/mo tools don't sell.

Fractional AI department vs fractional CAIO

The names sound alike; the deliverables don't. A fractional Chief AI Officer advises: several hours a month of strategy, vendor evaluation, and a roadmap. Valuable if your problem is not knowing what to do. But most companies of 50 to 300 people already know roughly what they want from AI; their problem is that licences sit underused and nobody has set up the workflows. A fractional AI department trains, ships, and operates. The roadmap comes out of the diagnostic; it is not the deliverable.

Vs hiring in-house: the honest math

A competent US ops or automation hire runs roughly $90–120K a year in salary before benefits and tools. They work one timezone, take two to four months to ramp, and when they leave, the context leaves with them. An in-house AI engineer costs meaningfully more, and still needs someone to supply the operational context.

A fractional AI department is scoped to the work you actually want changed and priced in the readout, typically landing under the loaded cost of that hire, with output from the first month, no ramp, and documentation that survives any staff change. But the comparison isn't one-sided, and pretending otherwise would be selling. Hiring wins when the work is judgment-heavy or relationship-heavy, when you need deep custom product engineering, when AI is central enough to your strategy to need a full-time executive owner, or when what you actually need is a manager for humans. The longer version of this comparison, including where DIY tools fit, is at AI agency vs hiring.

What it costs, and what's included

Helix publishes its prices. The diagnostic is $5,000, credited in full to Adopt, and it prices everything after it; two founding slots are open at $2,500 in exchange for a named case study. Adopt is priced in that readout, and so is Partner, the 12-month engagement, both in writing before work starts, because the honest drivers vary: how many teams are involved, how many systems a build touches, how complex the approval flows are, and how much of your knowledge already exists in writing. Whatever the number, the same things are always included: every action validated against its real side-effect, approval gates until an action type earns autonomy, a monthly adoption check and a return report against the baseline set in the diagnostic, and everything we build staying yours. Details and what drives cost up or down are on the pricing page.

How the engagement runs

It starts with the diagnostic: interviews with the people who do the work, and a map of where AI pays and what it's worth from your own numbers, not from a benchmark deck. Then Adopt: tools set up, teams trained, and builds only where those tools can't do the job, each running behind approval until it earns autonomy. Then Partner, for 12 months: a monthly adoption check, a return report against the baseline, tuning, new workflows, and a re-diagnosis every quarter. Twelve months, because whether the return shows up depends on usage, and usage takes more than a quarter to settle. And the exit is designed to be boring: everything documented, everything we build yours, every token revocable by you in one click.