Fractional AI CTO: role, costs, and a time-boxed alternative
The honest version of this page: "fractional AI CTO" is what many companies search for. It is not what I call myself — but it is close enough to what I do that the differences are worth explaining.
What is a fractional AI CTO?
A fractional AI CTO is a senior technology leader who takes responsibility for a company's AI direction on a part-time basis — typically a fixed number of days per month, for a monthly fee, across several months or years. The company gets executive-level AI judgment — which use cases to pursue, what to build versus buy, how to handle data and risk — without hiring a full-time executive.
When it makes sense
The setup fits a specific situation, and it is common in the Mittelstand:
- The C-level is facing AI decisions — from customers, from the board, from competitors — and wants them grounded in engineering reality, not vendor slides.
- There is no AI leadership in-house. The Head of IT runs the systems that keep the company alive; evaluating LLM architectures on top of that is a different job.
- A full-time hire isn't justifiable. An experienced AI executive is expensive and scarce, and most companies don't have a full-time AI agenda yet — they have a handful of decisions and one first system to get right.
If all three apply to you, you are the person this page was written for. The remaining question is what form the help should take.
Fractional AI CTO vs. interim CTO vs. agency
| Fractional AI CTO | Interim CTO | Agency / dev shop | |
|---|---|---|---|
| Scope | AI strategy and oversight, part-time, alongside the C-level | The full CTO role — team, budget, roadmap — temporarily | A defined build project, executed by their team |
| Duration | Open-ended; runs month to month until cancelled | Months, until a permanent hire takes over | Per project; follow-up work is the business model |
| Who does the work | Usually advises; building is delegated to your team or a vendor | Leads the people who do the work | Their developers — you translate between them and your business |
| What you own afterwards | Decisions and direction — capability depends on the person | An organization that runs without them, if it went well | The deliverable; the know-how usually leaves with the agency |
What it costs
Two pricing models dominate this market. A fractional AI CTO is sold as a monthly retainer: a fixed fee for a fixed number of days per month, running until one side ends it. The cost is predictable per month — but open-ended in total, because the engagement has no built-in finish line.
The alternative is time-boxed packages: a fixed scope, a fixed timeframe, and a defined deliverable, priced and agreed before the work starts. The total cost is known up front, and the engagement ends by design — with a handover, not a renewal conversation. That is the model I work in; the concrete figure is something we discuss in the first call, against your actual scope.
Why I work time-boxed instead
I advise the C-level on AI — and prove it by building the first system myself. But I don't sell a retainer for it, because a retainer pays me most when you stay dependent longest. My engagements are one of three time-boxed packages — the De-risked AI ladder:
- AI Discovery — we rank the AI use cases worth building in your company and scope the strongest one, with its success metric defined.
- AI proof of concept — I build the first system myself: production quality, on your data, with the technology that fits your business. The result is measured on the POC against the metric we agreed up front.
- AI enablement — your C-level and your developers learn to run, extend, and govern the system. I hand the wheel back.
Each package has defined deliverables and a fixed timeframe, agreed before we start. You get the part of the fractional-CTO promise that matters — executive AI judgment plus a shipped system — without the open-ended dependency. How the three packages fit together →
Questions worth asking — answered plainly
How is this different from a normal AI consultant?
Most AI consultants advise and leave the building to others; most dev shops build and leave the advising to you. I do both ends myself: I sit with the C-level to decide what is worth building, and then I write the code that proves it. The recommendation and the system come from the same person — there is no gap to fall into between strategy and delivery.
What happens after the engagement ends?
You keep everything: the system, the source code, the architecture notes, and — after Enablement — a team that can run and extend it without me. The engagement ends with a documented handover. If a new use case appears later, the ladder simply starts again at Discovery; there is no standing fee in between.
Do you work remote or on-site?
Both. I'm based in Hamburg and work on-site in the Hamburg Metropolitan Region; Discovery workshops in particular benefit from being in the room. The engineering work happens remotely just as well, and I work with companies across Germany and Europe that way.
Do you work in German or English?
Both, at working-language level. Workshops with the C-level typically run in German; code, documentation, and developer training can be in either language — your team's preference decides.
Who owns the code?
You do. The POC is built on your data and handed over with its source code, architecture notes, and the security answers your IT will ask for. There is no licensing, no lock-in, and no dependency on my infrastructure.
Can I hire you as a fractional AI CTO on a retainer anyway?
No — and that's deliberate. If after the ladder you still want a permanent AI executive, the honest answer is a hire, and Enablement will have prepared your team to work with one. What I offer is the faster path to the same outcome: decisions made, a system shipped, capability in your house.
Let's make AI real in your company
Tell me where it hurts — a process, a backlog, a board question. You'll get a personal reply from me. Get in touch →