AI Consulting for CEOs

Less than most executives expect, and it is concentrated at the start. For a first project, plan on a kickoff conversation, one or two working sessions to agree the problem and the success metric, and a review at the decision point — a handful of hours across a quarter. What cannot be delegated is the framing: if you hand over an unclear problem, you get back an unclear result. The delivery itself should not need you, and if it constantly does, something is wrong with the ownership structure rather than your calendar.

Tell them what you are testing, what it will cost, what evidence would justify continuing, and by when you will have it. That framing survives scrutiny far better than an ambition statement, because it converts AI from a matter of belief into a matter of evidence.

Avoid two things: committing to a transformation programme before a single project has produced a measured result, and describing the technology instead of the business effect. A board does not need to hear about model architecture. It needs to hear which number you are trying to move, by how much, and what happens if you are wrong.

Say something specific, early, and true — because the alternative is not silence, it is rumour. In most projects we deliver, AI absorbs a task rather than a role: the document handling, the counting, the first-line questions. If that is the case, say which tasks and what those people will do instead.

If a project genuinely will reduce headcount, do not disguise it as an efficiency initiative. Your workforce will work out the truth faster than your communications plan, and adoption depends on people who believe you. The message about jobs is the one part of an AI programme that cannot be delegated to anyone — see the section above on what belongs on your desk.

Three tests work quickly. Ask what they would decline to build. A practitioner will name several things AI is bad at; a reseller will find a way to fit everything to their platform. Ask to meet the people who would write the code. If the advisors and the builders are different companies, you will own the integration risk. Ask what you own at the end — models, data, documentation, and whether you can leave.

Then watch for guaranteed returns promised before anyone has looked at your data. No serious practitioner offers those, because until the data is assessed nobody knows. Our own project record is published with the numbers attached for exactly this reason.

Almost certainly not. Waiting for a data strategy before starting anything is one of the most reliable ways to lose two years. Most companies hold more usable material than they assume — transactions, tickets, documents, images, sensor logs — and modern approaches need considerably less labelled data than earlier generations of machine learning.

The practical route is to pick one problem, find out during the assessment whether the data behind it is sufficient, and let that project reveal what your data strategy actually needs to fix. A strategy written from a real project is worth more than one written from first principles, and it arrives a year earlier.

You know because you decided in advance what would count as working. That is the entire purpose of agreeing a number before the work starts: it converts a difficult judgement into an arithmetic one, and it removes the sunk-cost argument from the room.

Practical signals that a stop is due: the metric has not moved and nobody can explain why in plain language; the goalposts have been redefined more than once; the team is reporting activity rather than results; or the business owner has quietly stopped attending reviews. Stopping a project cleanly is not a failure of the programme — it is the mechanism that makes a programme affordable. Our staged model exists so that stopping is always a normal, budgeted option.

Usually not first. A senior AI executive with no delivered projects to point at tends to spend the first year building a mandate rather than a capability, and the role can become a place where accountability for AI is parked away from the operating business.

The sequence that works more often: deliver one or two real projects with an accountable business owner, learn where the friction actually is, and only then decide whether that friction needs a permanent executive, a data platform team, or simply clearer ownership inside existing functions. If you do eventually hire, you will hire far better for having seen the work up close.

Probably less than the announcements suggest. Press releases are a poor proxy for production systems, and a great deal of what is announced is a pilot, a partnership, or a feature switched on inside software the company already licensed. Being second with something that works is a stronger position than being first with something that does not.

The honest concern is different. What compounds over time is not the announcement but the accumulation — data you have organized, models trained on it, and people who know how to use them. If a competitor is two years into that and you have not started, the gap is real. The remedy is not a matching announcement; it is one project that finishes and produces a number, then another.

Ask this of any provider, and ask it in writing before you sign. In our engagements the client owns the models built for them, the data those models are trained on, and the documentation needed to operate and extend the work — including private deployments where sensitive knowledge never leaves your environment, as in our custom LLM chatbot project. If a provider cannot answer this crisply, the answer is that you own less than you think.

The market runs on hourly rates, retainers, and fixed-price projects. We recommend fixed-price stages — proof of concept, MVP, then full product — because they align the incentives correctly: the provider is paid for finishing a defined outcome rather than for extending an engagement. The figure depends on how complex the problem is, how ready your data is, and how many systems the solution must connect to, so talk to us and we will scope it against your actual case.

Yes. We operate from the Frankfurt Rhine-Main region (Darmstadt) and Berlin and work with clients worldwide, with engagements run remotely and structured checkpoints at every stage. If you would rather have the first conversation in person, both offices are open to you. Reach us at info@aisuperior.com or +49 6151 7076909.