Choosing an AI agency for the Mittelstand: five questions that place any vendor

There are now more AI consulting vendors in Germany than there are AI systems actually running in production. Some of them came out of online marketing, some out of classic management consulting, and some were founded last year. For a managing director in the Mittelstand awarding an AI project for the first time, there is no way to tell from the outside which of them can build a system and then operate it for years.

This is a buyer's guide. It covers what an AI agency working with the Mittelstand has to deliver, which questions place a vendor within twenty minutes, what the work realistically costs, and which criterion is missing from almost every tender. It applies to any vendor, including us.

What an AI agency has to deliver in the Mittelstand

The difference between IT consulting for a corporation and consulting for the Mittelstand is not project size. It is the question of who works with the result afterwards.

A corporation has an IT department of thirty people, a data team, and a budget for internal development. A machine builder with 120 employees has two people in IT who are already fully booked. A system that needs in-house expertise to stay alive after handover is not a success there. It is a liability.

Three requirements follow from this, and they are specific to the Mittelstand:

• The result has to work without the vendor, but not without an operator. An AI system ages differently from a machine. It does not wear out, but the world around it shifts: models age, interfaces change, licence terms get revised. Somebody has to keep up with that for years.
• The scope has to be small enough to finish. Projects that start with a company-wide AI strategy rarely reach production. Projects that start with a single process usually do. We described what such an entry point looks like in AI strategy for the Mittelstand.
• The data has to stay where your procurement department wants it. For many Mittelstand companies the question of storage location is not a nice-to-have but a contractual condition, often because their own customers put it to them first.

Five questions that place a vendor

You do not need a technical background to ask these. You only need the patience to insist on a specific answer.

1. What are you operating for other clients today, and since when?

The answer separates vendors who build systems from vendors who build presentations. Ask about duration, not count. A system that has been running for three years says more than forty projects last year. If a vendor can only talk about pilots, you know where their experience ends.

2. Who picks up the phone when the system stops answering at seven in the morning?

You are looking for a name and a role, not a ticket address. With many vendors the honest answer is: nobody, because operations were not part of the offer. That is a legitimate setup, but you should know it before you sign rather than after.

3. Where does our data run, and what happens to it at the model provider?

Specific answers sound like this: in a data center in Frankfurt, with a data processing agreement, training on our data contractually excluded. Vague answers sound like this: GDPR-compliant, of course. The difference is the entire point. What that means technically is on our GDPR-compliant AI page.

4. What does operating this cost in year three?

The project sum is the smaller part. The interesting figures are the running costs: model usage, hosting, maintenance, further development. A vendor who cannot quantify that has not accompanied any of their systems into a third year.

5. Which part of our problem would you not solve with AI?

This is the most revealing question in the conversation. Any serious vendor has an immediate answer, because they know the cases where a database query, a clean form, or classic rule-based automation is the better tool. Anyone who flounders here is selling you AI, not a solution.

What AI consulting costs in the Mittelstand

Prices vary, but the orders of magnitude are stable enough to judge an offer against. Typical for the German market:

• Assessment and opportunity analysis: EUR 5,000 to 15,000, two to four weeks. The deliverable is a written document with prioritized use cases, estimated effort, and a recommendation on where to start. If all you have after this phase is a presentation, you bought a presentation.
• First production system: EUR 25,000 to 80,000 for one clearly bounded process. Anything larger should be broken into partial deliveries you can accept individually.
• Operations and further development: a monthly retainer, depending on scope and response times. This is the line item most often missing from offers and most often painful in production.

Two warning signs on pricing. Per-seat monthly billing for a system you already paid to build ties your costs to your growth without the vendor doing more for it. And a fixed price for a project whose scope nobody has examined yet is either padded or will be renegotiated later.

The criterion missing from most tenders

The usual selection criteria are references, price, and technical competence. The decisive criterion is a different one: who is accountable for the system after it goes live?

The classic split separates the two. An agency builds, then your IT department or another provider takes over operations. On paper that looks efficient. In practice it creates a gap where accountability disappears, and with AI systems that gap is considerably wider than with normal software. A classic form behaves next year the way it behaves this year. A language model does not: versions turn over, terms shift, individual models get deprecated. Those changes arrive from outside and do not respect your release plan.

We wrote at length about why one team should own a system end to end. For vendor selection the short version is enough: ask whether operations are in the offer. If not, ask who takes them on. If there is no clear answer, the project has not been planned to the end.

When you do not need an AI agency

A share of the inquiries that reach us should not become AI projects. The most common cases:

• The data does not exist. If the information the system is meant to judge lives in people's heads, in mailboxes, or in handwritten notes, the first project is a data project, not an AI project.
• The process is rule-based anyway. If a decision can be written out completely as a set of rules, rule-based automation is cheaper, faster, and easier to evidence in an audit. AI earns its keep where the inputs are unstructured.
• The process runs twelve times a year. Automation pays off through repetition. At low frequency the operating effort exceeds the benefit.
• There is no internal owner. If nobody in the company can be named as the person who decides about the system, nobody will use it after rollout.

A vendor who turns you down in these cases, or proposes something smaller, is the better partner than one who takes the budget.

What a first conversation should look like

Thirty minutes is enough to establish fit. In that time the person across from you should ask more than they tell, be able to name at least one concrete example from their own operations, and tell you which part of your plan is the hard one. If it ends with a slide deck and no interim result, it was a sales meeting.

Our own offer is on the AI consulting for the Mittelstand page. If you have a specific plan and want to know whether it pays off: talk to an engineer, not to a salesperson. If we are not the right partner, we say so in the first email.