AI pays off in mid-sized companies where a process happens often, takes a lot of time and can be described clearly. Start with exactly one such process. With us, a first AI solution starts at 8,000 euros. In our projects, the time spent on the chosen process often drops by 80% or more.
This guide shows where AI helps today and where it does not. It explains how to find the right first process and how your data stays safe.
Where AI helps mid-sized companies today
Most useful AI projects in mid-sized companies are unspectacular. They take routine work off your team. Typical cases:
- Reading and evaluating documents. Quotes, contracts, tenders or loan documents are read by hand today. AI reads them, pulls out the key details and flags what stands out.
- Creating documents. Quotes, reports or tender responses are built from templates and existing data. A person checks and approves.
- Making company knowledge accessible. Your knowledge sits in manuals, tickets and emails. A RAG system (a search that builds answers from your own documents) answers questions about it, with sources.
- Moving data between systems. Where someone types data from one system into another today, an automation can take over.
- Handling multi-step tasks. AI agents work through a task in several steps, with access to your systems and with clear boundaries.
Where AI does not help
AI does not replace a process nobody can describe. If every employee solves a task differently today, the AI learns nothing reliable either.
AI rarely pays off for rare tasks. If something happens twice a year, building and testing costs more than it saves.
And AI should not make a decision on its own that someone is liable for. There it prepares, and a person decides. Why we deliberately put the human at the end is in the post The human belongs at the end.
How to find the right first process
The first process decides whether AI counts as a success in your company. Check your candidates with three questions:
- Does the process happen often? Daily or weekly is good. The more often, the faster the solution pays off.
- Does it take noticeable time? Estimate roughly: how many hours a week does your team spend on it today?
- Can it be described clearly? What comes in, what should come out, and what does a good result look like?
If a process answers all three with yes, it is a good candidate. Start with the one that takes the most time.
One mistake is common: companies pick a tool first and then look for a task. Turn that around. Process first, technology second.
How an AI project runs with us
- Short review. We look at two to three processes you see as candidates.
- Estimating effort and effect. For each candidate we estimate what building it costs and how much time it saves.
- Recommendation. We recommend the process that pays off first. If none does, we tell you.
- Building. We build the solution and connect it to your existing systems.
- Testing against real cases. Before launch we test the system on a set of real cases from your daily work. We measure the hit rate. So you know beforehand what it delivers.
If you only want a concept first, for example as a workshop for the management team, you can get that on its own. More about the process is on our AI consulting page.
What it costs
Our guide prices for 2026, net:
| Service | Price |
|---|---|
| Concept or workshop | €3,000 to €5,000 |
| First AI solution (MVP) | from €8,000 |
| Larger platform | from €40,000 |
We name the exact price after the first call.
On top come running costs for using the language models. They depend on how often the system works and how much text it reads. What really drives these costs is explained in What AI agents actually cost.
What it brings: three examples
Evaluating quotes. A trading company screened incoming quotes by hand. Today an AI reads them first. Analysis time dropped by more than 85%, with an accuracy above 95%. More in the case study on AI-assisted offer analysis.
Creating quotes. At a consumer goods manufacturer, the design and sales team built quotes by hand. Today the quote PDFs are generated automatically, with six design variants per order. Creating a quote takes 80% less time, and the whole team works with the tool. More in the case study on AI enablement.
Processing loan cases. At a mortgage broker, eight out of ten loan cases needed rework. After six targeted changes the process runs reliably, with 85% less manual work. The data stays in the EU. More in the case study on mortgage process automation.
You can find more projects under case studies for AI and automation.
Your data stays safe
Many companies hesitate with AI because of their data. The concern is valid, and it can be solved.
We build the connection so that the models run through European endpoints. For especially sensitive data, an open-source model runs on your own infrastructure. We decide together which option fits. The basis is your data classes, meaning which data needs how much protection.
The EU AI Act plays a role too. For most applications in mid-sized companies the obligations are manageable if you think about them early. More in the post AI ethics as a competitive edge.
When the AI is wrong
Every language model makes mistakes. We assume it will happen and build the system accordingly:
- Sources. Every answer from your documents names the passage it comes from. That makes it checkable.
- Thresholds. Automatic decisions are only made when the system is confident enough.
- Escalation to a person. If the model is unsure, the case goes to an employee.
- Clear boundaries. An AI agent may only do what its task needs. Why that matters more than trust is in the post Agents need boundaries, not keys.
Typical mistakes
Starting with the tool. Buying an AI tool first and then looking for a task rarely finds a good one.
Starting too big. A project meant to change ten processes at once often fails. One process that runs measurably faster afterwards convinces everyone.
Not measuring. Without numbers from before, you will not know afterwards whether the project paid off. Write down how long the process takes today before you start.
Forgetting the team. AI changes the work of the people who run the process today. Involve them early. They know where things get stuck, and they will have to trust the solution later.
Frequently asked questions
Do we need our own AI experts? Not to start. More important is someone from the business side who knows the process and has time for the project.
How long does a first AI project take? A first solution is usually ready within a few weeks. How long exactly depends mostly on the interfaces to your systems.
Will AI replace our employees? In our projects, AI takes routine work off the team. People check, decide and handle the cases that need experience.
What if no process pays off? Then we tell you after the short review. We do not build an AI project without a clear benefit.
The next step
Which process costs you the most time today? In a free first call we look at your workflows. Afterwards you know where AI pays off and what it costs. More about our work is on the AI consulting page. Or write to us directly.
