AI Training for Teams 2026: Program, Format and Price (from €1,200 net per day)
AI training that actually changes how a team works has three parts: an audit of what the team really does, a closed workshop on your real tasks and tools, then follow-up and a written way of working. At Syntalith it is run by engineers with systems in production, from €1,200 net per day. The AI-Native course for developers is priced after a call.
AI training for a team that actually changes how work gets done has three parts: an audit of what the team does day to day, a closed workshop on your real tasks and tools, and then follow-up plus a written way of working. We run closed AI (Claude) training for teams from €1,200 net per day, typically €1,200–1,500, priced per scope after one call. Developers we point to the AI-Native course on their own repository, priced after a call.
Quick answer
Most AI training ends the same way: enthusiasm on Friday, old habits on Monday. That happens when the day goes on trend slides and prompt tricks rather than the work the team actually does. Training changes how a team works only when it is structured as a process, not as a one-off event. At Syntalith we price it plainly, net:
- free process scan (€0): a 30-minute engineer call plus a written takeaway in two business days, to establish what the team needs before you buy anything,
- AI (Claude) training for a team (from €1,200 net per day): a closed workshop on your tasks, one or two days, online or on-site, for one workshop group (up to ~15 people),
- AI-Native course (priced after a call): agentic work 1:1 or as a team cohort, on your real repository, for teams that write code.
The daily rate usually sits in the €1,200–1,500 net band, and you get the final per-scope quote after one call. The full price list for every line is on the pricing page, and the team offer is described on the AI training for teams page.
How to structure AI training so it changes work, not just slides
Changed work does not come from one day in a room, it comes from three steps around it. The workshop itself is the middle, not the whole.
1. Audit what the team really does. Before anyone stands in front of a group, you need to know where that group spends its time: which documents, emails, analyses and decisions pass through their hands each week, which tools they already have, and where everyone improvises their own way. Without this the program is generic, and a generic program teaches generic things. For us this step lives in the call about the team and the goal, from which the scope is drawn.
2. A closed workshop on real tasks and tools. The day goes on the team's work moved onto the model: a specific quote, a real analysis, correspondence that has to be written anyway, with the output verified on the spot. That is the difference between "I saw how it works" and "I did it on my own task." Across two days, the first goes on hands-on work with the tool, the second on boundaries, escalation, the trail, and a shared way of working.
3. Follow-up and a written policy. What remains after the workshop is a document: data-handling rules (what may be pasted into the model, what may not), boundaries of autonomy, the verification method and the decision trail, plus materials and an attendance list. It is that artifact, not the day itself, that decides whether the team works differently on Monday. Without it, training is theater. You learn what you do, not what you watch, so the audit and the follow-up are not add-ons to the workshop, they are half of it.
Which AI training format to pick: open course, closed workshop, or 1:1
The AI training market looks like one basket, but it is three different purchases at different budgets and for different goals. You choose by who you are training and why, not by the name in the ad.
| Format | Who it is for | What you get | Price (as of July 2026) | When it is the wrong choice |
|---|---|---|---|---|
| Open course (mixed group from various firms) | Individuals, a general introduction to a tool | Knowledge from room examples, a certificate of attendance, no work on your process | Usually PLN 2,000–3,000 net per person (market rates, check with the provider) | When you want to change how a whole team works on your real tasks |
| Closed team workshop (Syntalith) | A whole team, one process, a shared standard | Work on your tasks, boundaries, a trail, a written standard, article 4 documentation | From €1,200 net per day, typically €1,200–1,500, priced per scope | When you are training a single technical person building software |
| AI-Native 1:1 course (Syntalith) | A developer or technical founder on their own repo | A repeatable agentic method on your code: steering, verification, a trail | Priced after a call about the repository, net | When the team does not write code day to day |
An open course is cheap per person and sensible when one or two people want the basics. It loses its point as a way to change how a team works: a mixed group learns from other people's examples, and what will actually work for you is settled only on your own data. A closed workshop reverses that: the price is per day and per group, but the program sits on your process and the way of working stays in the company. The AI-Native course is a separate category for teams that write code; more on the difference is in the guide to AI agent courses and training in Poland.
What AI training for a company costs (price per day)
A closed AI (Claude) training for a team starts from €1,200 net per day. The price is per day and per workshop group (up to ~15 people), not per person, which is the key difference from open courses. For a ten-person team a workshop day works out cheaper per head than sending each person to a separate course at PLN 2,000–3,000, and it teaches on your process rather than someone else's.
Three things move the price: the number of days (a one-day introduction or a two-day workshop with the second day on boundaries and the standard), the number of groups (a larger team is split into groups of up to ~15 and priced in one offer), and the format (online or on-site). The daily rate usually sits in the €1,200–1,500 net band; the final per-scope figure comes after one call. We do not sell seats off the shelf and we do not price per person.
What an AI training program for employees should include
A program that changes work combines hands-on work with a tool and the discipline that makes it trustworthy in a real process. In practice that is six elements:
- Model basics: how models actually work and where they fail, where the merely-plausible answer comes from, before we touch your data at all.
- Context and task: how to give the model context and a task, run longer work on a single document, and when to start a fresh conversation.
- The team's real tasks: documents, quotes, analyses, correspondence moved onto the model, with the output verified on the spot.
- Boundaries and tool choice: when a conversation with the model is enough, when a repeatable workflow is, and when an agent is, and where a human must approve the result.
- Data safety: what may be pasted into AI, what may not, where the model must stop, and how to set escalation before something goes wrong.
- Trail and quality: how to leave a decision trail and measure the quality of work with AI, which at the same time supports the competence obligation under article 4.
What a good program leaves out matters just as much: no hour on "the future of work," no list of a hundred prompts to copy, no promise that the team will "master AI" in a day. Competence is a way of working that stays, not a set of curiosities. For a narrower variant focused on wording prompts, see prompt engineering training for business; for development teams there is Claude Code training.
AI Act article 4: why documenting the training is worth it
This is a real reason to make training leave a trail, but not a reason to panic. Article 4 of the AI Act has applied since 2 February 2025 and requires companies that use AI to ensure an adequate level of AI literacy among the people who use those systems on their behalf. The rule does not prescribe a training format and carries no penalty of its own. It is not a meaningless obligation, though: staff competence is examined as part of market surveillance, and a company that cannot show it took care of it stands in a weaker position when an AI-related incident occurs.
The practical takeaway is simple: a closed training with a program, an attendance list and a named certificate is one recognized way to document a team's competence. Those artifacts stay with you and slot into your own documentation. What article 4 actually requires and what it does not (and why most penalty-scare marketing misses the rule) we unpack in AI Act article 4 literacy training. A caveat: our materials are an input to your documentation, not legal advice.
When NOT to book AI training
Honestly: there are situations where paid training is a bad buy, however fashionable the topic.
- A free webinar or the docs are enough. If the goal is a one-off, with no project where you will use the knowledge right away, start with the free materials from the tool vendors and public tutorials. Paid training makes sense when you have a real process on the table.
- No one will change the process afterward. A day in a room without a subsequent change to the rules and without a written standard is theater. If no one touches the process after Friday, save the budget, because the enthusiasm will evaporate by Monday.
- You want a trends lecture. If you want inspiration rather than work on your own tasks, that is a different product and a different provider. We run a workshop on your process, not a talk.
- The team writes code and wants to build software with agents. Then the right choice is a 1:1 course on their own repo, not a workshop for a non-technical team, and we say so plainly before quoting.
It is also worth seeing the wider picture before you spend anything. Statistics Poland (GUS), in data compiled by the Polish Economic Institute (December 2025), reports that 8.7% of Polish companies used AI in 2025 (up from 5.9% a year earlier), 42% among large firms and 6.1% among small ones. EY Poland, in an April 2026 report covering 497 medium and large firms, reports that about half of them cite disappointment or an incomplete return on AI. The common denominator behind these numbers rarely sits in the model. It is usually the lack of competence and rules among the people meant to use AI, and that is exactly the gap good training closes, provided it is structured as a process rather than an event.
How to start
The cheapest sensible first step is to establish what the team actually needs, not to buy a training day straight away.
- Book a free process scan and tell us who on the team uses AI or is meant to start.
- Prepare: which tasks the team does each week, which tools it already has, where everyone improvises their own way, and a goal bigger than curiosity.
- After the call you get a recommendation: a closed workshop on your process, the AI-Native course for developers, or an honest "free materials are enough for now."
Book a free process scan | AI training for teams | See pricing
FAQ
How much does AI training for a company cost?
A closed AI (Claude) training for a team starts from €1,200 net per day, typically €1,200–1,500, priced per scope after one call. The price is per day and per workshop group (up to ~15 people), not per person. Open courses on the market usually cost PLN 2,000–3,000 net per person (market rates, check with the provider). The AI-Native course for developers is priced after a call about your repository.
Open course or a closed team workshop?
An open course fits when one person wants a general introduction and learns from examples in the room. A closed workshop fits when you want to change how a whole team works on your real process, with one shared standard and documentation for AI Act article 4. If you are training a single technical person building software, the right choice is a 1:1 course on their own repo, not a team workshop.
What should an AI training program for employees include?
A program that changes work combines hands-on work with a tool (Claude or ChatGPT Work) on the team's real tasks with the discipline that makes it trustworthy in a real process: context and task, the boundaries of what may be handed to AI, spotting merely-plausible answers, data-handling rules (what may be pasted, what may not), and a decision trail. What remains at the end is a written way of working, materials and an attendance list, not a set of prompt tricks.
When is AI training a bad buy?
When a free vendor webinar or public documentation is enough, because the goal is a one-off with no project behind it. When the team gets a training day but no one changes the process or the rules afterward: that is theater, enthusiasm on Friday and old habits on Monday. And when you want a trends lecture instead of work on your own tasks.
Do you have to document AI training for the AI Act?
Article 4 of the AI Act has applied since 2 February 2025 and requires ensuring an adequate level of AI literacy among people who use AI on the company's behalf. The rule does not prescribe a format, but a closed training with a program, an attendance list and a named certificate is one recognized way to document competence. This is not legal advice; the materials are an input to the company's own documentation.