AI Training for Executives and Managers 2026: What and How (from €1,200/day net)
AI training for executives is not a prompt course, it is decision literacy: what to buy and what not to, how to read offers and spot agent washing, how to set a success metric, an owner and a shut-down point, and how to work with AI yourself within data boundaries. At Syntalith engineers with systems in production run it, from €1,200/day net, with scope tailored to the board.
AI training for executives and managers is not a prompt course, it is decision literacy: what to buy and what not to, how to read offers and spot agent washing, how to set a success metric, an owner and a shut-down point for a project, and how to work with AI yourself within data boundaries. At Syntalith engineers with systems in production run it, from €1,200 net per day, with scope tailored to the board and a per-scope quote after one call.
Quick answer
A board does not need to write prompts. It needs to be able to make four decisions that cannot be outsourced: what to buy and what not to, whom to trust, who owns the result, and where the boundary of its own work with AI lies. A prompt course teaches none of these, yet these are what decide whether the money spent on AI pays back. At Syntalith we price training plainly, net:
- free process scan (€0): a 30-minute engineer call plus a written takeaway in two business days, to establish whether there is even a process that is a candidate for AI before you buy anything,
- AI training for executives and managers (from €1,200 net per day): a closed workshop built around decisions, not tools, with scope tailored to the board,
- AI workshop for the whole team (from €1,200 net per day): working with the tool on real tasks, described separately in the AI training for teams guide.
The daily rate is the same as for the team workshop, and for the board the format is usually shorter, so we settle scope and amount after one call. The full price list is on the pricing page, and the workshop offer is described on the AI training for teams page.
How AI training for executives differs from team training
The team learns to work with the tool; the board learns to make decisions about the tool. These are two different products, because the team runs a process while the board decides which process enters AI at all, at what price, and on whose accountability. That is why an hour on "a hundred prompts" that will be stale in a quarter makes no sense for a board. Four decisions that stay do.
This table sets out those four blocks. The last two columns matter most: the boundary shows where the board's competence ends, and the outcome shows what the workshop actually leaves behind.
| Decision block | The question the board can answer | Boundary of the board's role | What the workshop leaves behind |
|---|---|---|---|
| What to buy and what not to | Is this a job for automation, an agent, or is a subscription to a ready tool enough? When NOT to buy? | The board decides category and budget, it does not design architecture | A "buy / do not buy" decision based on category, not on a marketing slogan |
| How to read offers | How to tell that an "agent" is a chatbot in new packaging (agent washing)? | Not a technical audit; the board asks one question and listens to the answer | A filter on an offer before it reaches the budget and a contract |
| Governance and accountability | Who owns the project, what is the success metric, and when do we shut it down? | The board sets the owner, metric and decision point, it does not run the project day to day | Every AI project has a metric, an owner and a designed shut-down point |
| The manager's own work with AI | How to prepare a brief, a summary and a decision with AI, without handing it the decision? | Sensitive data stays out of the model without arrangements; output is always checked by a human | Faster decision prep, with a trail and within data boundaries |
Below we unpack each of these four blocks. This is the whole content of the executive workshop, in the same order we run the day.
What to buy and what not to: automation, an agent or a subscription
The board's first decision is not "AI or not," it is "which category." Three purchases have similar names and differ in price and risk by an order of magnitude. A subscription to a ready tool (Claude for Work, ChatGPT Business) gives the team a model for their own work and costs a per-seat monthly subscription, an order of magnitude less than any implementation. Automation is a built system that handles one repeatable process end to end, from €3,500 net. An agent is needed only once the process has many paths, crosses several systems and requires decisions within the boundaries you set, from €6,000 net.
The board does not have to be able to build this. It has to be able to recognise which category is in play before it approves a budget, because the most common mistake is buying an agent where a subscription or a simple automation would do. When not to buy at all: when the process happens rarely, when the rules change every week and live in someone's head, or when a ready SaaS solves it at a fraction of the price. How to tell these categories apart step by step, we explain in the guide on what an AI agent is.
How to read offers and spot agent washing
The second decision is whom to trust, and the market makes it hard on purpose. Gartner estimates that of the thousands of vendors marketing "agentic AI" only about 130 are real; the rest is described by the term "agent washing," a chatbot in new packaging sold as an agent. That means the board chooses a large part of the project risk at the vendor stage, not the technology stage.
There is one question that exposes this before you sign: what exactly will this system do on its own, and how will I know it did it. A vendor who can name neither a success metric nor the point at which they would advise against building further is selling a project with no criterion and no brake. The board does not need to run a technical audit to catch this; it is enough to ask that question and hear whether the answer is concrete or just another slogan. How to read such offers point by point, we set out in why AI projects fail, which this workshop draws on.
Governance and accountability: metric, owner, shut-down point
The third decision is the one most often skipped, and it decides the outcome of the money. The board owns three things no vendor can deliver for the company: the success metric (the number by which we will know it worked), the business owner (who exactly is accountable for the result and for changing how the team works), and a decision point designed up front for "continue or shut down." A project without these three drifts until the budget runs out.
This is not theory. Gartner, in a January 2026 article, reports that at least 50% of generative AI projects were abandoned after the proof-of-concept stage by the end of 2025, while the same firm's earlier forecast said 30%; reality exceeded it. The common denominator rarely sits in the model. Usually it is the missing metric, owner and decision point, which is exactly what the board sets, not the engineer. Abandoning a pilot on criteria agreed up front is not a failure either, it is risk management working: the company spent little and learned a lot.
On top of this comes a management obligation, not a technical one. Article 4 of the AI Act has applied since 2 February 2025 and requires ensuring an adequate level of AI literacy among the people who use these systems on the company's behalf. It is an organisational obligation, so ensuring it falls to the board, not to an individual employee. A written AI policy that sets the rules for working with data and the boundaries of autonomy is one of the tools the board uses to meet it; we unpack it in the piece on the company AI policy. A caveat: this is not legal advice, and our materials are an input to the company's own documentation.
The manager's own work with AI: briefs, summaries, decisions
The fourth decision is the most practical, because it concerns your own work, not someone else's. A manager can use AI to prepare a brief for the team, to assemble a summary from a long document, and to lay out options ahead of a decision. This genuinely shortens preparation, provided it stays within two boundaries. First: sensitive and confidential data does not go into the model without agreed rules and the right plan (a business plan, not a consumer one, with a data-processing agreement). Second: AI prepares the input to the decision, but the human makes the decision, and the output is always checked, because a model can give a merely plausible answer delivered in a confident tone.
In the workshop the manager does this on their own real task, not on an example from the room: they take material that has to be prepared anyway and go through it with the model, verifying the result on the spot. The difference between "I saw how it works" and "I did it on my own brief" is exactly the one the board comes to a workshop for, rather than to a lecture.
How much executive AI training costs and how long it takes
The rate is the same as for the team workshop: from €1,200 net per day, typically €1,200–1,500, with a per-scope quote after one call. For the board the format is usually shorter than a full team-workshop day, because there are four decision blocks and no work on a volume of tasks. That is why we do not quote a separate "board price": we tailor the scope to the number of people and the goal, and give the amount once that scope is set. The price is per day and per group, not per head, and we include logistics (online or on your site) in the quote.
When NOT to book executive AI training
Honestly: there are situations where this workshop is a bad purchase, however timely the topic.
- The board wants a list of tools on slides. If the expectation is a review of fashionable tools and trends, that is a different product and a different vendor. We run a workshop about decisions, not a lecture about the market, and we say so plainly before quoting.
- The company has no process that is a candidate for AI. If there is not yet a single process about which a "buy or not" decision can be made, decision training has nothing to attach to. Then the first step is a free process scan, not a paid workshop.
- No AI decisions are planned in the coming months. Decision literacy fades if there is nothing to apply it to. If the topic is distant, start with free materials and come back when a real decision appears.
It is also worth seeing the wider picture. Poland's statistics office GUS, in data compiled by the Polish Economic Institute (December 2025), reports that in 2025 AI was used by 8.7% of Polish companies, and by 42% of large ones. Rising adoption means more AI decisions, not fewer, taken by boards that rarely have the apparatus for them. That is exactly the gap a decision workshop closes, as long as there is something to decide.
How to start
The cheapest sensible first step is to establish whether there are real AI decisions to take in the company, not to buy a training day straight away.
- Book a free process scan and tell us which AI decisions the board faces in the coming months.
- Prepare: which processes are candidates for AI, which offers have already reached you, and who on the business side could own a project.
- After the call you get a recommendation: a decision workshop for the board, a task-based workshop for the team, or an honest "a scan and free materials are enough for now."
Book a free process scan | AI training for teams | See pricing
FAQ
How is AI training for executives different from team training?
Team training teaches working with the tool on real tasks: context, data boundaries, a decision trail. Executive training teaches decisions about AI: what to buy and what not to (automation, an agent or a subscription), how to read offers and spot agent washing, how to set a success metric, an owner and a shut-down point, and how to work with AI yourself within data boundaries. It is decision literacy, not a list of prompts on slides.
How much does AI training for executives cost?
The rate is the same as for a team workshop: from €1,200 net per day, with a per-scope quote after one call. For the board the format is usually shorter than a full workshop, so we tailor the scope to the number of people and the goal, and give the amount once that scope is set. We do not price per head or sell an off-the-shelf deck.
How do you spot agent washing in an offer?
By one question worth asking before you sign: what exactly will this system do on its own, and how will I know it did it. A vendor who cannot name a success metric or the point at which they would advise against building is selling a project with no criterion and no brake. Gartner estimates that of the thousands of vendors marketing agentic AI only about 130 are real; the rest is described by the term agent washing.
Is the board accountable for AI under 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 the people using these systems on the company's behalf. It is an organisational obligation, so ensuring it falls to the board, not to an individual employee. An AI policy, a success metric and a named project owner are management tools, not a formality. This is not legal advice; the materials are an input to the company's own documentation.
When is AI training for executives a bad purchase?
When the board expects a list of tools on slides and a trend overview, because that is a different product and a different vendor. When the company has no process yet that is a candidate for AI: then the first step is a free process scan, not training. A paid board workshop makes sense when there are real AI decisions to take in the coming months.