AI training · For executives
AI training for executives
A session for the people who sign the decision, not a set of tool tricks. What an executive has to be able to judge, where AI genuinely changes a process, how to choose what to automate first, and how to tell real progress from theatre.
Contents
Definition
What is AI training for executives?
AI training for executives is a session for decision-makers that teaches judgement about AI rather than use of it: which processes to change first, which risks are acceptable, what internal rules apply to everyone, and which measures the result will be judged on. The audience is the owner or managing director, the HR director and the heads of department, meaning the same people who sign the decision.
An executive does not need to be able to build an AI system. They need to be able to appraise one: whether the task suits it, what an error costs, where the data ends up, and who signs the output. Everything else can be bought as a service, and does not have to be learned. The Lithuanian edition of this page is at AI mokymai vadovams, and the programme this session sits inside is AI training for companies.
Why this is a management question
The biggest difference is made by the environment, not the employee
Two large 2025 and 2026 workplace surveys point the same way: what a company gets out of AI tracks the behaviour of its leadership and the shape of its processes more closely than the motivation of individual staff.
The mechanism behind these numbers, and what restarts a stalled rollout, is set out in why AI rollouts stall.
The decision layer
What an executive has to be able to judge
Six questions an executive should be able to answer without help. In the session each one is applied to your actual processes rather than to worked examples from somebody else’s company.
01
Whether the task suits AI at all
An executive does not need to be able to build the solution. They need to be able to separate work where these models are strong (text, classification, summarising, searching a large body of material) from work where they are unreliable (exact arithmetic, rare exceptions, decisions taken without a check). That boundary is the single most common reason a project fails.
02
What an error costs
On an internal summary the cost of a mistake is small. On a commercial proposal or a customer invoice it is not. That number decides where one person’s review is enough, where a second pair of eyes is required, and where the answer is simply not to automate.
03
Where the data ends up
Which data goes into an external service, whether the account is a company one or a personal one, what contractual terms apply, and whether the process survives a GDPR question. This is a management decision, not an employee one, and it is the decision employees most often make by default when nobody else makes it.
04
Who is accountable for the output
A document drafted with AI needs a person who signs it. Where there is none, the error is found by the customer. Accountability is settled by one line in a policy, but only if somebody writes that line.
05
Whether the process changed, or one step got faster
If a seven-step process still has seven steps and one of them is now quicker, total elapsed time barely moves. Real change comes from redrawing the process, not from inserting a tool into an old one.
06
How this will be measured
Before anything starts, you need to know which measure you are watching and what it reads today. Without a baseline, any result six months later is an opinion rather than a measurement, and the discussion becomes unwinnable in both directions.
Process
Where AI actually changes a process
The common mistake is to drop AI into an unchanged process and wait for a result. If a report is still produced in the same seven steps and only one of them is faster, total elapsed time barely moves, and the licence is already being paid for.
Real change comes from three moves. The first is removing steps: when information is found immediately, the searching, the asking and the waiting disappear as stages. The second is removing queues: a process in which three people hand a document along shortens not because anybody types faster, but because the queue is gone. The third is changing scope: work you previously did not do at all, because it was too expensive to be worth it, becomes possible. An executive’s job is to see which of the three is available in a given process, because that is what decides whether the project is worth starting.
Where the difficulty actually sits
Boston Consulting Group asked 1,000 CxOs and senior executives from 59 countries in Asia, Europe and North America about AI maturity, and states that its experience, corroborated by that research, indicates that about 70 percent of the challenges relate to people and process, about 20 percent are technology issues, and only 10 percent involve AI algorithms, which often occupy a lot more organisational time and resources. Its recommendation follows the same split: focus 70 percent of effort and resources on people-related capabilities, 20 percent on technology, and 10 percent on algorithms.
If the process review has not been done, it is a separate piece of work and it belongs before the training rather than after it. In that case we start with scoping, and run the executive session once it is known which two or three processes are being changed. How that sequence runs is set out on how we work. For a group operating across several countries or sites, the scoping question usually starts at multi-location operations.
Selection
How to decide what to automate first
The session uses a plain selection table. Every candidate is scored on four criteria, and the first project is chosen not for how impressive it is but for where the cost of an error is low and the repetitions are many.
| Criterion | When the reading is favourable | When to leave it for later |
|---|---|---|
| Frequency of repetition | The task is done daily, or several times a week, by the whole team | The task comes up once a quarter and is different every time |
| Cost of an error | A mistake is caught by a member of staff before it is used | A mistake is seen first by the customer, a regulator or a court |
| Sensitivity of the data | The work uses public or anonymised material | Personal, health or commercially confidential data is unavoidable |
| Checkability of the output | The result can be checked in a few minutes and somebody signs it | Checking takes as long as doing the work again |
That table also explains why first projects are rarely impressive. The usual first choice is a dull internal process where nothing dramatic can go wrong and the result can be measured inside a month. The impressive projects go second, once the team has both experience and rules. Which systems that first project usually turns out to be is covered under solutions.
Where AI reliably gets worse
In a pre-registered field experiment run with Boston Consulting Group, 758 consultants worked on realistic consulting tasks. For tasks inside the frontier of AI capabilities, consultants using AI completed 12.2 percent more tasks on average and completed them 25.1 percent more quickly, with more than 40 percent higher quality compared to a control group. For a task selected to be outside that frontier, the same population using AI were 19 percentage points less likely to produce correct solutions than those without it. The boundary between those two regions is invisible from the inside, which is why an executive has to fix it in rules rather than leave each employee to find it alone.
Policy
How to set an internal AI use policy
A policy nobody reads is the same as no policy. So the target is one page, written during the session, out of what the team actually tried that day.
The full structure of that document, section by section, is on its own page: the internal AI use policy.
Write down what is already in use
Where part of the team already uses AI unofficially, a policy written without knowing that simply pushes the use further into the shadows. Start with an honest inventory and no sanctions for the past.
Draw the data boundary in three categories
Public information, internal information, and the material nobody puts into an external service. Three categories is about what a person retains. Fifteen levels turns into zero compliance in practice.
Decide when a human check is compulsory
Tie the check to the cost of an error rather than to the technology: anything that goes to a customer, anything with legal effect, and anything that lands in a report is checked by the person who signs it.
Name an owner and a review interval
One person, one date. Without both, the policy goes stale within a quarter, because the tools and their terms move faster than that. Every six months is a workable rhythm.
Publish it as a permission, not a prohibition
A document containing only prohibitions suppresses use in the places where it would have helped. Writing down clearly what is allowed removes the personal risk from the employee, and that personal risk is the largest single obstacle to regular use.
The technical side of data handling, once AI is being deployed as a system rather than used as a tool, is set out on the security page. What the AI Act does and does not require is in our explainer on Article 4.
Measurement
How to tell real progress from theatre
On the left, the measures that look good in a board pack. On the right, the measures that actually say something. The executive session moves the reporting to the right-hand column.
| Looks like progress | Actually shows progress |
|---|---|
| Number of employees trained | Share of employees still using AI at least several times a week two months on |
| Satisfaction form filled in on the day | Elapsed time of one named process before and after, measured the same way both times |
| Number of licences issued | Share of licences in active use, and which departments are not using theirs |
| Number of pilots running | Share of pilots still in daily use six months later |
| An impressive demonstration to the board | One dull process that from now on always happens the new way |
| A policy has been produced | A policy the team can restate in its own words |
One practical instruction we give everybody: write the starting measures down before the first session. Six months later nobody will remember how long the process used to take, and the discussion about value turns into an exchange of impressions. Ten minutes of work at the start settles the whole later argument.
What the evidence base behind this page actually is
| Study | Year | Sample | Key finding | Confidence |
|---|---|---|---|---|
| BCG, AI at Work 2025Source: BCG, AI at Work 2025 | 2025 | 10,635 respondents across BCG’s listed key markets; the leadership-support cut is n=3,537 frontline employees | Regular AI use runs 82% with clear leadership support against 41% without; only 25% of frontline employees report sufficient support | Medium. Self-reported survey, cross-sectional, so it establishes association rather than cause |
| BCG, Where’s the Value in AI?Source: BCG, Where’s the Value in AI? | 2024 | 1,000 CxOs and senior executives from 59 countries | About 70% of AI implementation challenges relate to people and process, 20% to technology, 10% to algorithms | Medium. BCG presents it as its own experience corroborated by the survey, not as a survey output on its own |
| Microsoft, Work Trend Index 2026Source: Microsoft, Work Trend Index 2026 | 2026 | 20,000 knowledge workers who use AI at work, across 10 markets, fielded 18 February to 7 April 2026 | Organisational factors account for more than twice the reported AI impact of individual factors, 67% against 32% | Medium. Self-reported, and restricted to people already using AI at work |
| Dell’Acqua et al., HBS WP 24-013Source: Dell’Acqua et al., HBS Working Paper 24-013 | 2023 | 758 consultants, pre-registered field experiment with three randomised conditions | Inside the capability frontier: 12.2% more tasks, 25.1% faster. Outside it: 19 percentage points less likely to be correct | High for the causal direction, because of randomisation. It is a working paper, and the task set is consulting work |
Regulation
What Article 4 actually asks of a board
Executive AI training is often sold on the claim that the EU AI Act requires you to train your staff. We will not tell you that, because it is wrong in two separate ways, and a board that acts on it buys the wrong thing.
What Article 4 says today is this. Providers and deployers of AI systems shall take measures to support the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in. That wording is new. Regulation (EU) 2026/1744, the Digital Omnibus on AI, replaced Article 4 in place, and it entered into force on the third day following its publication in the Official Journal of 24 July 2026, so the new text has applied since 27 July 2026. The previous version required deployers to ensure, to their best extent, a sufficient level of AI literacy. The replacement adds a sentence that was not there before: this obligation does not require providers or deployers to guarantee any specific level of AI literacy of any individual (EUR-Lex, Regulation (EU) 2026/1744). The duty to take measures still binds, and it has applied since 2 February 2025.
The second error is about money, and it needs stating precisely, because the precise version and the loose version point in opposite directions. Article 4 is not listed in Article 99(4), and never was. That paragraph enumerates the provisions whose breach attracts administrative fines of up to EUR 15 000 000 or, if the offender is an undertaking, up to 3 percent of its total worldwide annual turnover for the preceding financial year, whichever is higher, and we read the enumeration: obligations of providers pursuant to Article 16, of authorised representatives pursuant to Article 22, of importers pursuant to Article 23, of distributors pursuant to Article 24, of deployers pursuant to Article 26, requirements of notified bodies pursuant to Articles 31, 33 and 34, and transparency obligations pursuant to Article 50, with point (da) covering Article 25(2) and (4) inserted by the 2026 Regulation. Article 4 is not among them (EUR-Lex, Regulation (EU) 2024/1689). So Article 4 carries no EU-level fine ceiling.
That is not the same thing as carrying no consequence, and a board should not be told it is. Article 99(1) separately obliges Member States to lay down the rules on penalties and other enforcement measures applicable to infringements of the Regulation by operators, and to make sure they are properly and effectively implemented. Regulation (EU) 2026/1744 made that paragraph broader rather than narrower: where the original list of national measures read “warnings and non-monetary measures”, the replacement reads “administrative fines, warnings and non-monetary measures”, and “infringements of this Regulation” became “any infringement of this Regulation” (EUR-Lex, Regulation (EU) 2026/1744). The Commission’s own Q&A matches: national market surveillance authorities could impose penalties and other enforcement measures to sanction infringements of Article 4, on the basis of national laws, and those authorities started supervising and enforcing as of 2 August 2026. The accurate board-level statement is therefore this: Article 4 is a binding obligation, its consequences run through national law rather than through the Article 99(4) ceiling, and the EUR 15 million figure that training is often sold on was never attached to it.
The practical reading is the modest one, and it is neither of the two extremes on offer in the market. AI literacy is not a formality to be bought with a certificate, and it is not an EUR 15 million exposure. It is a binding duty of effort discharged through a set of measures a company chooses for itself, proportionate to what its people do and what its systems are used for, and answerable to a national authority rather than to a headline fine. If a supplier tells you the Regulation demands a particular course, a particular certificate or a particular policy template, it is fair to ask them to point at the provision. The full text, old and new side by side, is in EU AI Act Article 4 and the AI literacy rule. This is general information, not legal advice.
What the European Commission says in its own words
Asked what the format of a mandatory AI training in companies should be, the Commission’s AI literacy Q&A answers that “There is no one size fit all when it comes to AI literacy and no strict requirements or mandatory trainings are imposed.” Asked whether specific certificates are needed, it answers: “There is no need for a certificate. Organisations can keep an internal record of trainings and/or other guiding initiatives.” Asked whether an organisation shall set up an AI governance board, it answers: “No, no specific governance structure is mandated to comply with article 4 of the AI Act.” It also confirms that supervision and enforcement of Article 4 sit with national market surveillance authorities rather than with the AI Office.
Session
How the executive session runs
Format: three to four hours on site with the leadership team, or two 90-minute parts remotely with an exercise in between. One to one we work in sessions of 60 to 90 minutes, when a single executive’s area needs going into properly. A group is usually between three and ten people: the owner or managing director, the HR director and the heads of department.
The session is built on your processes. Beforehand we ask for a short description of two or three pieces of work you are considering changing, and of what the team already uses unofficially. The exercises come out of that, which is why the session does not discuss imaginary companies.
Afterwards an executive has three things: two or three chosen processes with their starting measures recorded, a draft internal AI use policy, and named owners with a review date. Not a certificate, but working documents that can go in front of a board or a shareholder.
The usual sequence is the executive session first, then team sessions under the general AI training programme, then a return session after four to six weeks. Where an AI system is being deployed at the same time, the training goes into the deployment stages rather than running separately. Function-specific programmes exist for accounting and finance teams, legal teams, and for teams that will be working alongside AI agents. Scope and price are agreed per engagement on a call.
FAQ
Frequently asked questions.
Founder & CEO, AInora
Building AI digital administrators that replace front-desk overhead for service businesses across Europe. Previously built voice AI systems for dental clinics, hotels, and restaurants.
View all articlesOne conversation, two decisions.
An hour to go through your processes and pick the two worth changing first. If it turns out training is not what you need yet, we say so.