AI Literacy Training for Employees

Training packages and pricing

AI literacy training for employees is priced per session, not per attendee. Training can be delivered on-site or online (Zoom); the price is the same either way.

What each package covers — and what it does not

Why the price is not per attendee

In a lecture format there is no difference in workload between speaking to 25 people and speaking to 150: the same preparation, the same talk, the same microphone. That is why we do not charge per head. The real difference is format: in a hands-on workshop every attendee's screen is looked at individually, which puts a physical limit on group size.

How many people can attend

• Essentials — up to 50. It is a lecture format, so a larger room is not a problem.
• Workshop — 25. This is not a pricing limit but a physical one: everyone needs to practise on their own work and receive feedback.
• Company Programme — up to 40.
• Full Programme — up to 100; teams are split into groups of 25 and trained in three separate sessions.

Additional items

• Essentials with 51-150 attendees: +$100. Above 150 attendees we quote separately.
• Workshop and Company Programme: $600 for each additional group of 25, if held on the same day.
• Travel and accommodation outside Istanbul are billed at cost to the client.
• Recording and video rights: $200. Included at no charge in the Company Programme and the Full Programme.

Included in the price

• Trainer, presentation and live demonstration
• Adapting the programme to your company — examples are drawn from your own sector
• A one-page internal usage rule template left with attendees
• Q&A session
• On-site or online delivery

Not included

• Room, projector, sound system and internet connection — provided by the client
• Subscription fees for any AI tools attendees use
• Travel and accommodation outside Istanbul
• Software setup, integration and automation work after the AI literacy training for employees — these are separate services

How it works

1. Intro call (20 minutes). How many people, which departments, what tools are in use today.
2. The programme is adapted. Examples and scenarios come from your sector.
3. Date and package are confirmed in writing.
4. The training is delivered.
5. Follow-up. The Company and Full Programmes include a follow-up session two weeks later and delivery of the written usage policy.

Your people are already using it

The question of whether artificial intelligence will enter your company has already been answered. It entered. It is used while a proposal is drafted, while a customer email is answered, while a messy spreadsheet is cleaned up. The difference is that in most organisations this happens with no visibility and no written rule. That gap is exactly why AI literacy training for employees has stopped being a nice-to-have and become an operational necessity.

The gap has two practical consequences. First, everyone invents their own method, so two people doing the same job produce wildly different quality. Second, because nobody knows what is forbidden, the boundary only gets drawn after an incident. Both cost more than a training programme does.

Banning it does not solve the problem — it hides it

The first instinct in many companies is a ban. The outcome is almost always the same: usage does not stop, it simply moves off the record. The employee opens the blocked tool on a personal phone, signs in with a personal account, and no trace remains.

The human behaviour here is not complicated. Nobody is trying to break a rule; they are trying to finish their work. When someone holds a tool that halves their workload, "do not use it" quietly becomes "do not mention it". A ban therefore does not reduce the risk. It makes the risk unmeasurable.

What works is a three-step approach: provide an approved tool, write the boundary clearly, and make usage visible. Once those exist, there is no longer a reason to stay in the shadows.

The real risk is silent data leakage, not bad intent

The most common problem organisations face is not sabotage. It is an employee pasting a customer list, a draft contract or a fragment of source code into a free tool to move faster. Nobody intends harm; they simply do not know where that data goes, how long it is retained, or who can see it.

What an employee actually needs to know is a very short list. Which data must never be pasted. What the difference is between a corporate account and a personal one. Where the output is checked and by whom. Who to notify when a mistake is spotted. That list fits on a single page. The problem is not its length — it is that in most companies it has never been written.

The difference between corporate plans and free tiers is not only speed or quota. Retention periods, administrator visibility, audit logging and contractual terms all change. Two employees pasting the same text into two different accounts are not taking the same risk.

These systems learn to sound confident, not to be correct

These tools are not built to find the correct answer. They are built to complete the sentence that should come next. Saying "I do not know" is behaviour that has to be deliberately encouraged; the default is to fill the gap. This is not a malfunction — it is the nature of the system, and it is the first thing AI literacy training for employees must explain.

We ran into three examples in our own work, and we use all three when we teach:

• Customer testimonials that never existed. We had reference quotes generated for a homepage. Names, company names, dates — all convincing, none real. Nobody asked the model to invent anything; it was asked to write, and it wrote. Had we not caught it, it would have gone live.
• A claim that grew on its own. A measurement we ran on a single system appeared in the finished text as "we measured across three separate engines". Nobody lied. The sentence simply wanted to sound stronger.
• A system that cancelled its own work. An overnight content routine flagged its own freshly written draft as "this content already exists" — the match it found was the text it had produced minutes earlier. We believed it had been producing for weeks.

Fabrication risk concentrates in numbers, in names of people and organisations, in cited sources and in legal provisions. In other words, precisely where verification is hardest. We examined this failure mode in more depth in our piece on whether AI can actually build a website.

The rule that follows is simple. Treat the tool as a capable intern: fast, tireless, broadly informed — and never allowed to send work to a client unchecked. What we see in companies is that because the tool is fast, the review step is quietly skipped, and the ten minutes saved become a defect corrected weeks later.

Documented training has become a compliance question

European Union artificial intelligence regulation places an obligation on organisations that use AI systems to ensure their staff have a sufficient level of AI literacy. The regulation does not prescribe a specific curriculum, certification or number of hours; the test is whether the training is adequate for the context.

The obligation can extend beyond permanent staff to contractors and service providers operating those systems on the organisation's behalf. Whether a company based outside the European Union falls within scope depends on its sales, premises or customer relationships in Europe. That assessment belongs to your legal team; nothing here is legal advice.

The practical consequence is clear enough. When the question is eventually asked, three artefacts should exist: a curriculum document, an attendance record and a certificate of completion. Training that happened but was never recorded did not happen on paper. Well-designed AI literacy training for employees therefore does more than transfer knowledge — it leaves an audit trail behind it.

What AI Literacy Training for Employees Should Cover

Most offerings on the market are tool demonstrations. A few sample prompts are typed, the room is impressed, and nothing changes the next day. A programme that works builds four layers together.

How the thing works. Conceptually, not technically. Why it fabricates, when it can be trusted, where it is strong and where it is weak. Without this layer everything else stays memorised rather than understood.

How I use it in my own job. Not generic demonstrations but examples from the participant's own desk. A sales example cannot serve the finance team; when it does, nobody can adapt it.

How I review the output. For someone who does not know what to check, speed is not an advantage — it is a faster route to the same mistake. This is the most frequently skipped and most valuable section.

Rules and data security. What must never be pasted, which account to use, how generated content is labelled, and how a mistake is reported.

There is a fifth topic almost nobody teaches: measurement. "We use AI" is not a result. The result is time recovered. An organisation that cannot measure may carry a useless tool for years — or shut down a genuinely valuable one because its benefit was never demonstrated. We showed a concrete example of that measurement logic in our article on how to measure AI visibility.

One warning on measurement: do not decide on short-term fluctuation. A wide average hides a recent collapse, while a narrow window makes a seasonal movement look like a catastrophe. A number without a comparison is not a number.

Who should attend, and who needs what

AI literacy is not an IT department subject. The heaviest users are usually sales, marketing, human resources, finance and customer service — the non-technical side of the business. Uniform AI literacy training for employees therefore does not work.

The model that does work is layered. A shared foundation for everyone: what it does, what it does not do, which data is off limits, how output is checked. Role-specific depth for heavy users, built on examples from their own work. And a separate track for managers: where to invest, how to measure, which decisions stay with a human.

Having the IT team in the room matters, and they are often the most sceptical group present. They are usually right to be. A sceptic is the person naming the risk optimists skipped; the team asking where the data goes, whether anything is logged and who is accountable when something breaks is not slowing the work down — it is keeping it standing.

"Is this going to take our jobs?"

This question comes up in every session. When it is not answered honestly, everything else loses credibility too, because the room can tell when an answer is being dodged.

The honest answer is that nobody knows the employment picture of the coming years, and anyone claiming otherwise is wrong. What is known is narrower: routine, repetitive, single-step work is the most exposed to automation. Meanwhile the person who can supervise the tool, question its output and adapt it to their own work is ahead of both the colleague who avoids it entirely and the one who trusts it blindly.

The purpose of AI literacy training for employees is not dependence. It is command. A programme that will not say this plainly is not serving the people in the room.

Success is measured thirty days later

Everyone leaves the room enthusiastic. By the third week the old habit returns, because nobody follows up. Three things determine the outcome.

Each team picks exactly one task on the way out — a single real job they will attempt that week. Two weeks later there is a short review: what happened, where it stalled, who gave up and why. And in each department one volunteer owns the topic, collecting questions and sharing what worked.

Most organisations buy AI literacy training for employees and skip the follow-up. That is precisely where the difference is made.

Where to start

The starting point is not choosing a tool or writing a policy. It is this question: in this company, who is doing what work, with which tool, using which data? In most organisations the answer is unknown — and because it is unknown, the policy that gets written floats free of reality and the training that gets purchased touches nobody's actual job.

A workable sequence looks like this. First make current usage visible — no bans, no penalties, simply ask. Then run a shared foundation session so everyone learns the same boundaries and an attendance record exists. Then hold a hands-on workshop with the heaviest users, where everyone leaves with something that runs. Then look again after thirty days.

Half-day foundation AI literacy training for employees is the right starting point for most organisations, with a practical session for the team that will actually automate the work. If you would like to discuss scope and duration for your organisation, you can reach us through our contact page. If you are curious how AI describes your company to the outside world, our AI visibility work is a useful place to look next.