Guide

AI training for companies: what it should cover and how to choose

If you are looking for AI training for your company, the chances are your people already use AI, just not at work, not on real tasks, or not in a way that shows up in the results. This guide covers what AI training for a team (company training rather than a course for one person) should include to change that, which formats exist, how to choose a provider and how to tell afterwards whether anything changed.

The short answer: training that works is a session where every participant builds something for their own role, using their own work, and leaves with something finished plus clear rules about what is allowed on Monday morning.

What is AI training for a company?

AI training for a company is a structured effort to get a group of people using AI in their everyday work. That sounds obvious, but the last part, everyday work, is where most training falls short.

Most of what is sold as AI training is knowledge transfer. Someone explains what large language models are, shows a few impressive examples, walks through prompting tips and sends everyone home with the slides. People leave better informed and go back to working exactly as before. Adoption is a habit problem rather than an information problem, and habits do not change because someone sat through a talk.

Training that changes behaviour looks different. It is hands-on, it starts from the participants' own tasks, and it ends with each person holding something they built and will use the next day. That gap, between understanding something and having done it, is what separates useful training from a pleasant afternoon.

What AI training for employees should cover

Whoever you hire, insist on these five elements. If the first three are missing, what you are buying is a talk.

Tasks from their own week: Participants bring real work to the session. A recruiter at a 40-person agency builds a shortlisting workflow, an account manager builds something that prepares a quarterly client review, a finance lead builds a first draft of the month-end commentary. Generic demos create interest but rarely ownership.
Everyone builds, nobody just watches: Each person builds their own thing instead of doing group exercises or following a shared screen. The difference between “I built this” and “this was shown to me” decides whether it is still used the following week. It matters most for the sceptics: a team that is wary of AI usually comes round once people have made something work themselves.
Ground rules set in the same room: Which tools are approved, what data can go where, who decides. Clear rules are what make people willing to use AI on real work instead of harmless test tasks. For a UK firm that means being specific about personal data.
The tools as they are today: Many people formed their view of AI from what the tools could do a couple of years ago, and training built on that picture teaches the wrong things. Participants need to see what current tools handle now, including, for some roles, Claude Code for non-developers, which lets people without a technical background build small internal tools.
A named next step: The session should end with a decision on who does what, by when, and who owns AI adoption internally once the trainer has left.

An AI course or team training: which do you need?

Someone searching for an AI course is usually looking for something for themselves: the fundamentals, at their own pace. There are good free options. Elements of AI, originally built in Finland, is a free online course on how AI works, and the major AI vendors publish their own free learning material. For one person who wants to understand AI, that is plenty.

AI training for business solves a different problem: a whole group changing how it works, at the same time, on its own tasks. Many AI courses for business are individual courses sold in bulk, with each person working through modules alone. A course gives one person knowledge; team training gives a team a shared way of working and shared rules. The difference shows a few weeks later, when the course graduate knows more but the team actually works differently.

That is also why whole teams beat hand-picked individuals. Send two people on a course and they come back to colleagues whose habits have not moved. When PwC rolled out Claude training, it started with partners and senior leaders before widening it to tens of thousands of staff (we looked at what PwC is teaching in its Claude programme). Leaders going first matters just as much in a 60-person firm, for the reasons in our guide to the CEO's role in AI adoption.

A rule of thumb: if you want one person to understand AI, a course will do. If you want the work to change, the whole team needs to take part, in training built around your tasks and the AI tools you actually use. What to prioritise afterwards is covered in AI strategy for companies.

Corporate AI training formats: workshop, course or programme?

Workshop (half a day to a full day). Best when you want to move a whole team at once and give everyone the same starting point. The rules are set once and the group gets a shared vocabulary. For most firms it is the right first step, and our guide to what to expect from an AI workshop and how to pick one goes into the detail. Our own version, Deployed Kickstart, is a half-day session where teams build on their real work.

Course (several sessions). Best when the content is technical and needs building in layers, or when people need time between sessions to practise. The risk is that momentum drains away between sessions if nobody owns the topic internally.

Ongoing programme. Best when you want adoption to survive the first few weeks, which is where most efforts stall: in the gap between the training room and the normal working week. Why most AI workshops fail looks at that gap in detail.

On site or remote? Both work. In person gets better attendance and better conversation, especially when there is scepticism to work through, and it is easier to help someone who is stuck without them having to ask in front of everyone. Remote suits teams split across offices or working hybrid, as long as it stays hands-on. A remote session that turns into a screen-share is a lecture again, so insist that everyone builds something wherever they are sitting.

Is it worth waiting before you train your team?

The most common objection to AI training is that it would be better to wait until things settle down. It is a reasonable instinct and an expensive one, because many of your people are probably using AI already, just not for work.

Anthropic's Economic Index matches real Claude conversations to types of task, country by country. In the May 2026 snapshot the UK scores 3.35 on Anthropic's AI Usage Index, meaning its share of Claude usage is more than three times what its working-age population would predict, which ranks it 16th of 121 countries. Yet a smaller share of that use looks like work than the global average: 39.0% against 43.4%. Personal use runs above average, at 43.5% against 40.2%, and coursework at 17.5% against 16.5%.

One reasonable reading: interest in AI is high in the UK, but less of it has found its way into the working day. If that holds for your team, the skill is closer than you think, and what is missing is permission, direction and a link to real tasks, which is exactly what training should supply. The same snapshot shows UK users leaning a little more towards working with the AI back and forth (augmentation 55.2% against 51.4% globally) than handing it whole tasks (automation 44.8% against 48.6%). Handing over a well-defined task from start to finish is a habit worth teaching deliberately.

About the data: it measures observed use of one AI product, from anonymised and aggregated conversations. It says nothing about who the users are or about employment, and it is a single snapshot, so it cannot show whether anything is rising or falling. Sweden shows a similar pattern (index 2.98, work share 38.1%).

How to measure whether AI training worked

Satisfaction scores are the wrong yardstick. A feedback form where everyone ticked “very useful” says nothing about whether the work changed.

Four to six weeks after the session, check three things: how many people use AI on real tasks each week, which tasks have moved, and what got faster against a number you recorded beforehand. Set that follow-up date when you book the training, or it will not happen. We set out the five numbers that show real AI adoption in a separate guide, and measuring the return in the first 30 days covers the short-term version.

Good results are concrete and slightly dull to describe. In our own client work they look like this: around 30 hours a week of marketing capacity returned at Après, around 15 hours a week of reporting removed at tm:rw, and a team at Pophouse that went from theory to ownership in a single day. Ask any provider for results in that form: hours per week, tied to a named task.

How to choose an AI training provider

Not every company should start with training. If you do not yet know which work AI should take on, an AI consultant or a short audit may be the better first step, which is why our own advisory work can begin either with a half-day Kickstart or with a few weeks of audit and discovery.

If training is the right place to start, ask these five questions before you book anyone. The answers sort serious providers from the rest quickly.

“What will each person build during the session?”: If there is no concrete answer, it is a lecture. A good answer describes something tied to the participant's own role.
“How will you prepare for our business specifically?”: A good provider maps the common tasks for each role before the session. A weak one runs the same slides for every client.
“Do you set rules for data and tools?”: If governance is not included, you will end up with a team that is enthusiastic but unwilling to use AI on anything that matters.
“What happens the week after?”: Ask specifically. That is where most initiatives die, and a provider without an answer has not thought about the problem.
“Who on your side has done this themselves?”: The difference between someone who has read about AI adoption and someone who has pushed it through a real organisation shows within minutes.

Frequently asked questions

What is AI training for employees?

A structured programme that gets a group of staff using AI in their daily work, rather than just knowing about it. Good AI training for employees is hands-on: people work on their own tasks, each builds something during the session, and the team leaves with clear rules on approved tools and data. That combination decides whether anything is still in use a week later.

How long should AI training take?

Half a day, around three hours, is enough to move a whole team from theory to each person having built something useful. Any shorter and people do not have time to build anything themselves, so the session slides back into a lecture. Longer formats make sense mainly when the content is technical and needs building over several sessions.

What is the difference between an AI course and AI training for a business?

An AI course gives one person knowledge of how AI works, often self-paced and sometimes free, such as Elements of AI. AI training for a business gets a whole team to change how it works, together and on its own tasks, with shared rules on tools and data. If you want one person to understand AI, a course is enough; if you want the work to change, the whole team needs to take part.

Does AI training work remotely?

Yes, provided it stays hands-on. Remote training where people watch a shared screen turns back into a lecture and loses most of its effect, so insist that every participant builds something for their own role. Remote suits distributed and hybrid teams, while in-person sessions work better when there is scepticism to work through.

Do employees need technical skills before AI training?

No. Good AI training assumes no technical background and no coding. It starts from tasks people already know inside out and shows how AI handles them, and many participants turn out to have more experience than they think because they already use AI outside work.

Do we need an AI policy before we train staff?

Not necessarily, but you should settle the rules at the same time. Without them people will not use AI on anything that matters, and the training delivers less than it should. The practical route is to set the rules during the training, so decisions on approved tools and which data can go where are made in the same room where the work changes.

Is AI training a legal requirement for UK companies?

For most UK firms the obligations come from existing law rather than an AI-specific training duty: under UK GDPR you are expected to make sure staff handle personal data properly, and that includes what they put into AI tools. Firms within the scope of the EU AI Act, for example because they place AI systems on the EU market, must take steps to ensure sufficient AI literacy among their staff. If you are unsure where you stand, check with a legal adviser.

How do you know if AI training has worked?

Look at behaviour a few weeks later rather than satisfaction on the day. Check how many people use AI on real tasks each week, which tasks have moved, and what has got faster against a baseline you recorded before the session. Book that follow-up when you book the training, or it rarely happens.

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Co-founder · Deployed AI