AI training people can use on Tuesday.

Begin with the team's tasks

A useful workshop starts with what people need to do: summarise a document, prepare a draft, compare information, organise an intake or find an approved answer. Participants need a way to judge whether the output helps. Producing more text is not a learning outcome by itself.

We discuss the audience, existing skills, tools your organisation permits and examples that can be used safely. Training for HR and training for a product team may share foundations while needing different exercises and review decisions.

A session that makes room for real questions

The learning can cover clear task instructions, useful context, checking results, recognising uncertainty and deciding when a human should take over. Exercises should use approved or anonymised material and fit the team's own information-handling rules.

My proposed structure is to demonstrate one task, let participants try a comparable example, review the differences together and agree where the approach belongs in everyday work. The actual programme, duration and group format depend on the audience and scope.

There should be room for the question someone worries is too obvious. If the participants need a second workshop just to translate the first workshop, we have created a new department.

Adoption needs a life after the session

Before the training ends, identify one task participants can try with approved tools, one person they can ask for help and a point at which to review what changed. The follow-up can look at the usefulness of the result, verification effort and recurring problems.

A company can have high attendance and limited adoption when access, process ownership or expectations remain unclear. Those are organisational questions to address with the sponsor. Training can help people use a capability; it cannot grant missing permissions or settle a disagreement about its purpose.

Teaching backed by building

As an AI Corporate Trainer at Coders Lab CZ & SK, I designed and delivered hands-on training that helped teams apply AI tools and workflow automation in their daily operations. My product and delivery experience at Lexicon Labs and RobosizeME connects those explanations with the practical decisions around implementation.

I hold a bachelor's degree in Applied Informatics and have founder, enterprise and startup experience. The full profile gives the context.

Tell me who is in the room

For a first conversation, share the team's roles, current tools, experience level, the work you want to make easier and the sponsor's expectations. We can discuss training in English, Czech or Russian and agree remote or in-person needs directly.

Scheduling, programme, group size and commercial terms depend on fit and scope. Start with the people and the work. The slide deck can wait.

Questions before we begin

Can you tailor AI training to our team?

Yes. The useful starting point is the participants' roles, everyday tasks, current skills and approved tools. We can shape exercises and review criteria around those needs rather than assume the same programme fits every team.

Is the training suitable for non-technical employees?

The programme can focus on practical use and human judgement without requiring coding. We first discuss the audience and what they need to do. Technical implementation training is a different scope and should be agreed separately.

How should we judge whether AI training worked?

Look beyond attendance. Choose a task participants can perform with approved tools, define what acceptable output looks like and review the effort needed to check it. Agree an owner and follow-up point for questions after the session.