AI adoption & training
Teams that use AI well and know its limits, with a policy on what may go into which tool before somebody pastes a contract into a public chatbot.
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Your staff are already using it
Almost certainly, and mostly without telling anyone. Drafting emails, summarising documents, writing code, translating. Surveys consistently find this happening well ahead of any policy, which means the practical question is not whether to allow AI but whether people are using it well and safely. At the moment, most are doing neither. They ask vaguely and get mediocre results, they trust output that should have been checked, and they paste company information into consumer tools with no agreement about data. A short amount of training changes all three.
What we actually teach
How to ask well: context, examples, constraints, and specifying the format you want. The difference between a vague request and a structured one is usually the difference between something unusable and something genuinely good, and it takes twenty minutes to teach. Where these tools fail: confident fabrication, arithmetic, anything requiring current information the model does not have, and tasks where being nearly right is worse than refusing. People who understand the failure modes verify in the right places rather than either trusting everything or nothing. And what belongs in which tool: the sanctioned options, what may go into them, and what must never leave your systems.
Policy that people will follow
A policy banning AI produces shadow usage on personal accounts, which is the worst possible outcome: the same risk, with no visibility. A policy nobody can find or understand does the same. We help write something short, specific and usable: named sanctioned tools, clear categories of data, and a straightforward rule for what needs checking before it leaves the building. One page people actually read, not fifteen pages of legal text that exists so somebody can point at it after an incident.
What you get
How to ask well
Context, examples and constraints. Twenty minutes of teaching that changes the quality of every result.
Where these tools fail
Confident fabrication, arithmetic and stale information. Knowing the failure modes is what makes verification efficient.
Tailored by role
Finance, sales, developers and management get examples from their own work, not generic demonstrations.
A usable policy
One page people read. Banning AI produces shadow usage on personal accounts, which is strictly worse.
Sanctioned tools set up
Proper options with data protections, so nobody needs to paste a contract into a consumer chatbot.
Current, not last year's
This field changes fast. We teach what these tools do now, including what recently stopped being true.
Technologies
Frequently asked questions
Who should attend?
Everyone who writes, reads or analyses as part of their job, which in most companies is nearly everyone. We split by role because a finance team and a development team need genuinely different examples, and generic training is why most AI sessions are forgotten within a fortnight.
Should we just ban AI tools?
It does not work. Staff use them on personal accounts and phones instead, which means the same data exposure with none of the visibility or control. Providing sanctioned tools with proper data protections and clear rules is both safer and more useful.
How long does training take?
A half-day session per role covers the practical ground well. We follow up a few weeks later, because the useful questions only appear once people have tried it on their own work and hit something that did not go as expected.
Get your team using AI well, and safely
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Location
Serbia