AI training for companies: how to choose a practical programme
From assessing needs to exercises and follow-up, discover what to look for in an AI training programme.
AI training for companies should help participants complete a work task and understand how to check the result. Familiarity with tools is useful, but the value of a programme becomes clear when the team can repeat the process after the training ends.
Before comparing agendas, define what you want to change. Should your team write clearer prompts, summarise documents, use an approved tool or identify automation opportunities? These goals require different exercises and levels of support.
Start with the needs of each role
A short questionnaire can collect three things: which tools people use, which tasks take time and where they do not trust the output. Ask each participant for a concrete example without confidential information. “I write reports” becomes more useful when the type of report, its sources and its reader are specified.
For a group with different experience levels, a programme can combine a shared foundation with exercises tailored to each role. More advanced participants can work through a process with several steps, while beginners focus on a defined task and a prompt template.
Ask for an output from every module
A prompting module should finish with a reusable template. A document module should produce a verified summary. An automation lab should deliver a process map showing the input, steps and approval point.
These outputs make the programme easier to evaluate. Instead of asking only whether the session was interesting, you can assess whether the material created is accurate, complete and usable.
One possible structure for a training day
An illustrative programme might divide the morning between understanding AI's limitations and building prompts. Participants then work with a document, compare responses and check for errors. The final session focuses on a task relevant to their role and documenting the process.
For example, the communications team drafts an announcement from source material, while operations prepares a weekly summary. Both groups use the same review criteria: accuracy, completeness and clarity. This is a sample structure, not a promise that every objective can be achieved in one day.
Teach participants how to check results
Participants should also try a case where AI produces an incorrect answer or lacks information. NIST's Generative AI Profile includes the risk of plausible but false content. Verification is therefore part of the user's skill, rather than an optional final step.
An exercise could require every figure to be linked to its source document and every assumption to be identified. Participants learn to seek clarification and avoid confusing fluent writing with factual accuracy.
Plan what happens after the training
Assign a task for the following week and identify someone who can answer the team's questions. Keep templates in a shared location and record changes made after using them. After a few weeks, check which templates were used, which were abandoned and why.
Frequently asked questions
Is one day of training enough?
One day can establish a foundation and produce some practical outputs. Applying AI to more complex processes usually requires further testing, support and review.
Do we need paid subscriptions?
That depends on the exercises and selected features. Check access before the training so participants can actually complete the tasks.
Build the programme around your team
Lumina develops AI training tailored to participants' roles and needs. Tell us about your team and your objectives to discuss a practical programme.