AI training for businesses and teams

When AI training can add value

Signs it could be a good fit

Everyone uses AI differently.
There are no shared criteria for when and how to use it.
Use is limited to basic questions or occasional experiments.
Good results are hard to repeat and build into everyday work.
The team has questions about data, confidentiality or review.
Management wants to support adoption while staying in control.

Training alone may not be enough if…

The main problem calls for systems implementation or integration.
Internal decisions about permitted tools and access are still missing.
A single session is expected to deliver automated processes.
There is no intention to apply what the team learns afterward.

What your team can learn to do better

Content follows the skills your team needs to develop, rather than a fixed syllabus of tools.

Use AI with better judgment

Frame a task, provide useful context, write instructions, review the response and refine a way of working that can be repeated.

Apply it to work situations

Work on documents, emails and communications; prepare content; support information analysis and specific steps in a process, with human review.

Decide when it makes sense

Recognize when AI can help, when a rule or conventional process is enough and which decisions should stay with people.

Use it responsibly

Recognize data that should not be shared, validate results, identify limits and work with approved tools where the company has already defined them.

From trying AI to making it part of everyday work

See what AI can do
Understand possibilities and limits through clear examples.
Apply it to real tasks
Practice with tasks that reflect the participants’ work.
Define criteria for use
Agree when to use it, what to review and when to stop.
Build it into work habits
Identify useful practices to try and develop afterward.

Useful training goes beyond writing a better prompt: it helps people decide when to use AI, how to validate the result and how to fit it into their daily work.

Formats to suit your starting point

Different formats are possible. Duration and scope depend on your objectives, the team’s experience and the tasks you want to work on.

Introductory session: Understand possibilities, limits and first uses before deciding what to explore further.
Practical team workshop: Practice with shared work situations and review together what works and what needs adjusting.
Training by role: Focus examples and exercises on the tasks of a particular department or professional role.
Several sessions with practice: Allow time to apply learning between sessions, then revisit questions, results and adjustments.

Tailored to the business and the participants

Before defining the training, we look at your starting point:

What you want the team to be able to do better.
The participants’ current skills and experience.
The tools you use and the access you have.
The most relevant tasks, work situations and processes.
Data, time or organizational constraints to take into account.

Exercises can draw on your own work situations, using adapted or anonymized examples where needed to avoid exposing confidential information.

Practical criteria for responsible use

Privacy and validation are part of the practice. We work through questions that arise when AI becomes part of daily work:

Which tools are approved, where the company has already decided.
What information can be entered and what must be kept private.
Which results need verification and human review.
How to share good practices and avoid conflicting individual approaches.

Training can help surface questions and limits. It does not replace an internal governance policy or guarantee regulatory compliance or the absence of risk.

How we work

Understand the team and its goals

We define what should change after the training.
Adapt content and examples

We prioritize situations that are useful to the participants and relevant to their work.
Practice with real tasks

The session combines judgment, examples and application.
Turn learning into next steps

We identify what to consolidate, try or explore further.

The value of training lies in what the team can apply afterward, not just what they have seen during the session.

See the broader training and adoption service

What this training is and is not

What to expect

Training grounded in real work.
Content adapted to the team’s context and experience.
Practice with examples and review.
Judgment to support useful adoption.

What it is not

A demonstration of twenty tools.
A collection of generic prompts.
A promise that AI can solve everything.
A substitute for an implementation project.

Frequently asked questions

Does the team need previous AI experience?

Not necessarily. The starting point is adapted to the group’s experience. If levels vary widely, we can consider separate sessions or adjust the exercises.

Can the training be adapted to our processes?

Yes. We can use tasks and situations from your business, agreeing on examples that do not expose confidential information. Training does not include implementing those processes.

Can it focus on specific departments or roles?

Yes. Examples can focus on the needs of a particular team or role. Scope depends on the tasks you want to work on.

Which tools will we use?

We agree on these based on your objectives, available access and company restrictions. The focus is on using them with sound judgment, not covering every tool on the market.

Does it cover privacy, data and responsible use?

Yes, as part of the practice: what to share, what to keep private and how to review results. This is not an audit, a certification or a guarantee of regulatory compliance.

Is it useful if we already use Copilot, ChatGPT or other tools?

Yes, if you want to improve how you use them: providing context, reviewing results or sharing criteria. We start with current uses to identify what to work on.

Can we start with one session and then decide whether to continue?

Yes. We can define an initial session and then assess whether it makes sense to go further. There is no need to commit to a long program at the outset.