One day with your own documents and spreadsheets. At the end, the team picks a use case worth developing.One day on your own documents. Then the team picks a use case to develop.
What matters is the link between the tool, the real task and the person responsible for the result.
Every participant leaves with a procedure for their own task: how to assign the work, check the result and save a template they will reuse.Everyone leaves with a procedure for their own task and a template they will reuse.
The team knows which data may go into which tool, what belongs only in a separate environment and where a human always has the last word.The team knows what data a tool may get and where a human has the last word.
At the end we separate what can be used right away from what needs integration, better data or further verification.We split what works now from what needs integration, data or verification.
This is the starting framework. We adapt the day to participant roles and the goal.
What AI can do and where it fails, with no slides. The team aligns on terms and on how to check output.
Summaries, version comparisons, working with sources. Participants get used to verifying whether a conclusion follows from the materials, or the AI invented it.Summaries, version comparisons, sources. Participants check whether a conclusion follows from the materials.
Analysis, calculation design, logic checks. AI helps with the numbers, a human stays responsible for them.
Each group solves a task from its own agenda. Marketing does not get a finance task, finance does not get an HR task.
A one-off chat prompt becomes a procedure: a named input, a brief template, a checkpoint and an output that works a second and third time.A one-off prompt becomes a repeatable procedure with a brief template and a checkpoint.
The team names several options and picks one priority. Management gets a basis for deciding what to develop and what should wait.The team picks one priority. Management gets a basis for deciding what to develop and what waits.
Before the training we map roles, tools in use, recurring tasks and constraints. We do not need live company data for that.
We build tasks on public, anonymised or synthetic data. Sensitive materials only under a separate agreement on environment and access.We use public, anonymised or synthetic data. Sensitive materials only by separate agreement.
Participants work on their own. The instructor only corrects briefs, shows checking techniques and explains why an output passed or failed.Participants work alone. The instructor corrects briefs and explains why an output passed or failed.
After the training we check which procedures the team actually uses and where it got stuck. Adoption is a separate follow-up step.Afterwards we check which procedures the team uses. Adoption is a separate follow-up step.
An opportunity map, tool selection criteria, a process owner and conditions for limited verification before a decision is made.An opportunity map, selection criteria, a process owner and test conditions before deciding.
Drafting texts, structuring materials, checking consistency. A human always makes the final decision about people.Drafting texts, consistency checks. A human always makes the final decision about people.
Working with materials, message variants, reformatting. References and numbers are verified against the source.Message variants and reformatting. References and numbers are checked against the source.
Data extraction, calculation design, a checklist for a recurring output. A number without a source never appears in the report.Data extraction, calculations, a checklist. A number without a source never appears in the report.
We tie rules to the work people do and the tools they actually use.
Yes. The AI training programme builds on everyday work with documents, spreadsheets and research. No programming is needed. Technical teams can get their own advanced scenario.
By default, no. In AI training we work with public, anonymised or synthetic materials. We handle sensitive data only after a separate assessment of environment and access.
Not fully, but AI training is a documentable step toward it. Article 4 of the AI Act, as amended by Regulation (EU) 2026/1744, asks for measures supporting AI literacy by role and actual AI use. It is an obligation of effort, not of result, a company does not have to guarantee a specific level for any individual, and the regulation prescribes no single course or stamp. CIAD supplies the syllabus, attendance records and role-based rules.
No public price list sets the price of AI training. It depends on the number of roles, scenarios and the format of the day. After a short introductory consultation we send the scope and a quote.
The company can stay with the procedures and rules it received in AI training, or follow up with a pilot of one use case. The next step has its own scope and a measurable goal.
The first consultation sets the goal, preparation and outputs. Pricing follows the scope.
