What will your team work on?
A proposed sequence, adapted to your tools, experience and available time.
Choose a useful task
Map the current workflow. Select a bounded task with available examples, a clear reviewer and a result you can judge.
Build a first attempt
Give the assistant context, source material and output requirements. Compare a vague request with a precise brief.
Challenge the answer
Check facts, omissions, tone and confidential information. Record where the AI needs human judgement.
Make it repeatable
Save the tested instructions, the source requirements and a short review checklist. Choose an owner for the next trial.
What does practice look like?
A documented use case and reusable instructions
Turn an approved meeting transcript into a decision log. Separate decisions, open questions and actions. Require a source passage for every decision. A colleague checks the log before it is shared.
A teaching example. It does not describe a completed client engagement.
| Work product | What makes it usable |
|---|---|
| Source material | Approved inputs, with the useful context retained. |
| Reusable instructions | A clear task, constraints and expected output format. |
| Review checklist | The checks a colleague must perform before using the result. |
| Next experiment | An owner, an example and criteria for deciding whether to continue. |
What should you bring?
Bring a concrete question, a representative example and the rules your team already follows. The purpose of preparation is to spend the session practising, with the right accounts, permissions and source material in place.
- A task that someone on the team does regularly.
- An approved AI tool, or a decision about which environment to use.
- A sample that can be shared safely, preferably stripped of sensitive data.
- A reviewer who knows what a good result should look like.
The final proposal sets the duration, group size, trainer, scope and fee. Tool subscriptions and any follow-up are specified separately.
