Planning a useful AI workshop

A useful AI workshop leaves people able to perform a specific task and judge the result. If you are arranging training for a team, start by writing that task down. “Understand AI” is too broad to choose exercises, prepare materials, or decide whether the session worked.

The planning problem is often practical: some participants have never opened the approved tool, others use it daily, and nobody knows which information can go into it. Resolve those differences before asking a facilitator to prepare a demonstration.

Define the audience by shared work

Job titles alone are a weak way to group a workshop. A salesperson and a customer-success manager may both need to turn meeting notes into accurate next steps. Two people in marketing may need entirely different training if one edits copy and the other manages reporting.

Choose one shared task for the main exercise. For a mixed group, prepare task-specific breakouts after a common introduction. Separate introductory tool access from advanced workflow design when the difference in experience would prevent either group from practicing.

Write a learning outcome that can be observed: “Participants can create a handoff summary from approved notes, mark missing information, and correct unsupported statements.” This gives the facilitator a standard for feedback.

Prepare a task pack

Give the facilitator four things:

• An input participants are permitted to use.
• An example of an acceptable finished output.
• A list of errors that would require correction or escalation.
• The place the output goes next and the person who reviews it.

For an illustrative support team, the pack might include a fictional customer message, an approved troubleshooting article, and a response template. Include a request the documentation cannot answer. The team should practice recognizing the limit rather than confidently filling the gap.

Synthetic or carefully approved examples can protect private information during group learning. They must still preserve the structure and difficulty of the real task. A sanitized exercise with every awkward detail removed teaches less than a realistic one.

Check access before the room fills

Confirm the product, account type, enabled features, and sharing permissions with your administrator. A facilitator's account may behave differently from a participant's. Test the exercise from an ordinary team account and provide a usable alternative if a required feature is unavailable.

Make a clear data-use decision before the session. Name the approved information, restricted categories, and person participants should ask when uncertain. Do not let the workshop become an informal workaround for a missing company policy.

Include accessible materials, readable examples, and time for people to work at different speeds. A person following with keyboard navigation should be able to complete the same task as someone using a mouse.

Use practice to test the instruction

A workshop should allow revision. If everyone copies the facilitator's prompt once and moves on, you cannot tell whether they understand the work or have reproduced one favorable example.

The illustrative workshop agenda turns this structure into a concrete session plan. Its timing is an example for planning, not the duration or scope of a Jiberish Labs package.

Decide what happens afterward

Before booking, agree who will maintain the instructions and where questions go. Follow-up might mean internal practice, an optional review session, or a separate implementation project. Put that choice in the scope.

Collect one completed exercise and a short explanation of what the participant corrected. Later, compare real work using the same criteria. Attendance and tool logins are useful administrative facts; they do not establish that the team can produce better work.

Microsoft's employee AI enablement guidance also distinguishes adoption signals from evidence of business value. Treat it as vendor guidance, not proof that a workshop will produce a particular return. Microsoft Learn.

Choose a package without assuming the fit

Jiberish Labs offers AI Training for Teams, including tailored sessions using actual team tasks. The existing sales Sprint remains a specific package with its own published commitments. It should not be treated as the default scope for every department.

The sales training work example describes the sales-focused offering; it is not evidence of measured outcomes across an entire workforce. Ask for a scope that names your audience, exercises, materials, and follow-up.

For a longer adoption effort, continue with AI training for teams. Bring your draft task pack to a working session if you want help shaping the workshop.