After an AI workshop, give participants a small authorized task, a named reviewer and a clear standard for acceptable work. Review what happens before expanding use. The sequence below is an adaptable planning example, not a promised AISFY delivery schedule or evidence that adoption has already occurred.
Close the session with a usable handover
Record the task participants practised, the output produced, the mistakes discovered and the questions still open. A slide deck alone rarely tells a manager what a participant is ready to do. Include the tool and environment used, because a practice task may not transfer to a different account or data setting.
Assign an owner to every next action. “The team will use AI more” is not actionable. “The reporting lead will review one AI-assisted draft against the approved source” is a bounded step that can generate useful evidence.
Choose the first workplace task carefully
Select a task similar enough to the workshop exercise for the learning to transfer, but different enough to require judgment. Use approved material and keep consequential actions under existing authorization. Do not turn a supervised drafting exercise into autonomous sending or production changes.
If access or policy remains unresolved, make that the next action instead. A participant should not be judged as unwilling to adopt when the organization has not supplied the conditions for responsible use.
Use a staged review plan
| Stage | Action | Decision supported |
|---|---|---|
| Immediately after the workshop | Capture outputs, dependencies and owners | Is there a suitable next task? |
| First practice cycle | Complete a bounded task with review | Can the participant apply the method? |
| Team review | Compare quality, errors and total effort | Should the method change or continue? |
| Operational review | Examine repeated use and relevant process evidence | Is broader evaluation justified? |
Make manager support concrete
A manager can provide time for practice, identify a suitable reviewer and remove an access barrier. These actions are more useful than a general instruction to innovate. The manager should also explain where AI use is optional and where existing procedures remain mandatory.
Encourage participants to report unsuccessful attempts. If the only acceptable story is a success story, people may hide rework or use an unapproved shortcut. A failed task can reveal a tool limitation, a training gap or a process that should remain unchanged.
Keep a proportionate evidence record
For each practice task, record the purpose, approved environment, reviewer, corrections and decision. Include total effort if time is relevant. Do not collect every interaction by default; choose records proportionate to the evaluation and follow organizational privacy and retention rules.
Where AISFY supports participation and readiness records, use them as learning and program signals. They do not replace a business process baseline. A completed activity is not proof that the participant uses the method consistently or that an operational result improved.
Decide whether to continue, adapt or stop
Continue when the task is authorized, output quality is acceptable and the team can review it reliably. Adapt when the method is promising but the task brief, source material or feedback process needs work. Stop when the data cannot be used appropriately, the output cannot be checked or consequences exceed the agreed boundary.
Write the reason for the decision. If the team expands the scope, reassess its assumptions and responsibilities. Evidence from summarizing internal notes does not automatically justify customer communications, financial recommendations or decisions affecting people.
Connect learning to the next program decision
The next step may be more practice, a specialist workshop, a policy clarification or no further AI work on that task. Use the original business problem to judge which is appropriate. A continuing learning path should respond to observed gaps rather than simply introduce another tool.
For an organization-wide initiative, keep local cohort evidence separate from broader claims. Leaders need to know who participated, what was demonstrated and which teams or processes were not evaluated before making a scaling decision.
Frequently asked questions
How soon should we review progress?
Set a review point around the task’s actual work cycle and the participant’s opportunity to practise. A fixed calendar interval is not useful if the relevant task has not occurred.
What if participants do not use the tool afterwards?
Investigate relevance, access, confidence, policy and managerial support before attributing the result to motivation. The original task may also be a poor fit for AI.