AI-Powered Project Management Workshop: Exercises and Evaluation

An AI-powered project management workshop teaches participants to use AI tools for real project tasks — not by explaining features, but by running through exercises where the AI actually helps. A good workshop produces practiced skills, a set of tested prompts and a clear sense of where AI adds value and where it does not.

The output should survive the workshop. Tools like fenbs keep approved prompts, exercise decisions and lessons in one record so participants can use what the workshop produced in their real work afterward.

What the workshop should accomplish

A clear goal for the day: by the end, each participant can describe three specific tasks where AI improves their project work, name the review step that keeps each output accurate, and demonstrate one complete workflow — from giving the AI the right context to reviewing and using the output.

Skip a workshop that spends most of its time on tool introductions or feature tours. Participants learn AI project management by using AI on project tasks, not by watching a demo.

Workshop structure and timing

A half-day workshop (three to four hours) works for a focused skills introduction. A full-day workshop allows for more exercises and team-level work. The structure below is for a half-day session; extend each section for a full day.

• Opening (20 min): What AI does and does not do in project management. Two concrete examples, one that worked well and one that produced an error the PM caught.
• Exercise 1 — Context and output (45 min): Participants use AI to produce a project artifact (status report, risk register draft, agenda) from provided project notes. Debrief on what context was needed and what the review step found.
• Exercise 2 — Role-play a decision (45 min): A scenario where AI proposes an approach and participants decide whether to accept, modify or reject it. Focus on what information was missing and what judgment the PM applied.
• Exercise 3 — Build a shared prompt (30 min): In small groups, participants develop a reusable prompt for a task they do regularly. Test it, refine it and record it.
• Evaluation and wrap-up (30 min): Participants apply the evaluation criteria below to their own exercises. Each writes one lesson from the day.

Practical exercises

Exercise: status report from task notes

Participants receive a set of project task notes (or use their own). They give the AI the notes and ask it to produce a stakeholder status report. Then they review the report against the notes: is every status claim accurate? Is anything missing? Is the tone appropriate for the intended reader? They edit the report and note what the AI got wrong and why.

Exercise: risk identification

Participants describe a project scenario to the AI and ask it to identify five risks. They evaluate each risk: is it real for this project? Is it specific enough to act on? Does the mitigation make sense? The goal is to practice using AI output as a starting point for judgment, not as a final answer.

Exercise: rewrite for a non-technical audience

Participants take a technical document and ask the AI to rewrite it for a non-technical client. They check every technical claim in the rewrite against the original. This exercise builds the habit of verifying accuracy even when the output reads well.

Evaluation criteria

Use these criteria to evaluate each exercise output. They also work as a framework for participants to self-evaluate their regular AI-assisted work after the workshop.

• Accuracy: Is every factual claim in the AI output correct? What did the review find?
• Completeness: Did the AI miss anything important? What context would have included it?
• Appropriateness: Is the output right for the intended audience and purpose?
• Efficiency: Did using AI save time compared to doing the task from scratch?
• Review quality: Did the participant catch the errors? What review step would catch them reliably?

What to do after the workshop

A workshop without follow-up produces participants who understood the exercises and then returned to their old workflows. Plan for a two-week check-in where participants report one real task they used AI for, what review step they used and what the outcome was.

The prompts developed in the workshop should live somewhere participants can access them in their real work. A private document works; a shared tool that connected AI apps can read works better. The goal is to make the tested prompts available in the moment they are needed, not require participants to remember them.

For the practical skills that underpin the exercises, see how to use AI agents in project management and prompts for project management AI. The related guide to AI project management interview questions helps participants prepare to discuss their skills.

How to do this with fenbs

1. Create a free account and set up a project for the workshop.
2. Add each exercise as a task. After the workshop, record the decisions each exercise produced — what prompts worked, what review steps caught errors.
3. Save tested prompts in Skills and prompts, with a note on what each prompt is for and what review step pairs with it.
4. Record lessons learned from the workshop: what the exercises revealed about AI use in your team's work, what to do differently next time.
5. Connect a supported AI app from Integrations so participants can use the prompt library in their real project work.