This is not about building models or becoming prompt engineers. It is about reclaiming time and attention so that human judgment, creativity, and relationship-based work can take center stage.
This workshop helps teams systematically identify, test, and share practical uses of AI that reduce low-value work, free up human judgment, and improve day-to-day efficiency. The goal is not automation for its own sake. The goal is creating space for the work that only humans can do well.
Overall Structure
| Time | Activity |
|---|---|
| 60–90 min | Part 1: Kick-Off — Framing & Fieldwork Assignment |
| 1–3 weeks | Between: Observation Period embedded in real work |
| 4 hours | Part 2: AI Efficiency Hack-a-Thon |
| Ongoing | Apply → Report → Refine cycle |
Part 1: Kick-Off — Framing & Fieldwork Assignment
Duration: 60–90 minutes Objective: Create shared clarity on what to look for, why it matters, and how to spot AI-ready work.
1. Set the Frame (15 min)
Open by grounding the group in what this work is not. It is not about automating everything, mastering technical tools, or keeping pace with hype. It is about reducing the volume of repetitive, low-judgment work so people can spend more of their time on what actually requires their expertise.
Define "busy work" for the group: work that is repetitive, time-consuming, low-judgment, and primarily about moving, summarizing, formatting, or translating information. A useful test: if a capable intern could do it with a clear set of instructions, AI might help.
2. Introduce the AI Opportunity Lens (20 min)
Give participants a simple mental filter they can apply during their actual workdays.
High-potential use cases tend to share these characteristics: repetitive tasks performed many times; text-heavy work like emails, notes, reports, and summaries; pattern-based work involving classification, comparison, or extraction; translational work that converts one form of information into another; and interruptive tasks that break focus or trigger context switching.
Lower-potential territory (for now) includes high-stakes judgment calls, ethical or people-sensitive decisions, and novel or ambiguous problem framing.
3. Provide Concrete Examples (10–15 min)
Anchor the conversation with relatable examples. The pattern to emphasize is first drafts and first passes as the primary sweet spot. Examples include turning meeting notes into action items, follow-up emails, or status updates; drafting routine emails, SOPs, and FAQs; summarizing long documents or email threads; extracting key themes from survey responses; and preparing talking points, slide outlines, or stakeholder summaries.
4. Assign the Fieldwork (15 min)
Over the next one to three weeks, ask participants to keep a running list of tasks that feel annoying, repetitive, or slow. The charge is simple: notice the moments when you find yourself thinking, "There has to be a better way."
Each entry should capture: a description of the task, how often it occurs, rough time spent, the type of output it produces, and a brief note on why it is frustrating. The capture method does not matter. The goal is habit, not precision.
Between Sessions: Observation Period
Duration: 1–3 weeks embedded in real work.
This is not a homework assignment. It is a structured invitation to pay attention to work that already happens. Encourage light, optional experimentation. Curiosity is the right orientation here, not performance. Capturing failures and dead ends is just as valuable as capturing wins. There is no pressure to solve anything yet.
Part 2: AI Efficiency Hack-a-Thon
Duration: Approximately 4 hours Objective: Convert real pain points into practical AI workflows people can actually use.
| Time | Activity |
|---|---|
| 60 min | Use Case Sharing & Selection |
| 120 min | Group AI Exploration |
| 30 min | Synthesis & Presentation Prep |
| 30 min | Report-Outs |
| 15 min | Individual Action Commitments |
1. Share & Select Use Cases (60 min)
Ask each participant to share their top one or two use cases from the observation period. Capture all ideas visibly — speed matters here, this is not a time for debate. Then select approximately three use cases to explore in depth using a simple prioritization lens around: time savings potential, frequency of occurrence, low risk if AI output requires revision, broad relevance across roles or teams, and ease of experimentation.
2. Group AI Exploration (120 min)
Break into small groups, one per selected use case. Each group uses AI as a collaborator to explore how it could help with their specific task. The questions to work through are practical: what inputs does the AI need, what outputs would be useful, and where is human review essential.
Suggested prompt starters to get groups moving:
- "Here is a task we do regularly. Help us think of ways AI could reduce time or steps."
- "What parts of this task are best suited for automation versus human judgment?"
- "Design a simple workflow using AI as a first pass."
Encourage iteration, follow-up questions, and testing multiple approaches. The deliverable does not need to be perfect. It needs to be one clear, concrete application that someone could actually try next week.
3. Synthesis & Presentation Prep (30 min)
Each group selects one use case to share with the full room. The presentation should answer four questions: What problem does this solve? Where does AI fit in the workflow? What is different compared to how the task is done today? What is the estimated time or effort saved, and what are the key guardrails?
4. Report-Outs (30 min)
Approximately eight minutes per group. Emphasize practicality over polish. The most valuable things to surface are what surprised the group, what they learned from iteration, and what they would do differently. Focus audience questions on replicability, not edge cases.
5. Individual Takeaways & Commitments (15 min)
This step is where learning converts to action. Each participant documents three things: one AI technique or workflow they will try, one specific task they will apply it to, and when they will test it. A brief note on what success looks like makes the commitment concrete.
Closing the Loop: Making It a Cycle
End the session with an explicit invitation to continue. At the next meeting, open with brief report-outs: what did you try, what worked, what did not. This practice creates psychological safety, sustains momentum, and builds a growing internal library of real examples that belong to your team.
The Apply → Report → Refine cycle is the mechanism.
A single workshop produces insight. Repeated cycles produce capability.