Your team doesn't need to become data scientists. They don't need to understand neural networks, gradient descent, or transformer architectures. What they do need is a practical understanding of what AI can do, what it can't do, and how to work alongside it effectively.
This is the framework we use in our AI Training & Workshops. We've run it with over 50 teams — from accounting firms to manufacturing plants — and the results are consistent: by Friday, teams that started Monday saying "AI is scary" are building their first automations.
Day 1: Demystify (Monday)
Goal: Replace fear with understanding
Most AI anxiety comes from not understanding what AI actually is. Day 1 is about stripping away the hype and showing your team what's really happening under the hood — in plain English.
- Morning: "AI is just pattern matching" — a non-technical explanation of how AI works, using examples from your actual business
- Afternoon: Hands-on demo where everyone uses ChatGPT, Claude, or similar tools to solve a real work task. The goal is to make AI feel like a tool, not a threat.
Key outcome: Everyone can explain what AI does in one sentence without using jargon.
Day 2: Identify (Tuesday)
Goal: Find AI opportunities in your own workflows
Now that the team understands what AI can do, they're ready to look at their own work through an AI lens.
- Morning: Process mapping workshop — teams map out their daily/weekly workflows on whiteboards
- Afternoon: "AI or Not?" exercise — for each process step, teams decide: Could AI help here? Should it? What would the benefit be?
Key outcome: A prioritized list of 5–10 potential AI use cases from the team's own work.
Day 3: Evaluate (Wednesday)
Goal: Learn to assess AI solutions critically
Not every AI opportunity is worth pursuing. Day 3 teaches the team how to evaluate potential projects like a pragmatic business person, not a tech enthusiast.
- Morning: The ROI framework — how to estimate costs, benefits, and timeline for an AI project
- Afternoon: Vendor evaluation workshop — how to ask the right questions when an AI vendor pitches you, and red flags to watch for
Key outcome: Teams can calculate a rough ROI for any proposed AI project and identify vendor red flags.
Day 4: Build (Thursday)
Goal: Create a working automation
This is where it gets real. Using no-code and low-code tools, teams build an actual working automation for one of the use cases they identified on Tuesday.
- Morning: Tool introduction — we cover practical tools like Make.com, Zapier, and AI-powered document processors
- Afternoon: Build sprint — teams work in pairs to build a working prototype. We provide hands-on guidance and troubleshooting.
Key outcome: Every team has a working automation they built themselves.
Day 5: Plan (Friday)
Goal: Create a 90-day AI adoption roadmap
The workshop is just the beginning. Day 5 is about creating a sustainable plan for continued AI adoption.
- Morning: Teams present their automations and learnings to leadership
- Afternoon: Roadmap workshop — teams create a 90-day plan with specific milestones, owners, and success metrics
Key outcome: A concrete, actionable 90-day AI adoption plan with team buy-in.
What Makes This Framework Work
Three principles make this approach consistently successful:
- Start with their work, not with AI. We never start by explaining AI technology. We start by understanding their daily frustrations. AI becomes the answer to a question they're already asking.
- Build on Day 4, not Day 1. People need context before they can build. Jumping straight to tools creates confusion. The first three days build the foundation.
- End with a plan, not a certificate. A workshop is worthless if nothing changes on Monday morning. The 90-day roadmap ensures the momentum continues.
Want to run this workshop with your team? Get in touch — we'll customize it for your industry and team size.