Torras.ai

Over the past few years, I’ve helped several companies learn and deploy AI within their organizations. Through multiple AI Bootcamps, I’ve seen what works, what doesn’t, and what truly accelerates transformation.

Here are some lessons learned from the field.

1. Identify a Dedicated Team of “AI Citizens”

The best results come when the company appoints a specific group to participate in the Bootcamp — we call them AI Citizens. They are not necessarily data scientists or IT experts, but motivated employees willing to learn and apply AI to their daily work. Having a stable, dedicated team ensures continuity and ownership of the results.

2. Start with Internal Use Cases

At the beginning, we identify 4-6 internal use cases — processes that are tedious, repetitive, or clearly waste employees’ time. From this initial list, we select one use case to focus on during the Bootcamp. The ideal use case is short, specific, and provides visible value when solved.

3. Clarify the Tools Early

Before starting, make sure everyone knows which tools will be used — whether it’s Microsoft Copilot, Copilot Studio, Power Automate, or even more technical environments like Visual Studio Code and Python. Don’t underestimate motivated business users — with the right guidance, the latest AI models, many can become surprisingly capable coders.

Also, ensure all participants have the necessary licenses and access before the Bootcamp starts. Nothing slows progress more than missing permissions.

4. Structure the Bootcamp for Learning and Results

The structure I’ve found most effective:

• 6 sessions, each lasting 4 hours
• At least one business day between sessions
• No more than two sessions per week

This pacing allows participants to apply what they learn — their “homework” involves refining the use case, testing tools, and preparing documentation.

Session 1 is about defining the use case — expanding or narrowing it as needed. By Session 6, the team should have a working AI solution, along with a final presentation deck, an ROI calculation, and a clear sense of achievement.

5. Keep a Dual Objective: Learn and Deliver

Each Bootcamp has two goals:

1. Build a useful AI-powered solution that improves a real process.
2. Learn enough during the process to replicate success in future projects.

This dual focus creates both immediate impact and long-term capability within the company.

6. Involve IT — but Stay Autonomous

Keep the IT or IS department informed and aligned with the Bootcamp’s strategic purpose. You want to be as autonomous and self-sufficient as possible while still maintaining IT as a partner — not a gatekeeper. The relationship works best when IT feels included and supportive, not bypassed.

7. Present Results to Leadership

Once the Bootcamp is complete, prepare a presentation for the C-level team. The outcome should be a working tool that addresses the chosen use case. Include a realistic ROI, typically expressed as hours saved per year — tangible, measurable, and credible.

8. The Role of the Facilitator

As the facilitator, I take a hands-off approach. My goal is to guide, not to build. The AI Citizens must be able to create their own solutions — that’s how the learning sticks. Think of it as a hackathon with purpose: limited time, real impact, and personal accountability.

9. Scale from There

Once the first Bootcamp succeeds, more use cases can be developed, and the team of AI Citizens can expand. Over time, these employees evolve into internal facilitators, spreading AI skills and mindset across the organization.

An AI Bootcamp is not an experiment — it’s the first step toward transforming the company from within, unlocking efficiency and maximizing human potential.

Final Thoughts

AI adoption doesn’t happen through technology alone. It happens when motivated people learn to use new tools to solve real problems. That’s what the AI Bootcamp is all about — empowering teams to build, learn, and lead the AI transformation from inside the organization.