AI Labs

AI Labs are recurring workshops where your organization’s AI journey truly takes flight. These sessions create a safe, collaborative environment where AI Ambassadors and creators come together to experiment, learn, and build valuable AI assistants.

Overview

An AI Lab is a regular physical or digital meeting space designed to foster innovation and hands-on learning. During these sessions, participants explore advanced concepts from the SAMR model—specifically Modification and Redefinition—by demonstrating what they’ve built, solving challenges together, and redesigning workflows with AI.

Think of it as your organization’s innovation sandbox: a high-trust environment where experimentation is encouraged, failure is a learning opportunity, and collective knowledge drives individual growth.

What Happens in an AI Lab?

AI Lab sessions typically include:

• Demo Time: Participants showcase assistants they’ve built, explaining their use cases and demonstrating functionality
• Collaborative Problem-Solving: Team members work together to overcome technical challenges or refine assistant designs
• Knowledge Sharing: Learning from each other’s successes and setbacks
• Hands-On Building: Creating and testing new assistants as a group
• Workflow Redesign: Exploring how AI can transform tasks, not just automate them

Key Benefits

Promotes Trying & Learning

The AI Lab acts as a high-trust “sandbox” where you can experiment without fear of making mistakes. This hands-on environment is crucial for moving past technical anxiety and building genuine competence with AI tools.

Facilitated Growth

Rather than leaving learning to chance, AI Labs provide structured opportunities for growth. The group’s collective knowledge lifts individual capabilities, making the journey up the AI Staircase feel achievable and supported.

Collaborative Prototyping

Instead of working in isolation, creators build together. This transparency makes “ease of use” visible to everyone and accelerates learning across the organization.

Cross-Functional Knowledge Sharing

When HR demonstrates a policy-searching assistant, Finance can apply those same principles to build a procurement assistant. This cross-pollination reinforces the value of AI across different domains and use cases.

Getting Started

Frequency

We recommend bi-weekly or monthly sessions to maintain momentum without overwhelming participants. Consistency is more important than duration—regular touchpoints keep the innovation flowing.

Structure

While each organization’s AI Lab will evolve to fit their needs, a typical session might include:

1. Check-in (5–10 min): Quick round of updates and wins
2. Demonstrations (20–30 min): Showcasing new or updated assistants
3. Collaborative Work (30–45 min): Building, troubleshooting, or planning together
4. Knowledge Sharing (10–15 min): Lessons learned and best practices
5. Next Steps (5 min): Planning for the next session

Tips for Success

• Create Psychological Safety: Emphasize that all questions are valid and mistakes are learning opportunities
• Share Both Wins and Failures: Learning what doesn’t work is just as valuable as celebrating successes
• Keep It Practical: Focus on solving real problems, not theoretical exercises
• Document and Share: Capture insights so knowledge isn’t lost between sessions
• Celebrate Progress: Acknowledge achievements, no matter how small

Next Steps

Ready to establish your own AI Lab? Here are your next actions:

1. Identify potential facilitators (usually from your admin team)
2. Schedule your first session and invite AI Ambassadors
3. Choose a format that works for your organization
4. Start with simple demonstrations and build from there
5. Establish a regular cadence that fits your team’s schedule