Anyone using AI is expected to show leadership, because you need to lead AI’s work. In gaming, there’s a role called IGL — the in-game leader. It’s a concept worth borrowing for AI adoption, too. Try this method with your team.
In a gaming team, anyone can step into the IGL role, agreed upon or not.
Either way, it means shifting into a more active and responsible position: you’re reading more of the big picture and leading your teammates. Being in this role even 30 minutes, teaches one a lot. So, give your team an opportunity to take ownership over AI.
Next, I’ll guide how to facilitate this method with your team.
The leaders in action
Let’s start. Pick the IGL method when you have specific themes you want to work on together in an agile, swift way.
Choose as many themes as you have members in your team, so each one will have a chance to act as the in-game leader at least once.
Step 1: Set the goals for the IGLs
Timing: A week or a couple of days before the exercise
- Agree together the themes to work through (e.g., 5 of them)
- Set together just one goal for each theme
- Share the themes and goals for each team member
For example, if one of the themes was “learning about AI”, the goal could be designing a weekly method for learning together. Now, the IGL has a direction to lead towards.
Step 2: Unleash the IGLs
Timing: 2 – 4 hours for the exercise
- Each theme is one “game” running about 30–40 minutes (agreed in advance)
- The IGLs will take turns leading the games, aka their themes
- The game philosophy is to spot and focus on wins – even small ones
When it comes to the game concept (a specific facilitation technique, for instance), let your team be creative. Each IGL can design their own approach, based on how they want to lead the team towards the goal. The mindset is that anything they come up with will work.
Concentrating on wins is vital. By wins, I mean any progress. People are more prone to notice what went wrong or what’s not working, while the best performances are built on strengths and succeeding together. If anything goes right, notice it and repeat it.
Outcome: Steps forward with AI adoption, while your team has learned to take ownership, be solution-orientated, and celebrate wins together.
This kind of method won’t fit every team, but it’s adaptable for many. For example, for teams who’re dealing with AI fatigue and need a fun sprint forward.
Either way, the IGL exercise helps people take ownership of the AI topic. Instead of AI happening TO them, they are the ones who steer the change.
/fiaskonina (IGL)
