What super teams do differently, and what it means in the AI era
As always, October’s Constellation's Connected Enterprise 2026 was packed with fantastic sessions from so many smart minds. One of my favorite talks was a conversation with psychologist and author Ron Friedman on what the best teams do differently.
For his book Superteams: The Science and Secrets of High-Performing Teams, Friedman asked thousands of workers across industries two questions:
How effective is your team at achieving its goals?
How would you rate your team’s performance compared with others in your industry?
Only about 8% of teams earned perfect scores. Those super teams shared three strengths:
They protect their time, energy, and attention.
They make one another better.
They keep improving, even when things are going well.
The most encouraging finding? Every one of those strengths is learnable. Here are the takeaways that resonated most with me, especially for leaders guiding their teams through AI adoption.
Five takeaways for leaders
1. Protect focus before adding anything else
The average worker spends 18 hours a week in meetings and another 11 on messages, which is about three-quarters of the week gone before any focused work begins. Super teams treat meetings as a last resort: they are 50% better at avoiding unnecessary meetings and 54% less likely to schedule recurring ones. Of more than 25 workplace amenities studied, only one predicted a high-performing team: a quiet place to do focused work.
2. AI can make individuals faster and teams slower
AI hasn't shortened the workday. Friedman's research found the average team has added three hours to its week. Teammates have more content to review, polished output can be thin on substance, small "15-minute" tasks pile up, and people turn to AI instead of each other. Super teams respond differently. They are three times more likely to share their best prompts, they push back on weak outputs, and they share AI work only when it adds something. Their leaders ask where AI is worth adding, not simply where they can use more of it.
3. Think first, then bring in AI
Starting with AI can mean handing off the thinking itself, and it tends to produce average ideas. Friedman recommends generating your own ideas first, then using AI to broaden the options and find the weak spots. It's the same principle behind brainwriting, where each person develops ideas independently before the group builds on them together.
4. Ask "What are you stuck on?"
Most team meetings focus on recent wins. Super team leaders ask what people are stuck on instead. The question signals that obstacles are normal when you're aiming high, and it turns the meeting into a place where colleagues help solve real problems.
5. Culture follows what leaders reward
Culture is shaped by what leaders say, what they pay attention to, and what they reward. When those signals conflict, such as praising work-life balance while promoting people who work every weekend, teams learn to stop trusting what they hear.
Why this resonated
At Forever Human AI, our guiding belief is that technology is here to serve you, not the other way around. Friedman's research puts numbers behind that idea. Giving people access to AI isn't enough. Teams need shared guidelines, protected focus time, and leaders who measure AI by the value it creates, not the volume it produces.
That's the lens we bring to every engagement: design for the people who will use the technology, not just the technology itself.
I’m also preparing a recap of my own CCE panel, "Lessons Learned in Governance: AI in the Boardroom", so more from CCE 2026 is coming soon!
Comments