Good Teams, Bad Teams in the Age of AI
This week ran as a set: Part I named the activity trap, Part II showed why fixes ripple sideways, and Part III turns to the humans inside the system — drawing on Steven Thurber and Bryan Miller’s Good Team, Bad Team (Page Two, 2024).
The core reframe: as AI agents join the team, human team quality matters more, not less — technology amplifies whatever dynamic already exists. An agent doesn’t fix a bad team; it gives one a faster way to be wrong together. Deloitte found 56% of leaders design AI for business outcomes but only 40% for both business and human outcomes. Thurber and Miller’s distinctions translate directly: good teams create psychological safety to challenge AI recommendations and hold a shared definition of winning; bad teams suppress dissent and drift into activity without alignment. The difference is rarely expertise — it’s whether disagreement surfaces before deployment or only after an incident. One plant’s challenge protocols caught edge cases on a whiteboard; another found the same ones on the production line.
Building the good version is deliberate: select for collaborative capacity, define decision rights, reward challenge of humans and AI, invest in facilitation, review team effectiveness like technical performance. Your action this week: ask when someone last challenged an AI recommendation out loud — and what happened to them. Full model at renegrywnow.com.
Reflection questions
When did someone last challenge an AI recommendation on your team out loud — and what happened to them?
Does your best human–AI team differ from a weaker one in expertise, or in whether dissent is allowed to surface?
Do you review team effectiveness with the same rigor you apply to technical performance?
Keywords: Team Effectiveness, Good Team Bad Team, Thurber Miller, Psychological Safety, Human-AI Collaboration, Challenge Protocols, Decision Rights, Cross-Functional Teams, Physical AI, Leadership
Series: Energy Dominance · Week 35 · Part III
Previous: Part I — AI Activity vs Real Progress · Part II — Systems Thinking in Autonomous Operations.
