Building High-AQ Teams That Outperform
By Bruce Wade
Sarah Martines had a problem. Her star engineer was manually recreating work their AI system had completed perfectly. Despite having cutting-edge tools and comprehensive training, half her development team was avoiding AI collaboration while still meeting productivity targets.
The irony? As director of software development for a major fintech company, Sarah led a team responsible for building AI-powered products. Yet her own team struggled with human-AI collaboration.
Traditional team management focuses on coordinating human activities and managing individual performance. High-AQ team leadership requires orchestrating relationships between humans and AI systems, developing AI collaboration competencies, and creating cultures where human-AI partnership feels natural.
High-AQ teams develop distinctive characteristics. They exist along a collaboration spectrum from basic (AI as sophisticated tools) to elite (seamless integration creating entirely new collective intelligence). Understanding this spectrum helps managers assess current capabilities and identify development opportunities.
Successful teams develop shared mental models about AI capabilities and limitations. Team members understand not just what AI systems can do, but when and how to leverage those capabilities most effectively. This shared understanding prevents duplicated effort while ensuring appropriate oversight.
Knowledge sharing happens naturally in high-AQ teams. Members share discoveries, troubleshoot challenges, and build on each other’s successes. This informal learning often proves more valuable than formal training because it addresses real challenges with practical solutions.
The best AI team isn’t the one with the smartest algorithms—it’s the one with the strongest human-AI relationships. As I explore in “The AQ Leader,” building these teams requires fundamentally different leadership approaches most managers have never learned.
