Design Feedback Loops

The careful design of feedback loops is critical to developing an organization’s AI muscle. Thoughtfully cultivate communication channels that will surface concerns and tap into grassroots innovation happening closest to workflows and customers. Use this structure to nurture a shared understanding of responsible AI development and foster meaningful dialogue that supports organizational change. Formalize regular retrospectives or debriefs after implementing new AI approaches, making continuous improvement an integral part of your process.

Make It a Team Sport

Accelerate learning by forming smaller “learning circles,” or workgroups of people united by their enthusiasm to explore specific AI topics. Establish a regular format and frequency for sharing sessions where members discuss their discoveries, explore implications, and refine key questions. This approach ensures time is allocated to the open-ended exploration that is so important for driving new insights and can energize learning efforts because people feel accountable to their team members. Additionally, organize team activities that make it fun to explore use cases for AI. I’ve had clients create “Curiosity Cafes” (informal learning and ideation spaces complete with baristas), “No-Code Hackathons” designed specifically for non-technical newbies, “AI Challenge Leagues” to gamify the exploration process, or “Swap Sessions” where team members can freely discuss AI questions and experiences over food or drinks. Organizations often find there is so much interest in learning about the technology that events are frequently oversubscribed. When this approach works best, it ends up building a community of AI explorers in your organization, and by not only plugging into this community but supporting and celebrating its success, you can foster a great deal of AI momentum in your company.

Model Success with Storytelling

Storytelling is a powerful tool for bringing your organization’s AI journey to life. It can vividly demonstrate how AI brings value and supports strategic objectives, while also honestly portraying the challenges encountered along the way—and crucially, how these obstacles were overcome. By finding and sharing these stories more broadly, you can show what success looks like, help to ease fears, and make education on more abstract concepts such as responsible AI practices, data privacy, and governance more tangible and engaging. Using diverse formats—written case studies, video interviews, or live presentations—helps to cater to different learning styles and encourages team members at all levels to share their experiences.

Establish Regular Pulse Checks

Implement regular checkpoints to assess your team’s AI learning progress. Use these sessions to surface new key questions and evaluate the outcomes of your AI experiments. Discuss as a team how to improve these initiatives and identify new areas where AI could add value. Encourage open dialogue to surface team concerns and evaluate your responsible use of AI.

Prioritize Responsible AI Practices

Make discussions about AI ethics a regular part of team meetings to ensure everyone understands and commits to responsible AI practices. If your company has a responsible AI team, collaborate closely with them. If not, advocate for its formation. Integrate ethical considerations into every phase of your work, and regularly assess the ethical implications of your AI use cases.

Navigating AI as a Team

Just as individuals have varying levels of experience with AI, from novice to expert, teams exhibit a wide spectrum of AI proficiency. In the same way it can be challenging for individuals to start thinking with AI, establishing a collective “practice of using AI” within an organization can be difficult. It can be hard to bring all team members to a common understanding of AI capabilities, develop a shared language around AI concepts, and align on where and how to experiment with AI in their work together.

As we discussed in Chapter Six, using AI at the wrong time can hinder our cognitive performance. This principle applies to teams too: we must invest in helping our teams grasp the subtleties needed to prevent AI overreliance from short-circuiting their collective brain power. A recent study found that if teams don’t have a deep understanding of when and how to apply AI, it may result in only modest gains in team creativity—and that some AI-using teams even underperformed compared to those relying solely on human intuition.¹

The teams that truly excel with AI grasp the importance of relying on human cognition during crucial parts of the creative process. When they do turn to AI, they approach it as a collaborative partner, iteratively refining its outputs and exploring creative avenues that might otherwise remain unexplored. It’s this nuanced approach—knowing when to lean on human insight and when to tap into AI’s capabilities—that enables teams to truly enhance their performance with AI. This underscores the importance of not simply introducing AI into the creative process; organizations must equip their teams with the knowledge and skills to engage effectively.