What can help leaders more quickly gain value from AI?
The 5% of organizations succeeding with AI have leaders who can navigate uncertainty, build trust, and rethink work when AI handles routine tasks. We’ve explored what makes these leaders different. Individual coaching helps as it gives leaders space and tools to develop critical leadership capacities.
But there’s more to an effective AI adoption strategy. Here’s what it is, and what it could mean for how organizations approach AI readiness.
Individual and collaborative learning work together
Individual coaching builds confidence and gives leaders space to work through their specific AI challenges. But when you add structured opportunities for leaders to learn with others facing similar challenges, the development compounds.
In individual coaching, a leader might work through their uncertainty about which AI use cases to prioritize. They build a framework, gain clarity, and feel more confident. That’s real progress.
When that same leader also connects with peers working through similar decisions, something shifts.
What changes in peer learning:
- You hear how others approach the same problem
- You compare frameworks and spot gaps you’d miss alone
- You realize uncertainty is normal, not a personal failing
They realize other leaders are also navigating uncertainty, which normalizes the discomfort of not having all the answers.
Why discomfort matters
Most leaders are consciously incompetent with AI, a term from the conscious competence learning model popularized by Noel Burch in the 1970s. This framework describes four stages people move through when developing new skills: unconscious incompetence (you don’t know what you don’t know), conscious incompetence (you’re aware of what you don’t know), conscious competence (you can do it but must concentrate), and unconscious competence (the skill becomes automatic).
With AI, most leaders are in that second stage. They know they don’t know. That awareness can spark motivation, but it can also trigger avoidance, inadequacy, even shame.
Shared struggle becomes productive learning. Isolated struggle becomes avoidance.
When leaders experience that discomfort alone, it’s easier to avoid the challenge and potentially get bogged down with those feelings. When they experience it together, the shared struggle becomes part of the learning. The discomfort doesn’t disappear, but it becomes productive rather than stifling.
Why peer learning accelerates development
Decades of research across multiple disciplines shows how we learn in relationship with others.
- Social learning theory emphasizes how leaders’ behaviors are reinforced by observing and modeling others.
- Sociocultural theory reveals how learning is grounded in culture and supported by knowledgeable others.
- Situated learning demonstrates that learning happens in the specific context where it can be directly applied.
This means leaders learn by watching peers navigate real challenges, building shared language around how AI should be used in their organization, and stress-testing ideas in authentic contexts rather than abstraction. Connection accelerates development.
Moving from “I don’t know what I’m doing” to “I can lead effectively through this” happens faster when leaders learn together.
What happens when leaders learn AI together
Consider a cohort of leaders from a financial services company who are all experimenting with AI in their departments. Marketing is testing content generation, operations is looking at process automation, customer success is exploring chatbot applications.
Initially, each leader is working 1:1 with their coach on their specific department’s challenges. They’re making progress, but they’re doing it in silos. They aren’t connecting with each other.
What changed
The dynamic changes when they connect. The marketing leader shares that her team is worried AI will make their work less creative. The operations leader admits he’s overwhelmed by the pace of AI capability changes and isn’t sure where to focus. The customer success leader talks about trust issues. Her team doesn’t trust AI outputs enough to use them with customers.
These aren’t new problems. But naming them together changes the dynamic. They realize they’re all dealing with versions of the same challenge of helping people feel capable and confident, not just trained.
They start sharing approaches:
- Marketing’s “AI as thought partner, not replacement” framework helped customer success reframe team conversations
- Customer success’s prioritization method helped operations cut through competing priorities
- Operations’ approach to managing pace helped marketing move forward when decision-making stalled
Six months later, all three departments have scaled their initial pilots. But more importantly, the leaders have built a practice of learning together. The speed of their AI adoption doesn’t just increase. The quality of their implementation improves because they’re stress-testing ideas across different contexts before rolling them out.
Five approaches that work
DDI’s Global Leadership Forecast 2025 found that organizations that use five or more development approaches, including peer learning, are 4.9X more likely to report that their programs improve leadership capabilities. Multiple methods working together create the strongest impact.
Based on what we know about adult learning and what I’m seeing work in designing experiences, here are five approaches that can create the conditions for collaborative learning:
- Group coaching for AI challenges
Cohorts of leaders work through AI adoption challenges with a coach facilitating. This combines expert guidance with peer learning, building psychological safety while developing leadership capacities together. At Torch, we design group coaching programs that address AI challenges in real-time. - Cross-functional action learning
Small teams from different departments tackle real business problems using AI. Regular check-ins, shared accountability, and permission to fail all matter here. Marketing leaders partner with finance teams to redesign processes. They identify gaps, test assumptions collaboratively, and build solutions neither would have designed alone. - Peer coaching partnerships
Pair leaders with different AI experience levels and give them protected time to share approaches, troubleshoot challenges, and explore use cases. Cross-department pairings work particularly well. Different perspectives reveal assumptions that might otherwise stay invisible. - Executive learning communities
Bring together executives from across the organization (or even across non-competing companies) to share what they’re learning about AI strategy. Rotating facilitation, shared frameworks, and commitment to candid exchange make these work. Leaders need spaces where they can admit what they don’t know. - Leaders as learning role models
Executives who visibly engage in collaborative AI learning (participating in group coaching, joining action learning teams, openly sharing what they’re trying and what’s not working) normalize experimentation and collective learning. Their presence signals that leaders shouldn’t figure this out alone.
What the research shows
LinkedIn research found that professional networks are the #1 trusted source for advice, ahead of AI tools and search engines. 43% of professionals rely on the people they know.
AI readiness isn’t just about developing individual leaders. It’s about creating the conditions for learning to spread across the organization through relationships, not just through curriculum.
When leaders learn AI collaboratively, research shows clear benefits.
- For individuals: Collaborative learning shows positive relationships with meta-cognitive skills, critical thinking, and creative thinking (Khasawneh et al). Managers with peer-based learning experiences are 18% more likely to excel (Gartner).
- For teams: Performance improves measurably. One study found that positive social-emotional interactions during collaborative learning promoted conflict resolution (Hu et al). When teams more effectively regulated their learning during collaborative learning activities, they performed better than those with reduced regulation patterns (Zhang et al).
- For organizations: Organizations using collaborative learning report measurable business outcomes: 56% see better efficiency, 52% better engagement, and 50% meet business objectives (Intrepid Learning). Companies with comprehensive leadership programs are 4.2 times more likely to deliver superior financial performance (Culture Partners). Collaboration-centered initiatives can boost productivity by 20–25% (McKinsey).
What this means for organizations
Most organizations approach AI readiness through technical training. Get people using the tools, and hope leadership development happens on its own. A smaller number are investing in leadership development to support AI adoption, but even then, many design for individual learning. 1:1 coaching, self-paced courses, individual skill-building.
The organizations making real progress do something different. They design for collaborative learning from the start. They build it into their strategy from day one because they know leadership capacities develop faster when leaders work through AI challenges together, not in isolation.
The 5% of organizations succeeding with AI aren’t waiting for leaders to figure it out alone. They’re building collaborative learning into their strategy from the start.
Which raises the question I’m working through next. In designing leadership development experiences, how can we use AI to embed collaboration by default?
The challenge isn’t getting leaders to use AI tools. It’s creating the conditions where they develop the capacities to lead through AI transformation together, not in isolation. That’s what we design for at Torch.
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Ready to build collaborative learning into your AI readiness strategy? Let’s talk.
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