AI transformation: 4 ways to build human-centred movements that deliver results

Organizations are bleeding capital on investments in artificial intelligence (AI), with often little to show for it. As executives throw cash at expensive tools and chaotic pilot projects, the promise of AI-driven transformation remains elusive.

The problem isn’t the technology. Too many leaders rely on traditional change management thinking and old-school training methodologies. Plus, many employees hesitate to step into AI because it seems like an extra task.

Only a small percentage adopt AI enthusiastically. Elite organizations are taking it on more seamlessly by re-engineering how they operate and building grassroots movements inside their organizations.

Here are the four game-changing strategies to galvanize your employee base to successfully propel your AI projects.

1. Harness peer networks

Traditional technology rollouts fail with AI. Instead, you must build momentum from the ground up by:

• Surfacing super users: They identify who’s making AI work because they’re already experimenting and creating value. Invite these employees to share their AI use cases while others are still stuck in training.
• Creating peer coaching circles: Four super users meeting weekly creates magic. Place them in small groups to share practices, coach each other and document successes. Bypass consultants for your in-house practitioners to elevate winning approaches. You only need outside expertise when internal innovation falls short.
• Cross-pollinating ruthlessly: Groups can present findings to others and document them in an accessible knowledge base. Remix groups systematically with new members until proven methodologies emerge organically.
• Scaling through peer-to-peer networks: These super users become coaches for others. Every expert takes on a small group of volunteers hungry to learn. Knowledge cascades through organizations with minimal central coordination or mandates.

This approach works because it taps into fundamental human psychology – people adopt what their respected peers are using, not what management dictates; it’s how grassroots movements begin.

2. Reimagine workflows and business models

AI’s real power lies in reimagining workflows across functions – not just boosting productivity. Instead of calling meetings, pose a powerful question beforehand and have teams collaborate in shared documents to co-create solutions.

Effective collaborative problem-solving is a structured practice that my research shows increases inclusion and innovation:

• Leverage asynchronous collaboration: Send a shared document two weeks before meetings with one or two key questions: How is AI impacting your roles? How are you using AI now? What workflows should we fundamentally rethink using AI?
• Broaden participation: Include perspectives across functions, levels and external partners.
• Focus on outcomes: Challenge teams to reimagine workflows based on desired outcomes with clear measurements.

Use a shared spreadsheet listing all attendees with space for their insights and encourage them to gather input from their teams. This async approach prevents later commitments from backsliding by ensuring all data is available upfront with a clear decision trail.

After everyone reviews inputs, the meeting leader determines:

• What agenda will finalize decisions?
• Who needs to be involved?
• Can immediate “yes,” “no” or “maybe” feedback be given?

This approach helps organizations move beyond incremental changes to radical reinvention. For example, one international manufacturer, known through our client base, completely reimagined their customer support workflow, reducing resolution time while increasing satisfaction scores.

3. Assemble cross-functional AI squads

Last year, a group of executives calling themselves the Radical Innovators Collaborative met frequently to define the biggest breakthrough AI use cases in the workplace.

Companies such as Kearney and Distyl demonstrated progress in re-engineering supply chains, achieving the greatest breakthroughs when they included the most diverse perspectives.

If the transformation was only led via the supply chain, the results would be less impactful than if sales, procurement, manufacturing, the chief information officer and the change management enablement function partnered in the most co-creative way.

It requires radical curiosity and looking at all vantage points to re-engineer workflows.

An AI team isn’t a tech team; it spans organizational boundaries. Rather than organizing around outcomes and existing organizational structures. Each major AI initiative should have two teams:

• Core team: They own the initiative’s success, with dedicated time allocation and clear accountability – it shouldn’t be staffed exclusively with technologists.
• Engaged team: Map out the critical stakeholders whose support, expertise or influence you need. This includes the quiet voices with crucial context and the loud detractors who can derail everything.

Document every relationship critical to success. In a relationship action plan, rate each connection quality from -1 (actively opposed) to 5 (true ambassador). Prioritize improving relationships that will make or break your initiative.

4. Build team resilience, not individual endurance

Transformation initiatives often fail because people get tired, overwhelmed and resistant. Leaders commonly respond by sending people to workshops and offering wellness programmes. However, resilience building should be approached team-wide.

Our research shows that only 14% of team members feel a collective responsibility for each other’s energy and well-being but elite teams use targeted practices to build collective resilience:

• Energy check-ins: Begin meetings by asking team members to rate their energy level from 0-5, explaining why. The opportunity to share and be vulnerable creates transparency about challenges, allowing teams to adapt and support each other.
• Peer celebration: Make it a monthly practice for each team member to recognize others’ contributions. This isn’t generic praise – targeted appreciation reinforces the behaviours driving transformation.

These practices are the foundation of psychological safety, enabling bold experimentation and candid feedback – essential for successful AI implementation.

Master teamship: The ultimate competitive edge

The organizations seeing the greatest returns from AI aren’t distinguished by technical expertise. They’re characterized by what I describe in my book, Never Lead Alone, as “teamship.”

This is the ultimate competitive advantage, shifting from top-down leadership to peer-to-peer teams sharing the leadership load and committed to lifting while challenging each other to achieve world-class performance.

The AI revolution will separate winners and losers in every industry. The winners won’t be those with the most advanced tools or the biggest budgets. They’ll be organizations that master building movements, reimagining workflows, assembling the right teams and creating collective resilience.

The technology is available to everyone. But a human-centred, collaborative approach makes the difference.