AI for AI

Part 2 of 2

By Larry Pearlman and Eduardo Lan

In Part 1, we explored why traditional change management falls short for AI adoption. The urgency already exists. The experimentation is already happening. What’s missing isn’t motivation. It’s permission, access, and legitimacy that only leadership can provide.

And yet, even knowing this, we fall into the same trap many leaders do. When someone describes a problem, the instinct is to diagnose it, dissect it, figure out what’s broken, and fix it. That mindset has served organizations well in many contexts, but only up to a point. It can be effective with predictable change, but not so much with transformational one. Leading with “what’s broken” puts people on the defensive and drains energy. You sometimes get commitment. But you rarely get the creativity required for real transformation.

A different approach

Appreciative Inquiry offers a fundamentally different starting point: What gives life to this organization when it’s at its best, and how do we create more of that? It’s important to understand that Appreciative Inquiry is not a tool you deploy during a change initiative and put away when the project ends. It is a mindset, a way of engaging with people and organizations that begins with genuine curiosity about what is already working. You don’t “do” Appreciative Inquiry. You practice it. It shapes the questions you ask, the stories you listen for, and the possibilities you help people see.

This reframe is especially powerful for AI adoption. It recognizes that successful AI integration and human flourishing are not competing goals; they are mutually reinforcing. AI delivers its promise only when people are freed to do what humans do best. Many people at work today are overloaded. Emails, reports, meetings, and administrative tasks consume the time and attention needed for deep thinking and meaningful collaboration. AI offers a chance to reverse that trend. When AI handles data processing and information synthesis, organizations can reclaim space for creativity, connection, and insight. We’ve seen this happen in organizations that approach AI adoption through a human centered lens.

As David Cooperrider observed, organizations move in the direction of the questions they ask most persistently (Cooperrider & Srivastva, 1987). If we keep asking, “Why aren’t people adopting AI?” we’ll get better at cataloging resistance. If instead we ask, “What becomes possible when humans and AI work together at their best?” we open the door to transformation.

Using the 4D cycle to understand how organizations thrive through disruption

The 4D cycle, Discover, Dream, Design, Destiny, provides a practical way to examine how organizations have successfully navigated past disruptions and to apply that intelligence to AI adoption. In the Discover phase, teams surface stories of times when they adapted to major technological or operational shifts and emerged stronger. These stories reveal the conditions that enabled success: clear purpose, strong relationships, psychological safety, visible experimentation, and shared ownership. In the Dream phase, people imagine what work could look like if those same strengths were applied to AI. The Design phase turns those insights into concrete practices: ways of working, decision making norms, and partnerships between humans and AI that build on what already works. Finally, Destiny reinforces these patterns through ongoing learning, peer communities, and leadership behaviors that keep the organization oriented toward possibility rather than fear. By using the 4D cycle to reflect on past successes, organizations build confidence and clarity about how to approach AI adoption with the same strengths that have carried them through previous transformations.

Accelerating AI adoption through appreciative inquiry

Accelerating AI adoption begins with creating structured, low risk opportunities for people to experiment, learn, and see value quickly. Leaders can start by forming small, cross functional “AI Learning Labs” where employees explore real tasks that drain time and energy: incident reporting, data analysis, documentation, audits, training development. In these labs, people test how AI can streamline that work and free them for what actually requires human judgment and creativity. These labs should be intentionally diverse, mixing frontline employees, supervisors, and technical staff so that insights reflect the full system. Leaders then translate early wins into simple, repeatable workflows that clarify the partnership between humans and AI: what AI does, what humans do, and how the two reinforce each other. This approach builds confidence, reduces fear, and creates visible proof that AI is not replacing people but amplifying their ability to think, collaborate, and solve problems.

The next step is scaling adoption through shared learning, visible sponsorship, and consistent reinforcement. Leaders should establish regular “AI Showcases” where teams demonstrate what they’ve learned, highlight practical use cases, and share templates others can adopt. This builds a culture of transparency and accelerates diffusion of effective practices. At the same time, leaders must remove barriers that slow momentum: access restrictions, unclear policies, and inconsistent guidance. They should set expectations that AI experimentation is not only allowed but encouraged, and they should model this behavior by using AI in their own work. Finally, leaders embed AI into daily routines: pre shift huddles, safety meetings, planning sessions, and after action reviews. When AI becomes part of how the organization thinks, learns, and collaborates, not a separate initiative, adoption accelerates naturally and sustainably.

Take the next step

EHS work is entering a period where complexity moves faster than our traditional tools can keep up. More data, more controls, and more oversight will not get us where we need to go. But AI can, if leaders create the conditions for people to explore, learn, and apply it in ways that strengthen safety, not strain it. Appreciative Inquiry offers a path forward by helping leaders focus not on what is broken, but on what gives life to their organization at its best, and how those strengths can guide AI adoption.

Leaders can practice Appreciative Inquiry to discover where experimentation is already happening, where people feel most energized, and where AI is quietly reducing burdens or improving decisions. From there, they can:

• Launch short Appreciative Inquiry interviews with teams to uncover existing bright spots in AI use, however small, and the conditions that made them possible.
• Create spaces for experimentation, where employees can apply AI to real EHS challenges such as reporting, analysis, risk reviews, learning, and communication.
• Invite teams to co‑design “ideal future” workflows using the 4D cycle, so that humans and AI are intentionally paired where each contributes its best.
• Embed and scale what works, reinforcing the practices, relationships, and leadership behaviors that help AI strengthen, not replace, human capability.

By using Appreciative Inquiry as the starting point, leaders legitimize experimentation, surface what already works, and accelerate the shift toward a more productive, proactive, human centered approach to safety. If this direction resonates, we’d welcome the conversation.

References

Cooperrider, D. L., & Srivastva, S. (1987). Appreciative inquiry in organizational life. In R. Woodman & W. Pasmore (Eds.), Research in organizational change and development (Vol. 1, pp. 129 to 169). JAI Press.

Larry Pearlman is Vice President of EHSS at AFL Global with over 35 years of experience designing and implementing transformational safety and culture change initiatives for Fortune 100 companies across aviation, energy, manufacturing, and transportation.

Eduardo Lan brings over 25 years of consulting experience in Human and Organisational Performance (HOP), Human Factors, safety, organisational transformation, leadership development, appreciative inquiry, and AI integration and adoption into safety and business processes, working with Fortune 100 companies and their people in oil and gas, mining, manufacturing, utilities, automotive, among other industries.