Leading Through Sensemaking in the Age of AI

In recent years, there has been a surge of articles, reports, and commentary aimed at leaders on the future of work and the evolving talent landscape. Much of this attention is now focused on artificial intelligence and related technologies—how AI may automate tasks, augment decision-making, or fundamentally change how work gets done. What is far less common are thoughtful conversations about how leaders are meant to make sense of these changes while they are unfolding, and how leadership itself must evolve as a result.

Rather than predicting or catastrophizing what AI will do to organizations, the focus here is on how leaders can interpret what is already happening, learn in real time, and guide others through ongoing uncertainty. This requires us to revisit long-standing assumptions about how work is organized and how leadership operates, many of which were shaped in a far more stable, industrial-era context.

Few organizations today are immune to uncertainty. As leaders, we are expected to navigate rapidly changing work environments while simultaneously providing clarity and direction to others, often at the same time. This creates a real tension. It is unrealistic to expect leaders to fully sort out their own views on AI-enabled work while also helping others process their reactions, questions, and concerns. And yet, this is precisely the work before us.

At the heart of this challenge is sensemaking. Organizational scholars Karl Weick, Kathleen Sutcliffe, and David Obstfeld, in their 2005 article, Organizing and the Process of Sensemaking, describe sensemaking as the ongoing effort to understand what is happening and determine how to respond. They distill this process into two practical questions: “What’s going on here?” and “What do I do next?” To those, I would add a third question: “What do I need to learn and how will I learn it?”

These questions may sound simple, but they demand discipline. Effective sensemaking requires connecting abstract ideas, like “AI transformation,” to concrete experiences, decisions, and actions. It cannot remain an intellectual exercise carried out in isolation, nor can it rely on leaders positioning themselves as experts in a rapidly shifting landscape. In fact, clinging to expertise can slow learning rather than enable it.

Working with AI places a premium on adaptability, collaboration, and continual learning. Data is more abundant, change cycles are shorter, and teams are often distributed across functions, geographies, and technologies. In this environment, sensemaking becomes less about control and more about curiosity.

One practice that has helped me is treating sensemaking as a series of small learning cycles rather than a single moment of clarity. “What’s going on here?” surfaces complexity. “What do I do next?” positions that complexity in potential next steps. “What do I need to learn and how will I learn it?” moves into action. Each learning-focused action generates insight, and momentum toward continual development. In this way, sensemaking is not something leaders complete before acting—it is something they do through action.

Leading in the age of AI is not about mastering every new technology. It is about developing the capacity to learn, adapt, and act thoughtfully in the midst of change. Sensemaking, then, is less about arriving at the right answer and more about staying engaged with what the work is teaching us as conditions continue to change.

Questions for Reflection

1. In the context of AI and ongoing change, what question are you currently trying to make sense of and what might be one small action that could help you learn more about it?

2. Where might you be waiting for clarity before acting, and how could engaging in a next step generate the learning you need instead?