FRESH TAKES ON BIG IDEAS

Even before the advent of our AI moment, traditional change management was under strain. Gone are the days when organizations and leaders managed discrete, time-bound changes—rolling out a new system, restructuring a function, or implementing a defined process—with relatively stable end states. Today’s environment is characterized by continuous disruption.

The number of change initiatives that executives at major corporations have taken on has soared over the past decade. Many organizations now have dozens, if not hundreds, of initiatives that they consider of utmost urgency.
 
In response, many employees are opting out of discretionary efforts to participate in these initiatives, even when doing so could benefit their careers.
 
McKinsey Health Institute research has found that mental health and well-being challenges are widespread in today’s workforce, with significant shares of employees reporting symptoms of anxiety or burnout. Something fundamental isn’t working.
 
The AI boom has created further disruption—characterized by three surface-level effects on change management. First, it has turned the dial even higher on the number of initiatives that companies are pursuing or feel they have to pursue.
 
Second, because AI itself is moving so quickly, executives feel the need to increase the pace, not just of AI experimentation and adoption, but of other change initiatives as well. For example, because AI works best with modern technology systems and high-quality enterprise data (and can also speed up software development), many companies are accelerating efforts to modernize legacy technology platforms. This, in turn, raises expectations for broader transformation speed.

Third, because AI has the capacity to automate parts of many jobs in the future, it has increased levels of employee anxiety and job market uncertainty that were already too high.
 
These problems, while difficult, are exacerbated by the fact that AI use has not infused the leadership ranks. Executives can be less likely to adopt AI tools, in part because of the nature of their roles, their own time pressures, and because they’re not in a position to experiment as much as more junior employees do.

“My advice to leaders is to take a big step back and reimagine their roles augmented by AI, with much of their routine work automated. Doing so will help them realize that the thing they most need to accelerate is their own learning.”

What was a growing digital divide is now an AI divide, one that extends beyond usage to include understanding what new AI tools can do and how long tasks should now take. And so there is a loss of credibility for leaders, and employees know it. Deep down, many leaders know it too.

As a result, some leaders have lost confidence in their ability to lead based on experience. To address this, they need to reimagine their own roles in ways reshaped by AI. AI can help them ask illuminating questions about the future of the business and how value will be created.

How can they think differently about risk, innovation, and new business models? Where should the organization accelerate adoption, and where should it be more selective? What activities are no longer as important and can be stopped to create more capacity and give employees a chance to catch their breath?

My advice to leaders is to take a big step back and reimagine their roles augmented by AI, with much of their routine work automated. Doing so will help them realize that the thing they most need to accelerate is their own learning. Then they are better positioned to help others experiment with new tools.
 
If we think of progress on AI adoption as equivalent to being, say, three years into the development of steam power—at a point when the potential of a new technology is not yet fully understood because its impact remains difficult to measure—it becomes clear that leaders can do more to create cultures of learning. They should view leadership itself as an exercise in helping others learn, including by shifting from a directing stance to a questioning role that creates energy rather than contributing to collective exhaustion.

But they also have to think more strategically about the gains that are possible through AI. Many organizations are opting for obvious applications that center on task automation, when they could be planning to take bigger, more imaginative swings. Instead of automating tasks to save fractions of a researcher’s time, for example, they could be empowering researchers to use AI to run thousands of trials simultaneously. 

The opportunity for leaders is to look at AI from a human augmentation point of view, not just from a workforce automation point of view. These important mindset shifts can help leaders manage disruption in new ways. Change management is now about reinvention, requiring leaders to move beyond discrete programs and initiatives and instead lead continual adaptation across organizations. Their roles can evolve as well—from directing change and providing answers to creating the conditions for learning and experimentation. Rather than simply implementing new processes or structures, they can rethink how a company creates value at its core while helping employees navigate ongoing uncertainty and disruption.

—Edited by Barbara Tierney, senior editor, New York

ABOUT THIS AUTHOR

Richard Steele is a partner in McKinsey’s New York office.