Leadership and change management in the AI era: from automation to augmentation

By Richard Steele (McKinsey Quarterly) · July 1, 2026.

The article, by Richard Steele, a McKinsey partner in New York, starts from a clear premise: traditional change management was already under pressure before AI burst onto the scene, and now the AI boom has added a further layer of disruption that demands a complete rethink of the role of leadership.

Steele explains that, in the past, organizations managed discrete, time-bound changes —implementing a new system, restructuring a function, rolling out a defined process— with relatively stable end states. Today, by contrast, the environment is defined by continuous disruption: the number of change initiatives that executives at large corporations have taken on has soared over the past decade, to the point that many organizations are running dozens or hundreds of initiatives deemed maximally urgent at the same time.

This overload has a direct effect on employees: many are opting out of discretionary efforts tied to these initiatives, even when taking part could benefit their careers. The author cites research from the McKinsey Health Institute that has found that mental health and well-being problems are widespread in today's workforce, with significant shares of employees reporting symptoms of anxiety or burnout. According to Steele, "something fundamental is not working."

The AI boom, the article notes, has produced three additional surface-level effects on change management. First, it has further increased the number of initiatives companies pursue or feel they must pursue. Second, because AI itself is advancing so quickly, executives feel the need to speed up not only AI experimentation and adoption but other change initiatives as well; for example, because AI works best with modern technology systems and high-quality enterprise data (and can also accelerate software development), many companies are speeding up efforts to modernize legacy technology platforms, which in turn raises expectations about the pace of transformation across the board. Third, because AI has the capacity to automate parts of many jobs in the future, it has raised employee anxiety levels and labor-market uncertainty that were already too high.

A point Steele especially stresses is that these problems are compounded by the fact that AI use has not penetrated far enough into management ranks. Executives tend to adopt AI tools less often, partly because of the nature of their roles, their own time pressures, and because they are not in a position to experiment as much as more junior employees do.

The author offers a direct, personal recommendation: his advice to leaders is to step back and reimagine their own AI-augmented roles, with much of their routine work automated. Doing so, he argues, will help them realize that what they most need to accelerate is their own learning.

Steele describes how what was once a widening digital divide has now become an "AI divide," one that extends beyond simply using tools to understanding what the new AI tools can do and how long tasks should now take. This costs leaders credibility, and employees notice; deep down, many leaders are aware of it too, which has led some to lose confidence in their ability to lead on the basis of accumulated experience.

To address this, the article argues that leaders need to reimagine their own roles as reshaped by AI. AI can help them frame illuminating questions about the future of the business and how value will be created: how to think differently about risk, innovation and new business models; where the organization should accelerate adoption and where it should be more selective; which activities no longer matter as much and can be stopped to free up capacity and give employees some breathing room.

Steele insists that stepping back to reimagine leadership roles, with much of the routine work automated, puts leaders in a better position to help others experiment with new tools, because they will first have accelerated their own learning.

One particularly illustrative analogy the author uses compares current progress in AI adoption to being, say, three years into the development of steam power: a point at which a new technology's potential is not yet fully understood because its impact is still hard to measure. From that vantage point, it is clear that leaders can do more to build learning cultures. They should conceive of leadership itself as an exercise in helping others learn, including a shift from a directive stance toward a question-asking role that generates energy rather than adding to collective exhaustion.

The author also warns, however, that leaders must think more strategically about the gains AI makes possible. Many organizations are settling for obvious applications focused on task automation when they could be planning bigger, more imaginative bets. He gives a concrete example: instead of automating tasks to save fractions of a researcher's time, organizations could empower those researchers to use AI to run thousands of trials simultaneously.

The article's central conclusion is that the opportunity for leaders lies in looking at AI from a human-augmentation point of view, not just a workforce-automation one. These significant mindset shifts can help leaders manage disruption in new ways. Change management, Steele argues, is now about reinvention, and it requires leaders to move beyond discrete programs and initiatives to instead lead continuous adaptation across their organizations. Their own roles can evolve too: 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 continuous uncertainty and disruption.

The article is edited by Barbara Tierney, a senior editor in New York, and notes that Richard Steele is a partner in McKinsey's New York office. The email also links to other related pieces by the same author, such as 'Change is changing: How to meet the challenge of radical reinvention,' on how leadership must rethink its traditional change-management tools, and 'Bias Busters: Escaping the echo chamber at the top,' on biases such as egocentric anchoring and authority bias that can amplify leaders' perspectives and silence everyone else's.