When talking with fellow leaders about what’s changing in the age of AI, I rarely hear about efficiency gains and workflow automation. Instead, conversations drift toward an existential sense of disruption, with questions that cut deeper than FOBO (fear of becoming obsolete). Who am I as a leader when my accumulated knowledge is accessible to anyone? What will we be to each other when the roles we once lived by are reshaped beyond recognition?
AI is pressing on assumptions fundamental to leadership itself. What should a leader know, do, and care about? What is the relationship between a leader and those they lead?
And while organizations focus on integrating tools and automating processes, there is something vital and human that leaders must take up at the same time. We have to examine the leadership disruptions emerging as AI reshapes the very nature of work and identify the human essentials that will define leadership going forward.
Here’s what I’m finding on that quest.
Meaning Disruption: A Leadership Gap AI Can Never Close
AI excels at optimization. It processes, predicts, and recommends with staggering speed. What it cannot do is care. It produces outputs without understanding why they matter, to whom, or at what cost. This creates what we might call a meaning gap.
Aaron Dignan, an entrepreneur working at the intersection of AI and organizational change, recently shared a playful description of how large language models “speed date” to build ‘meaningful interpretations’ of text input. Rather than building progressive understanding by reading words in the order they have been written, LLMs take in the entire passage at once – comparing each word with every other and mapping statistical relationships until the likeliest interpretation emerges. As Dignan puts it, it’s as if each word is asking all the others, “hey, are we related?” The result is a dense tapestry of associations across an entire passage. It’s extraordinarily effective at producing coherent conversation, but it isn’t meaning as we know it. It’s probability.
This is a useful analogue for a larger meaning gap created by AI. Automation optimizes decisions but flattens the vital context accumulated through lived experience. Without that context, data replaces dialogue and harm becomes harder to see. Connection thins and people lose sight of what the work is for. Organizations drift into numb efficiency—hitting metrics while hollowing out purpose.
Harvard professor Arthur Brooks offers a counterintuitive lens here. We’ve nearly eliminated boredom from modern life, he argues, through constant digital stimulation. In doing so, we’ve shut off access to the deeper questions. Meaning and purpose surface when our minds aren’t occupied. As AI promises to fill any cognitive gap with useful output, perhaps we can reclaim time for reflection and find meaning in questions that have no efficient answers.
Leaders now serve as stewards of meaning in organizations where speed threatens to crowd it out. They make values visible in decisions and hold accountability for the human consequences of AI-assisted choices. They stay connected to lived experience, not just dashboards. This is leadership as moral and relational grounding—and it is the work no algorithm can perform.
Identity Disruption: When the Old Anchors Disappear
For decades, leadership identity rested on familiar anchors: expertise, control, and being the person who knows. AI destabilizes all of them. When a system can access more information, process it faster, and generate competent answers on demand, the leader-as-expert model—which had already been cracking—crumbles.
The conversations I’ve had reveal leaders grappling with this erosion. Some feel diminished. Others describe a creeping irrelevance—a fear of being replaced not by a robot but by a more AI-fluent colleague down the hall. The emotional ballast that came from mastery? Gone.
In a world where AI provides competent answers instantly, the right questions matter more than the right answers. Leadership identity has to shift from I know to I learn and from mastery to inquiry. The educator Parker Palmer, in his work on vocation and the inner life, offers an insight: sustainable leadership must come from within—from values and presence rather than position or expertise.
As technology confounds identity and what was once inimitable is now automatable, it can be tempting to lead with an unfiltered version of oneself—tapping into something that feels raw and authentically human. Psychologist Tomas Chamorro-Premuzic, who studies leadership and AI, cautions against this, warning that this interpretation of authenticity can invite chaos, cruelty, and fatigue. Instead he calls for responsible transparency or ‘sharing what is necessary, admitting what you do not know, and regulating yourself in service of the mission.’
When the role-based self erodes, only an internally anchored self can act with clarity rather than fear. This isn’t comfortable. It requires leaders to tolerate not knowing while remaining calm enough to help others do the same.
Systems Disruption: From Tin Man to Octopus
AI has the potential to reshape how organizations operate by flattening structures, compressing decision cycles, and blurring roles as tasks are divided between humans and bots. Information flows everywhere at once. The familiar levers of control stop working. A recent Harvard Business Review piece by Jana Werner and Phil Le-Brun captures this shift through an unexpected metaphor. Most organizations, they argue, are “Tin Man” structures—rigid, predictable, optimized for control. Like the character in The Wizard of Oz, they’re slow to move and react. They take instructions but show little initiative. They were built for a complicated world where problems had known solutions and stability was the norm. That world is gone.
The alternative is what they call the “Octopus” organization. Its arms think and act independently but also work in concert. It thrives in complexity because it distributes intelligence rather than centralizing it. It senses, responds, and learns continuously. Strength comes from adaptive coordination rather than rigid control.
For leaders, this demands a shift from controlling the system to guiding its adaptation. Coordination happens through clarity, not command. Decisions get made iteratively, with partial information. The leader’s job becomes working on the system—removing friction, clarifying purpose, creating conditions for others to excel—rather than directing every move within it.
Development Disruption: Beyond Leadership Skills to Bigger Minds
All of this exposes the limits of how we’ve developed leaders. Skill lists age out instantly. Knowledge is no longer a differentiator. The old competency models were designed for a world where expertise retained value over time. That assumption has collapsed.
What’s needed now is what developmental psychologists call vertical development—not acquiring more skills horizontally, but expanding the complexity of how we think. Robert Kegan’s research shows that many leaders are operating “in over their heads,” facing challenges that exceed their current meaning-making capacity. The solution isn’t to simplify the world. That’s impossible now. It’s to grow our capacity to meet it.
Bill Torbert’s work on leadership action-logics offers a map for this growth. His research identifies seven stages of development. Most leaders plateau at what Torbert calls “Expert” or “Achiever”—stages focused on technical mastery and goal attainment. Useful stages. But the AI age demands what he calls post-conventional thinking: the capacity to question assumptions, hold paradox, and adapt mental models in real time.
What differentiates leaders, Torbert argues, is not philosophy or management style but “action-logic”—how they interpret situations and respond when their power or certainty is challenged. AI challenges both constantly. The leaders who thrive won’t be those with the most skills; they’ll be those who’ve evolved how they think.
The Clarification, Not the Crisis
It’s tempting to see all this as a crisis of leadership. I’d frame it differently. AI is clarifying what leadership is actually for.
Strip away the expertise that machines can replicate, the control that complexity makes impossible, the skills that become obsolete before they’re mastered. What remains is the human: presence, purpose, judgment, connection. These were always the core of the work. We just couldn’t see them clearly when they were buried under everything else.
Effective leaders in this new era won’t be those who fight to preserve past notions. They’ll be the ones who understand that this leadership disruption can be a reclamation instead of a collapse—revealing that what AI cannot do is precisely what matters most.