Most people never change careers, which is remarkable when you consider how little evidence most of us had when we chose our first one.
For many professionals, early career decisions are shaped less by talent or long-term fit than by convenience and coincidence. We follow friends into certain degrees, accept the first decent offer, listen to family advice, or pursue interests that feel meaningful at 18 but prove less durable at 38. These choices are understandable, but they are weak predictors of where our strengths will compound over time, or of what will sustain both performance and satisfaction across decades of work. In essence, we follow our own or other people’s intuition rather than facts or data, which is rarely a recipe for success.
The problem is further compounded by how fast the ground has shifted, especially over the past decades.
Even before the rise of generative AI, career predictability had been steadily eroding. Globalization, repeated economic shocks, declining job tenure, the collapse of clear promotion ladders, and the shift from stable organizational careers to project-based and boundary-less work all contributed to rising uncertainty. Longitudinal labor data shows that occupational half-lives were shrinking well before automation became a mainstream concern, with entire roles emerging and disappearing within a decade. In parallel, individuals were expected to manage their own employability, continuously update skills, and absorb risks once carried by employers or institutions.
AI has not created this uncertainty so much as amplified it, accelerating the obsolescence of skills, compressing career ladders, and blurring professional boundaries. Knowledge and technical expertise, once reliable sources of differentiation, are increasingly commoditized. At the same time, employers place growing value on judgment, learning agility, influence, and curiosity, capabilities that universities still struggle to measure or systematically develop. The result is a widening gap between what people trained for, what they are good at, and what the labor market actually rewards.
It is therefore no surprise that career anxiety is rising, even among people who appear successful on paper. Gallup data shows that roughly 60% of employees feel emotionally detached at work, while fewer than one in four strongly believe their job aligns with their strengths. LinkedIn data consistently finds that the average worker will change roles every three to four years, yet meaningful career pivots remain rare and often delayed until dissatisfaction becomes acute.
At the same time, mobility has slowed. After the Great Resignation came what economists now call the Big Stay: people feel stuck rather than settled. They are rethinking their careers cognitively, but postponing action behaviorally. In other words, “job hugging” has replaced “job hopping”.
So how can you tell whether you are merely going through a rough patch or whether it is genuinely time for a career pivot? Decades of research on career development, identity, and motivation point to four reliable signals.
1. Your learning has stalled, not just your motivation
One of the most robust predictors of engagement and career satisfaction is perceived progress. When people feel they are learning, they tolerate stress and uncertainty better. When learning stops, even high performers disengage.
Professor Herminia Ibarra’s research on career transitions shows that people rarely pivot successfully through introspection alone. Clarity follows action, not the other way around. If your role no longer exposes you to new skills, perspectives, or problems, that is not a temporary slump but a structural constraint. Before quitting outright, experiment: Be ready to fail smart, in the sense of learning from your experience and becoming wiser as a consequence. As the saying goes, “experience is what you get when you didn’t get what you wanted to get”. For example, take on side projects, advisory roles, or temporary assignments that test alternative identities. Stagnation becomes dangerous only when experimentation stops.
2. Your strengths no longer translate into value
Many careers falter not because people lose competence, but because the market stops rewarding what they are good at. Technological change makes this especially common. Skills that once differentiated professionals are automated, standardized, or absorbed into platforms. As I have illustrated in one of my previous books, AI is far more likely to automate tasks within jobs—changing the skills constellation needed to perform them—than actual jobs.
Research on person–job fit shows that sustained misalignment between strengths and role predicts burnout and underperformance, even among conscientious high achievers. A useful diagnostic question is whether your best contributions still feel essential or merely adequate. Successful pivots rarely involve abandoning strengths. They involve redeploying them where they matter more.
3. Your career identity has become rigid
Ibarra’s work highlights that career change is as much an identity transition as a skills transition. People delay pivots not because they lack options, but because they are overly attached to who they think they are supposed to be. This is also why authenticity is overrated: Why limit yourself to your past and present self when you can instead create or sculpt a broader, more diverse, and richer version of yourself?
This is where the “squiggly careers” concept, popularized by Helen Tupper and Sarah Ellis of Amazing If, is especially useful. Modern careers are no longer linear ladders but adaptive paths, shaped by lateral moves, pauses, reinventions, and redefinitions of success. If you feel compelled to defend your current title, industry, or trajectory rather than evolve it, you may be protecting a legacy identity rather than building a future one. Indeed, progress is not a straight line!
4. You are succeeding externally but disengaging internally
One of the most overlooked signals is sustained performance paired with declining well-being. Longitudinal studies show that people can maintain output for years after motivation erodes, but at a cost to health, creativity, and long-term employability.
If your reputation is strong but your curiosity, energy, or sense of meaning is steadily diminishing, that is not ingratitude. It is misalignment. Career satisfaction is not a soft outcome. It is a leading indicator of future performance and adaptability.
In short, we tend to romanticize career pivots as bold acts of reinvention. But the evidence suggests the opposite. Successful pivots usually happen through small, low-risk experiments that reshape identity over time, guided less by passion than by a disciplined willingness to revise assumptions in response to reality.
In an era where work will change repeatedly, the real risk is not changing direction too often, but staying in place long after the signals suggest you should move. The most resilient professionals are not those with fixed plans, but those who know when the cost of standing still has quietly begun to exceed the cost of change.
Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from …
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In a world where trust in institutions is at an all-time low and the pace of change is relentless, the most effective leaders are not those who hide behind polished press releases or corporate jargon. They are the ones who step forward with authentic …
2026 will be a crucial inflection point for businesses. The data are striking—the proportion of employees using AI in their role in the U.S. doubled between 2023 and 2025. Across the Atlantic, 30% of EU workers are already using AI in their jobs. And according to Gartner, by 2026 more than 100 million workers will collaborate with “robo-colleagues.”
The question for the coming year, then, is no longer whether AI will transform your organization, it’s whether your leadership team will guide that transformation thoughtfully or let it happen haphazardly, tool by tool and team by team.
I have spent much of the past year working with my research team and industry partners to think through the most pressing challenges organizations face as they implement AI at scale.
Drawing on this work, I have identified seven key priorities for leaders preparing for 2026. These aren’t isolated tactics but interconnected imperatives that, taken together, provide a road map for building resilient, adaptive, and human-centered organizations in the age of AI.
1. Embrace Regenerative Leadership Principles
Traditional leadership models that focus on efficiency and extraction are creating burned-out workforces and fragile organizations. As AI increases the temptation to pursue short-term gains, leaders must shift toward regenerative approaches that actively restore and enhance human, environmental, and technological resources.
Regenerative leadership means moving beyond sustainability to create systems that actually improve over time. This involves:
- Adopting systems thinking to see your organization as an interconnected ecosystem
- Prioritizing employee well-being alongside productivity
- Measuring impact beyond profit to include ethical AI usage and community impact
Companies like Patagonia, Interface, and Unilever have demonstrated that this approach doesn’t sacrifice performance. Rather, it enhances it through improved brand loyalty, employee engagement, long-term resilience, and higher growth.
The key is recognizing that AI should augment human potential, not exploit it. Leaders who build purpose-driven, people-centered strategies will create organizations that don’t just survive disruption but evolve through it.
Read more: The age of AI requires a new kind of leadership
2. Transform Your Organization, Not Just Your Training
Most organizations treat AI reskilling as a training problem. But AI will cause systemic change that affects virtually every function and workflow, so preparing employees for the coming revolution requires more than just teaching them how to use AI tools. Fundamentally, it requires organizational redesign—reimagining what work looks like in the AI age and transforming the organization to enable that work.
Success requires working in three interconnected dimensions:
- Rebuilding the infrastructure of work (providing the tools, data access, and workflow redesigns that make AI adoption possible)
- Redesigning interconnected roles across the organization simultaneously (because when one role changes, every connected role must shift)
- Cultivating a learning culture that prizes experimentation over perfection and treats failure as data rather than disgrace
Read more: What AI reskilling really requires
3. Master the Art of Leading AI-Augmented Teams
Throughout history, the core competence of leadership has always been guiding humans to achieve defined goals. But in the age of AI, leadership will take on a new meaning: leading hybrid teams in which humans and AI systems work side by side. Leaders must learn how to leverage the unique strengths of each while creating the context in which those unique strengths multiply by working in concert.
In order to succeed, leaders must:
- Personally engage with AI tools themselves, so they can understand and eventually model effective collaboration
- Cultivate clarity of purpose to discern what is actually worth doing in a world in which AI makes everything possible
- Become moral agents capable of navigating urgent ethical questions surrounding the use of AI
- Develop the enhanced emotional intelligence needed to guide teams through an often unsettling transition
Read more: The 7 secrets to successfully leading AI-augmented teams
4. Build a Balanced AI Portfolio: Moonshots and Mundane Wins
Successful AI transformation requires a well-balanced innovation portfolio—a deliberately diversified mix of initiatives spanning different risk levels and time horizons. Leaders must grapple with big-picture questions about industry transformation while simultaneously identifying tactical opportunities for near-term deployment. The immediately practical projects create the foundation—and funding—for more ambitious undertakings.
Evaluating which initiatives deserve resources requires systematic assessment across multiple dimensions: technical feasibility, true investment costs (including organizational attention), risk-reward balance, alignment with core purpose, and realistic timeframes. In our book Transcend and a companion piece in Harvard Business Review, we provide comprehensive tools—the OPEN and CARE frameworks—for this strategic evaluation and portfolio management.
Critical to all of this is engaged leadership at the C-suite level. CEOs cannot simply delegate AI projects to technical leaders. Instead, they must orchestrate the entire innovation portfolio to maintain strategic coherence.
Read more: Why your organization needs both AI moonshots and mundane wins
5. Protect Your Organizational Uniqueness
When everyone uses the same AI tools that have been trained on the same public data, outputs converge toward generic mediocrity. The quirks, the specific language, the unique ways of thinking that define your organization get smoothed into statistical averages.
Competitive advantage in 2026 will come from authentic difference—leaning into what makes you, you. You can do this by:
- Auditing your uniqueness: Identify what is different and distinctive about your organization.
- Creating proprietary datasets: Whenever possible, use internal data rather than generic datasets that everyone has access to.
- Establish AI-free zones: Maintain places where only humans get to operate.
- Adversarial prompting: Use AI to critically evaluate your conclusions rather than to confirm them.
The goal isn’t to reject AI but to use it strategically while preserving the distinctive elements that make your organization valuable.
Read more: How AI is creating a crisis of business sameness
6. Reinvent Middle Management for the AI Era
AI is eliminating traditional middle-management functions at an unprecedented rate. Gartner predicts that through 2026, 20% of organizations will use AI to flatten their structures, eliminating more than half of current middle-management positions. And by 2028 and 2029, AI-driven “jobs chaos” will force organizations to reconfigure, redesign, split, or fuse more than 32 million jobs every year.
There are significant efficiency gains to be made here. But organizations must be careful not to throw the baby out with the bathwater. Rather than simply trying to eliminate as much of middle management as possible, the task is to streamline it intelligently—middle management is still important, but its role must be fundamentally reimagined.
- Orchestrating AI-human collaboration: Understanding how to integrate AI with humans
- Serving as agents of change: Guiding organizations through continuous AI-driven disruption
- Coaching for a new era: Mentoring employees through constant reskilling and role evolution
To successfully navigate this transformation, organizations must reskill middle management to enable them to succeed in their reimagined roles.
Read more: How AI is killing (and reinventing) middle management
7. Know When to Change—and When to Hold Steady
In a profoundly uncertain world, and a business environment in which the only constant is disruption, the most critical leadership skill of all might well be discernment: the wisdom of knowing what to preserve and what to transform.
Effective leaders distinguish between their organization’s core identity (the “what” that they must not compromise) and their methods (the “how” that can be endlessly reimagined). In times of change, it is essential for leaders to be steadfast about the former while being completely flexible about the latter.
Read more: How to know when (and when not) to make a change
An Integrated Path
Not every organization will need to emphasize all seven priorities equally. A company with a strong and adaptable middle-management culture may focus elsewhere; one already grounded in regenerative principles can move quickly to portfolio building. The point is not to tackle everything at once but to recognize that these are the dimensions along which the AI transformation will play out, and to make deliberate choices about where to invest attention.
What leaders cannot afford is drift. Organizations that treat AI adoption as something that happens to them—tool by tool, team by team, without strategic intent—will find themselves shaped by the technology rather than shaping it. The difference between leading and following in 2026 will come down to whether these choices are made consciously or by default.
The leaders who prepare their organizations for 2026 by tackling these priorities won’t just survive the AI revolution. They’ll shape it—building organizations that are more resilient, more human, and more capable of creating lasting value in an age of unprecedented technological change.
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