For leaders managing change today – which is all leaders – the job can more often feel like being a pilot flying through a thunderstorm. While trying to build a plane.
It requires navigating a relentless mix of challenges: economic pressure and hiring freezes. Political instability and global uncertainty. Constant restructuring and transformation fatigue. And of course, the rapid proliferation of AI technology.
And through it all, the message from the top is often the same: Do more with less. And, also: yesterday.
This isn’t just a tough quarter, it’s a new normal. The pace is unrelenting, the stakes are high, and the path forward is rarely clear. Teams are expected to move fast, stay resilient, and somehow remain inspired while the ground shifts beneath them.
Welcome to The Squeeze, where uncertainty and speed collide, and traditional change management starts to fall apart.
Why the Old Playbook Doesn’t Work Anymore
Most change models were built for a world that moved slower and made more sense. They assume you can map out a neat journey from “current state” to “future state,” with clear milestones and predictable outcomes.
But today’s reality is messier. Change is emotional, nonlinear, and deeply human. Leaders are making decisions with incomplete information, while their teams are still recovering from the last three transformations.
As one executive put it: “In a world where uncertainty is the only certainty, we’re still using change management like it’s a roadmap for a terrain that never shifts.”
Not All Change is Created Equal: Four Scenarios, Four Strategies
If change is messy, then the way we manage it should be flexible. Borrowing from high-stakes, mission critical environments such as space travel, it’s more important than ever to have a defined destination in mind, and be adaptable long the way. Not every mission – or transformation – moves at the same pace or carries the same level of uncertainty, so why treat them all the same?
Picture a matrix with two axes: Speed of change vs. Level of uncertainty.
Depending on where your initiative lands, you’ll need a different set of tools, priorities, and leadership behaviors. Here’s how that plays out across four common scenarios:
1. Real-Time Disruption (High Speed, High Uncertainty)
Think: AI adoption, crisis response. You’re building the plane mid-flight. Focus on piloting change, coaching in the moment, and engaging key employee groups.
Example: To drive gen AI adoption a global pharma company ditched the traditional From > To approach and focused on fostering a growth mindset using immersive gamified learning —resulting in a 63% increase in engagement.
2. Strategic Market Moves (Low Speed, High Uncertainty)
Think: M&A, long-term strategic shifts. Here, resilience is key. Help teams manage uncertainty fatigue, stay grounded in the present, and build hope for the future.
Example: A luxury retailer launching a new strategy and transformation overcame employee uncertainty and built leader accountability by creating space for emotional processing, leadership development, and community problem-solving, contributing to a rise in share price.
3. Operating Pivots (High Speed, Low Uncertainty)
Think: targeted org changes with clear direction. Use change sprints and rapid communication. Park the long-term plan until you’ve gathered feedback.
Example: A pharma medical team that needed to make change happen fast focused on the initial plan and cascade as a ‘sprint’ and used employee feedback to shape the ongoing change and communications plan, enabling real-time responsiveness and agility.
4. Enterprise Rollouts (Low Speed, Low Uncertainty)
Think: system implementations, process changes. These are more predictable and benefit from traditional change management approaches focused on coordination and alignment.
Example: A professional services company leveraged a traditional multi-phased approach to roll-out an enterprise business strategy, improving understanding of the shift and future expectations by team and region.
Understanding the type of change you’re facing goes beyond strategic exercise to leadership imperative. When you match your approach to the nature of the transformation, you reduce friction, build trust, and increase the odds of success.
What’s Next for Change Leaders?
The world isn’t getting simpler. AI will keep evolving. Markets will keep shifting. And teams will keep looking to their leaders for clarity in the chaos.
You can keep hoping for a clean, linear roadmap, or you can embrace the mess and lead with agility, empathy, and impact.
As Astronaut Sunita Williams said: ‘adaptability is essential for survival and success.’
At United Minds, we’re building tools that help leaders thrive in uncertainty—not despite it, but because of it.
Organizations that do it right can compress what might take years into months.
Every technological revolution has its awkward adolescence. We’re living through AI’s right now. Recent research from Stanford and BetterUp has given this moment a name: “workslop.” It’s the flood of hastily AI-generated content that clogs inboxes, clutters presentations, and quietly erodes productivity. The email that reads like it was written by a committee of robots. The strategy document with oddly formal phrasing and zero original insight. The presentation deck that says nothing new.
If this sounds familiar, you’re not imagining it. And if you’re a manager watching your team’s output simultaneously increase in volume and decrease in quality, you’re not alone.
But here’s what history teaches us: this phase is predictable, necessary, and temporary. The question isn’t whether we’ll move through it. It’s how quickly we can get to the other side.
WHY WORKSLOP HAPPENS
When personal computers arrived in offices, workers treated them as expensive typewriters. When the internet became ubiquitous, we spent years learning that you can walk 10 feet to talk to someone instead of firing off another email. Each time, we mistook the tool for the solution.
We’re making the same mistake with AI. Only faster, and at greater scale.
The core problem is one of delegation versus collaboration. AI will deliver increased speed and efficiency, but most organizations have accidentally encouraged their people to treat it as something to offload to rather than something to work with. An associate generates a client memo with Claude and sends it along, complete with the telltale “AI can make mistakes, please double-check” footer still attached. A manager asks ChatGPT to write a strategy document and forwards it without adding context, nuance, or judgment.
This isn’t a technology problem. It’s a mindset problem that technology has exposed.
When content creation becomes effortless, the cognitive work of thinking deeply becomes optional. And when it becomes optional, people can opt out. What researchers are calling “cognitive atrophy” is really just a gradual disconnection from the thinking process itself. We’re delegating not just the execution, but the strategy. AI will get you 70% of the way there, but someone still needs to own that final 30%, and right now it seems some people are checking out before the finish line.
THE WAY THROUGH
The good news? Workslop isn’t a crisis. It’s a phase. Organizations that recognize it as such can compress what might take years into months.
Start by redefining what you measure. The drive to do more with less can create pressure to crank out more work in the same time, with AI as the productivity multiplier. But leaders need to resist the assumption that one person plus AI should equal twice the output. If you’re still evaluating employees primarily on volume, you’re incentivizing exactly the behavior you don’t want. Prose and code generation are now commoditized. What matters is the quality of thinking that directs these tools. In your performance management processes, assess people on their judgment, their ability to steer AI effectively, and their capacity to iterate toward genuinely excellent outcomes.
Draw bright lines. Leaders need to align on the AI vision, the guardrails, and how they’ll hold people accountable. Establish explicit standards for what constitutes acceptable AI-assisted work. Some organizations are implementing simple rules: AI-generated content must be marked during internal review. Client-facing materials must demonstrate clear human value-add. Any work bearing AI watermarks or disclaimers gets automatically returned. These aren’t punitive measures. They’re cultural signals about what professionalism means in an AI-augmented workplace. Without mutual commitment from leaders to embed these standards, the bright lines blur.
Embrace experimentation, but guide it. The workslop phase exists because people need room to learn, and that requires a growth mindset, not a fixed one. Risk aversion kills experimentation. Moving through this phase means reframing failure as data, celebrating what you learn from missteps, and managers modeling vulnerability about their own learning curve. Managers can accelerate this shift by tapping into people’s intrinsic motivation for mastery. But experimentation without feedback loops doesn’t create change. So have forums where teams share what’s working and what isn’t, and celebrate the wins and the learnings of human-AI collaboration.
Learn from unexpected sources. Universities faced the workslop crisis before corporations did. Many have developed sophisticated approaches to maintaining rigor while embracing AI tools. They’ve created assignments that inherently require human judgment, implemented systems that flag low-quality automated work, and redesigned evaluation criteria to emphasize critical thinking over production. These aren’t perfect solutions, but they’re battle-tested ones that can translate to corporate contexts.
Resist the delegation instinct. The most important cultural shift is also the simplest: don’t treat AI as your copilot. Treat it like a student, and you’re the teacher. This reframes the entire relationship. You’re not handing off work. You’re responsible for what that student produces, which means staying engaged in the iterative process, using tools to enhance rather than replace human judgment, and taking full ownership of outputs regardless of how they were generated. Organizations that successfully embed this mindset move through the workslop phase measurably faster. The upside? New Stanford research tells us that employees trust AI more when they can see it as a collaborator, not a closed system.
AN UNEXPECTED OPPORTUNITY
Here’s what makes this moment genuinely unique: the traditional corporate hierarchy of expertise has temporarily inverted. Right now, a brilliant 22-year-old who knows how to work with AI tools can create more value than their manager who doesn’t. This isn’t a threat to experienced leaders. It’s an opportunity. Junior employees have rare insight into what actually works, and smart managers are creating channels for those employees to lead the way forward. You’re not being replaced. You’re being offered a shortcut to expertise that would otherwise take years to develop.
The companies that emerge strongest from the workslop phase won’t be those that restricted AI use or pretended the problems didn’t exist. They’ll be the ones that acknowledged the awkwardness, called it out, learned from it quickly, and built cultures where humans and AI genuinely complement each other. Experience shows us that the most critical cultural factors that will shape the success of AI include the degree of autonomy of teams to shape workflows, the measures and controls put in place, and what gets rewarded and recognized.
We’re in the messy middle of the AI adoption curve. Workslop is almost certainly happening in your organization right now. The only question is whether you’re managing the transition or hoping it resolves itself.
History suggests which approach works better.
This article was originally published by Fast Company.
Leadership must be prepared to change
Picture this: A seasoned Fortune 500 CEO sits across from their board, confidently presenting a five-year strategic plan built on decades of operational expertise and financial acumen.
The projections are precise, the market analysis thorough, and the execution timeline methodical.
Six months later, an AI system produces the same analysis in six minutes—with greater accuracy and deeper insights than the executive team spent months developing.
This isn’t a hypothetical scenario. It’s happening right now in boardrooms around the globe, and it’s fundamentally reshaping what it means to lead.
The artificial intelligence revolution is transforming the traditional executive competencies that have long dominated corporate leadership. And for organizations searching for their next CEO, this means the next five years will look dramatically different from the last twenty.
THE END OF THE 20TH-CENTURY EXECUTIVE PROFILE
This isn’t the first time a technological revolution demanded new kinds of leadership. The industrial age created the management structures we still largely operate within today.
Think about the typical CEO profile that’s dominated boardrooms since the post-World War II era. The profile is a product of management as a discipline, McKinsey-style analytical rigor, and the corporate structures that emerged to manage large-scale, global enterprises with increased efficiency and precision.
Often, they are consulting-trained CFOs or operations leaders who methodically climbed hierarchies designed for predictability. They’ve accumulated technical expertise, mastered financial engineering and shareholder relations, and proven themselves through increasingly complex operational challenges.
But here’s the problem. Traditional competencies are increasingly automatable. AI can handle much of the heavy lifting that once distinguished senior executives in terms of financial and strategic acumen.
What remains distinctly human—and therefore invaluable—are historically undervalued skills in corporate leadership.
As someone who spends my days helping Fortune 500 companies navigate organizational transformation, I’m witnessing this shift in real time.
THE NEW LEADERSHIP COMPETENCIES THAT MATTER
The executives who will succeed in an AI-augmented world share characteristics that are remarkably different from those of yesterday’s corporate stars. Leaders in the new era will be uniquely skilled at:
Learning and teaching: As change accelerates, executives will need to be both learners (expressing curiosity about new systems and their potential) and teachers (helping their teams understand new ways of thinking and working). This is especially relevant in light of new MIT research, which found that 95% of AI pilot projects failed to deliver any discernible financial savings or profit uplift due to challenges such as workflow misalignment, inadequate integration, leadership or culture barriers, and other organizational issues.
Emotional intelligence and empathy: Leaders must excel at the distinctly human work of understanding, motivating, and connecting with people. They’ll use skills like cognitive empathy, or the ability to understand another person’s emotional state from an intellectual perspective. It’s a unique combination of thinking and feeling.
Comfort with ambiguity: Unlike previous eras, where leaders could paint clear visions of the future, today’s executives must guide organizations through constant uncertainty. The ability to work in gray zones, make decisions with incomplete information, and help teams navigate ambiguity becomes paramount.
Ethical decision-making: As AI assumes more decision-making responsibilities and the ethical implications of human-AI systems become increasingly complex, leaders will need increasingly sophisticated moral compasses.
Change agility and resilience: The half-life of business strategies continues to shrink. Leaders must adapt to continuous transformation rather than episodic change management. A growth mindset enables them to fail and learn quickly—for both themselves and their organizations.
THE PIPELINE CHALLENGE
Boards are beginning to recognize they need different kinds of leaders, but we may be creating a talent pipeline crisis.
Today’s emerging leaders will grow up in an AI-augmented workplace. They’re outsourcing more critical thinking to technology, spending less time in the trenches making tough judgment calls, and navigating fewer firsthand, real-world experiences that traditionally build executive competency.
It’s a classic catch-22: We need leaders who understand AI but also have deep human judgment. The very prevalence of AI may be preventing the next generation from developing that judgment.
The implications for executive recruitment are profound. First, boards must expand candidate pools beyond traditional executive development paths. The next great CEO might not have climbed a conventional corporate ladder but started their own AI-enabled company, led transformation initiatives across industries, or developed their leadership skills in entirely new contexts.
Second, boards should reconsider the premium placed on industry experience versus change leadership capabilities. In rapidly evolving sectors, deep industry knowledge may be less valuable than the ability to learn quickly, adapt continuously, and guide organizations through fundamental shifts.
Third, assessment processes need to evolve. Traditional executive interviews and reference checks may miss the competencies that matter most in AI-era leadership. Boards might need to evaluate candidates’ ability to work in ambiguous situations, their track record of bringing people along during transformation, and their approach to ethical decision-making in complex scenarios.
THE BROADER ORGANIZATIONAL QUESTION
All of this raises a fundamental question about the future of corporate structure itself. As AI enhances certain types of coordination and production, we may see a continued disaggregation of traditional corporate forms.
If that happens, tomorrow’s leaders will need to excel not only at running existing organizations, but also at reimagining how work is organized in the first place.
The leaders and leadership teams who will thrive won’t necessarily be the ones who’ve mastered traditional corporate roles. They’ll be the leaders who can operate in gray zones, guide teams through radical uncertainty, and maintain their humanity while leveraging unprecedented technological power.
Kate Bullinger is CEO of United Minds.
