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.

Q3 2025 Insights

This past quarter, generative AI accelerated its integration into business and society. Companies moved from pilots to enterprise-wide adoption, regulators began stepping up oversight and the workforce experienced rapid disruption. Those shifts were mirrored in the media: the tone moved from wide-eyed optimism to pragmatic consideration, so while coverage volume remained high, it increasingly scrutinized how AI should be used rather than if it should be used. These shifts underscore the need for leaders to:

This report outlines five key takeaways for executives and three signals to watch heading into Q4 2025, supported by practical recommendations.

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.

This article was originally published by Fast Company.

The multibillion-dollar consulting industry built on helping companies manage change is about to get very uncomfortable.

Change management is a multibillion-dollar industry built on the fundamental claim that most people dislike change, and that someone needs to manage that resistance. 

But after decades of organizational theories and billions in consulting fees, the industry does not work as promised: change management projects have a failure rate of around 70%. There’s a reason no one asked McKinsey or any other leading consulting firm to run DOGE.  

As enterprises large and small grapple with the wholesale transformation that will be wrought by the rise of artificial intelligence, it’s time to face an uncomfortable truth: Change, especially today, doesn’t happen in neat phases. It’s cyclical, unpredictable, and requires constant adaptation.  

The Old Model Never Worked–But It Especially Doesn’t Now  

Traditional change management follows a predictable model. Under investor scrutiny—or to avoid it—the CEO announces a transformation is coming. Consultants conduct surveys, present slides at workshops, and create communication plans to deal with signs of revolt. Success gets measured by stakeholder-focused metrics like “adoption rates,” and KPIs like “number of training programs deployed.” It’s an industry that prioritizes rationality at the expense of inspiration and serendipity. 

AI will be the force that kills this episodic approach. First, the tech is already moving too quickly for rigid approaches to be relevant. Second, AI requires significant amounts of training and customization to be effective in most organizations, rendering a “one-size-fits-most” approach obsolete. And finally, the human side of AI-driven change is more complicated than a standard reorganization—because AI anxiety strikes at the heart of what is human, and what sort of careers we and our children will have.  

In our recent work inside companies that are adopting new AI tools and workflows, we’ve seen the potential for a new way of working. Instead of a traditional change management approach, smart leaders today are understanding—and embracing—that change in the era of AI is often organically driven by shifts brought about by AI eureka moments. Competitive advantage is built not by how quickly you move humans through a change program, but how seamlessly your organization’s source code—the unique combination of people, process, and technology—rewrites itself in real time.

A Tale of Two Companies

Consider a recent tale of two companies.  

First, fintech company Klarna’s recent initiative to automate its customer service operations using generative AI. The company publicly claimed that its AI tools were performing the work of 700 full-time agents, leading to a dramatic reduction in hiring and headcount. The rollout was managed through a centralized, top-down approach: executive-led messaging, internal dashboards to track AI performance, and a focus on cost savings and productivity metrics. But the transition sparked internal unease and external criticism, and Klarna quietly began rehiring human agents within the year. The AI may have delivered efficiency on paper, but the rigid implementation and lack of human-centered change management eroded trust, both inside and outside the company. 

Contrast that with one of our clients—a multinational pharmaceutical organization that took a radically different approach. Rather than relying on static KPIs and sequential rollouts, they used AI to surface real-time insights from employee sentiment, social media behavior, and internal feedback loops. These insights continue to inform tailored interventions across roles and geographies. AI-powered chatbots enable employees to access personalized resources on demand, while leaders use behavioral analytics to trigger timely nudges and adapt strategies instantly. The result has been a more agile, inclusive transformation—where change has been continuously shaped by how employees are actually working.  

How Organizations Can Stay Ahead 

In this new world, “best practice” changes from week to week. But the most important trends we see in recent, successful transformations are:  

First, build a nonlinear approach. When it comes to generative and agentic AI, you often don’t know your best use cases until you experiment. Embrace the 3-D problem solving that comes with transformation by moving to organized but flexible processes that account for two-way feedback.  

Second, create pilots. Understand that new processes, technologies, and workflows will work differently for each organization and team. Select specific organizational areas for focused experimentation and training. Give them deadlines and establish feedback loops between pilot participants and the transformation team. Then, scale successful approaches across the organization using champions as advocates for the technology and its impact.  

Third, work to understand and activate teams with precision. Identify specific employee categories to play a role in championing change. Every organization has a group of early adopters—the “weekend warriors” who explore AI on their own time. And every organization also has laggards—those who will require structured protocols and personalized training plans to implement new systems. Focus your communications—and your expectations—by identifying each group and understanding the different needs it requires.  

Finally, empower leaders. Measure success not by who attended the meeting or did the training, but by who’s actually creating new pathways in process or technology. Encourage those leaders, from the CEO down, to show how they use AI tools, and arm with appropriate nudges for staff.  

According to the Boston Consulting Group, the small minority of companies already operating at this level are realizing 1.5× revenue growth, 1.6× shareholder returns, and 1.4× ROI. 

The goal is improving organizational metabolism so your organization stays healthy, instead of contracting a disease that needs treatment. The business model for change management consulting may shift to something far more organic: Enabling leaders to role model and guide, designing teams built for experimentation and imbuing organizational culture with a growth mindset. 

Adaptability counts most

Corporate America rewards risk-taking and stories about explosive growth, rapid innovation, and bottom-line-enhancing layoffs. But it’s adaptability that will count most in the AI era, and continuous improvement is what will deliver it. 

Organizations that continue to rely on traditional change management consultancies are not just wasting money—they’re actively handicapping their ability to compete in an increasingly dynamic business environment. Consultants can either change— the irony!— or go down with their ship.  

This article was originally published by Fast Company.

When organisations announce a new strategic direction, formal communications such as town halls, newsletters, and videos are expected. Yet, beyond traditional communication methods, what actions can senior leaders take to demonstrate commitment to new ways of thinking and working?  

The power of symbolic acts 

 ‘Symbolic acts’ are a powerful but often overlooked tool for shifting culture and moving the needle on a new vision or strategy.  

A ‘symbolic act’ is a highly visible action or decision leaders take to role model organisational changes.  

Symbolic acts in action 

What do symbolic acts look like?  

Consider an organisation that is refocusing on customer innovation, intent on being first to market with best-in-class products. Historically innovation has been stifled by red tape and a fear of failure.  

A senior leader could become a role model by regularly sharing personal stories of risk taking and failure, promoting a growth mindset and normalising calculated risk-taking. Alternatively, the leader may choose to speak last in every meeting, encouraging input from employees at more junior levels.  

The symbolic act should resonate with employees and provide a clear signal of a change in direction. Consider Steve Jobs’ decision to discontinue 70% of product lines upon returning as interim CEO of Apple in 1997. This act clearly communicated that Apple would focus on its core offering of personal computers. 

By “walking the talk” and executing symbolic acts that are meaningful and noticeable to employees, leaders demonstrate their commitment to thinking and acting differently. 

Some additional examples include:

Symbolic acts across the organisation 

Symbolic acts aren’t just for senior leaders. It is impactful when middle managers and influential frontline employees adopt symbolic acts.  

Symbolic acts can be individualized or collective. For example, employees could start meetings with a ‘value share’, or an entire leadership team might collectively adopt casual office attire. 

Symbolic acts are an effective way to deepen transformation efforts by positioning leaders to visibly and authentically role model the changes they want to see in the business. By incorporating symbolic acts into engagement strategies, leaders can deepen the impact of transformations. 

Have you employed symbolic acts in organizational changes? Or do you have alternative strategies for effective role modeling and engagement? I would love to hear your thoughts in the comments section. 

The role of the middle manager has already evolved beyond recognition

Cast yourself back to the early 1990s.  No internet, no email, no instant messaging or video calls.  Communication was slow and structured which reflected the hierarchical, top-down nature of many organisations.  The average middle manager acted as an administrative gatekeeper, controlling the bridge of communication and flow of information between executives, support functions and operational staff.

Technological advancement of the last 30 years has evolved the middle manager role beyond recognition. Now the middle layer takes on an increasingly strategic role in what have become much flatter organisational structures. Responsible for managing up, down and across. Middle managers are the driving force of business. They are the engine of performance and they have become responsible for everything from influencing and shaping the vision to communicating and executing the strategy.

It’s not surprising that as these roles have become less bureaucratic and administrative, ‘softer’ skills have become more important. We all know the best managers are the ones that can connect with you on a personal level and have the meaningful conversations that create a sense of belonging first and foremost, ignite commitment and drive performance. In other words – to be successful, middle managers must now develop their people skills in empathy, active listening, and influencing others.

But we’ve still not got it right. Middle managers cope with high stress and often little thanks.

We are not making the middle manager role easy though, when it’s never ‘enough’ and lacking in meaningful reward. Organisations are increasingly asking middle managers to do more with less as businesses continue to tighten belts and squeeze out efficiency. Middle manager workloads are high, expectations are higher, and both the external and internal operating contexts are often uncertain, changing and ambiguous. While we know that ‘ruthless prioritisation’ is the answer, it’s almost impossible to achieve. It’s no surprise that a global pandemic has forced many to re-consider their priorities, lifestyle and options, and that younger generations are shying away from progression into middle management in search of roles that offer less stress, more meaning.

The advancement of AI and agentic workforces could change everything. Again.

And what’s more we are facing into a very real and imminent scenario of being on the brink of another major shift in the way our workplaces operate. As AI capabilities continue to advance and business continues to de-layer organisational structures, middle managers may once again find themselves in a very different environment altogether. One where they are managing a broader, more cross-functional set of responsibilities, a mix of human and AI agentic teams, and perhaps in many cases even less resource.

Beyond the initial reaction of fear, this future holds an exciting opportunity for middle managers to make higher-value, more meaningful contributions to the business performance. But doing so relies on an enhanced set of capabilities over the ones needed today.

To succeed in the future middle managers will need different skills and capabilities.

If business wants the middle layer of their organisation to succeed, they must look to upskill and equip them in the essential skills of the future. This will become less about being the most applauded subject matter expert operating with the highest technical proficiency. Going forwards, leadership development must focus on a combination of:

But leadership development alone is not enough. It’s time to reinvent middle management and strengthen leadership at the essential core.

And yet, even if capability building will become an important lever, it’s not enough by itself. We know that even the very best leadership development courses don’t change business by themselves. It will take more than a 2-hour, 2-day, 2-week training to be able to shift what it means to be a middle manager, to embed a shift in ways of working that allows for higher-value contribution, to ensure that executives are playing a role in empowering this layer to step up. It will take a reinvention of middle management to strengthen leadership at the essential core.

The reinvention of middle management requires a systemic approach.

If we really want to shift the role of middle managers for the future, it will take a systemic approach that reviews organisational culture holistically, going beyond the mission statement and values.

It’s about reviewing the embedded ways of working in the organisation that come to characterise a middle manger’s day. The way individuals and teams collaborate and communicate with each other; the way work is managed and performance is measured; and the way the organisation learns and adapts to evolve. These, often unspoken, rules about how things get done must support the reinvention of middle management if we want to strengthen leadership at this level.

And this may mean reviewing the operating model, structure and accountabilities; how the business strategy is cascaded to inform day to day activities; the organisational symbols, rituals and stories as well as the leadership style – how are these factors contributing to the role of middle managers, and how do we shift them in support of the reinvention?

And if we get it right, middle management will become the pipeline of emerging leaders that it ought and deserves to be.

If it’s time for a reinvention of middle management in your organisation let’s talk about how United Minds can partner with you on:

Change & transformation: supporting the adoption and embedding of AI-driven ways of working.

Leadership development and capability building: in the competencies your leaders need to for today, as well as the technological, emotional and cognitive competencies they’ll need to lead humans in a future AI-enabled workplace.

Organisational redesign: designing sustainable structures, and future-proofed job roles with realistic expectations that are adaptable to technological, AI advancement.

Organisational culture: shifting expectations, mindsets and beliefs, as well as the ways of working, activities, and processes that either enable or prohibit progress towards the strategy.