For C-suite executives committed to systemic organizational transformation in the Age of Ai & Perpetual Disruption
There will always be a “future of work.” That “future,” though, is getting closer, closer, and closer — and in many cases, it’s here. Unfortunately, not all leaders and leadership teams have accepted this reality. This is scary. There’s a cost to this. There’s a cost to individuals, teams, groups, and organizations and, in fact, to our society.
With this in mind, what follows are 10 myths about Ai and the future of work along with how I suggest leaders address them with more clarity and confidence. As you’ll see, this isn’t great news for leaders longing for the “good ol’ days.” It’s for leaders who recognize they need to go beyond legacy thinking and processes and evolve to future-ready ways of formulating, measuring, and managing workforce strategy — workforce now being humans and Ai.
From CEOs and COOs to CHROs, CTOs, and Heads of Ai or Transformation, the unfortunate reality is that many still hold on to outdated beliefs — myths, really — about what the future of work will look like and how best to prepare. These myths create blind spots. They lead to poor investment decisions, failed change efforts, angst among employees, as well as poor organizational performance.
These 10 myths are the first output of the work I’ve done to date on the Future of Work Project. Over the coming weeks, I’ll dive deeper into each one. For now, though, here’s a high-level look and a brief call to action for each.
Reality: The impact of Ai is uneven across job families. Some are being disrupted rapidly. Others, less so. This is what I refer to as the “dimensionality” of change. The impact of Ai on the work in certain job families needs to be understood; and this understanding will be achieved through data and analytics, yes, yet the most valuable insight will likely come from the perspectives and ideas of those actually doing the work. What a thought!
What to Do: Conduct a dimensional analysis of your workforce. Include voices from across the organization. Use job-family-level insights to guide learning investments and AI augmentation plans. Understand the nuances, don’t generalize, and honor human potential as well as our constraints.
Reality: This isn’t true. Some job families have critical shortages, as well as forecasted shortages. Other job families have surpluses, along with forecasted surpluses. Some will flip, while new job families will be created. For example, an AI Workflow Designer, AI Workflow Architect, or AI Solutions Architect. These “new” titles highlight roles that leverage Ai to design and implement workflows that actually do work in automated, sustainable ways. Many workers have the existing skills to fill such roles, while few have the 2 to 3 years of experience most employers blindly require.
What to Do: Workforce planning must account for work disruptions, identify the needed talent, and develop and recruit the talent with the relevant skills. In other words, Workforce Planning must go way beyond “skills”. It needs to clearly understand the work, how the work will get done, and plan and invest accordingly. This will require way more cross-functional collaboration that exists today. Map current and future skill demands and build strategies to help people transition from oversupplied to in-demand jobs.
Reality: Work is fracturing. Independent talent, project-based work, and flexible ecosystems are defining the modern organization. Many layoffs are happening, yet unemployment remains relatively low. Even so, many inside larger organizations are underemployed, not contributing close to their full economic potential; plus, the cost of housing, healthcare, transportation, food, and essential services keeps climbing relative to wages. As such, many across the age spectrum are looking to work as a consultant/contractor or join a small company.
What to Do: Workforce Planning must include building partnerships and ecosystems with independent consultants, contractors, and small-firms. Embracing this as an enduring trend will provide more options to get work done. It will provide financial and organizational "elasticity", a huge asset in midst of ongoing change and uncertainty. As such, reimagine the onboarding and integration of such individuals and firms, and have inclusion programs include these people. The fractal organization is here.
Reality: Good information – information that’s timely, relevant, and actionable – requires time and effort to create and, once created, it takes time and effort to consider. Meanwhile, there’s a flood of information that’s being created by direct reports, partners, influencers, and, of course, Ai, all of which is competing for your attention. Added to this, good information does not mean good decisions. We must check our biases, understand the processes by which we’re making decisions, both individually and part of a leadership team, and, in turn, ensure we’re making the best decision possible for sustained organizational performance.
What to Do: Good information and decision-making takes much more than good insight and data-based stories. It takes getting the right people “in the room” with the time and space to assess, explore, consider scenarios, ideate, etc. While Ai can certainly help surface timely, relevant, and actionable information, it often does so in a disconcertingly positive way. Leaders, thus, must ensure their individual and leadership team decision-making processes instill cross-functional collaboration that optimizes human-tech and human-human experiences over time.
Reality: Leaders have uncommon pressure to meet financial targets over the near term. This often drives CEO’s, CFO’s, and other executives to place big bets on the supposed productivity gains of Ai, robotic automation, and other technological innovations. Sometimes this is prudent. Other times it’s quite the opposite. Decisions that are too quick, with little understanding of how the subsequent change will impact employees, customers, and related processes. This often proves costly, sometimes very costly. Rapid change without dependable foundations is a recipe for widespread uncertainty, angst, and disengagement.
What to Do: Commit to slowing down and creating the time and space to consider goals, information, strategies, options, impacts, etc. Doing so will prove to be an organizational asset, a competitive advantage, and a sign of a lucid, confidence-inspiring leadership team. Especially in the midst of non-stop external change, leadership teams, and the employees for which they’re responsible, need dependable “stones to stand on” – the values, frameworks, communications, governance, and decision-making processes that they all recognize and that inspire ongoing confidence.
Reality: We have a longing for certainty. It doesn’t exist, not in organizations anyway, or in the environments in which they exist. Organizations are open-systems. They are impacted by external factors that are out of the control of leaders. This is disconcerting, yes, yet it’s reality. As such, there’s a seeking of more information, information that’s more timely, relevant, and actionable. This is a normal desire, and the good news is that Ai enables this. The bad news: Ai enables this. Neurobiologically, as humans, we’re not built to consume information at the quantity and pace that it’s coming at us.
What to Do: Individually, consciously manage your “digital diet”, especially including information coming directly from Ai. The depth of information seeking is literally never-ending. As such, knowing when “enough is enough” is key. Collectively, seek the perspectives and ideas of other functional leaders, subject matter experts (SMEs), employees, and others. This will certainly help surface the information that’s most timely, relevant, and actionable.
Reality: Most governance and decision-making processes are outdated, and most leadership teams are operating like they did five, ten, or even 20 years ago. Unnecessary risks are thus being incurred, and the associated uncertainty affects all stakeholders. For example, investment decisions in Ai and other technologies are often driven out of IT, prioritizing how solutions will fit into an existing tech stack. All fine and good. What is often lacking is a similar understanding of how those technologies will impact employees, culture, workforce size, org design, location strategy, customers, legal risk, ethics, etc. Complex, yes, yet dealing with complexity is the role of executive leadership teams. They thus need to function expertly in terms of setting the vision and, in turn, formulating, measuring, and managing the strategy that will achieve that vision. Unfortunately, most have not unshackled from legacy processes.
What to Do: CEO’s and Boards need to update, if not outright reimagine, their governance models and leader decision-making processes. Make them so that they will endure over time. Make futuring and scenario planning strong institutional muscles. Make them uncommonly cross-functional, with crystal-clear clarity on the responsible decision-makers, the information to be considered, for what purpose, at what frequency, etc. Redesign for speed, adaptability, and sustainability.
Reality:
Ai often creates new work – training systems, building workflows, monitoring outputs, adapting processes. Layered on top of today’s flood of tools and communication channels, this leaves many workers facing the opposite of what was promised: collaborative overload, constant context switching, and rising overwhelm, burnout, and angst. The assumption that Ai will automatically free human capacity is dangerously simplistic. Much of the so-called “freed-up time” is quickly consumed by new forms of human-to-human and human-to-technology work. Real capacity and productivity gains are neither automatic nor uniform. They thus require deliberate strategy, careful measurement, and ongoing iteration.
What to Do:
Executives must rigorously evaluate technology adoption not only for its impact on productivity, but also on workload, culture, and well-being. Assume that work will shift, not disappear, and that employees’ capacity is finite. Leverage People Analytics (or build that capability) to study how Ai and tech are truly shaping work and the employee experience. Use those insights to guide cross-functional decision-making. Most importantly, design for the whole human: recognize that sleep, health, relationships, and community are not optional—they are constraints on organizational performance. Leaders who plan with these truths in mind will avoid the trap of overestimating short-term efficiency while underestimating long-term disruption.
Reality: While no leader I’m aware of, who’s currently in role anyways, is openly stating that employees are less important, the actions of many leaders and leadership teams clearly suggest that their employees are disposable or at least easily replaceable. While workforce size is understandably influenced by financial realities, it’s also a philosophical decision. It reflects the values of the leadership team, the Board, and, of course, the CEO. Many choose to minimize the number of employees, while other leaders see employees as uniquely valuable assets, true sources of competitive advantage that continually enhance their capabilities, creativity, and relationship equity.
What to Do: Value relationship dynamics, culture, and employee experiences. Measure them. Understand them. Nurture them. As Ai and tech commoditize a lot of processes, the uniqueness of an organization is going to be rooted in how its employees – the humans who’ve chosen that particularly company – communicate, collaborate, create, make decisions, and generally get things done; and this is true of external interactions as much as internal interactions. Acknowledge when Ai and tech augment humans versus when humans augment the Ai and tech. The latter is when humans provide the specialness and own the output. The former is when the Ai/tech does the work prompted by the human or autonomously. In the end, recognize that employees are truly more important than ever.
Reality
Far from being overhyped, Ai is actually underhyped—and, more importantly, under-acted upon. Most individuals, organizations, and even societies are not approaching Ai with the seriousness, clarity, or urgency it demands. Instead, there’s a dangerous assumption that “things will just work out”. Yet, history suggests otherwise: unchecked disruption rarely resolves itself without intentional action. For executives, the default playbook has been to cut human costs in the name of shareholder value, leaving the human impact of Ai adoption --layoffs, burnout, and social dislocation -- unaddressed. Meanwhile, individuals are now competing not only against global talent but against Ai itself, creating an unprecedented pressure to learn, adapt, and create value at an inhuman pace. Ignoring these realities is not just shortsighted, it’s irresponsible.
What to Do
For individuals, the imperative is continuous learning and reinvention. Degrees or certifications are no longer endpoints but waypoints on an ongoing journey. To stay employable, people must cultivate the ability to learn quickly, apply knowledge at scale, and set personal boundaries to preserve their well-being in an increasingly fluid labor market. For executives, the challenge is to move beyond rhetoric about “people being our greatest assets” and design real workforce strategies that integrate Ai, technology, process, and people. This means involving employees in the co-creation of new workflows, investing in their development even if they eventually move on, and embracing workforce planning as a dynamic, iterative process. By doing so, organizations not only accelerate Ai adoption but also build trust, brand equity, and long-term resilience. The alternative -- clinging to outdated, zero-sum approaches -- will harm employees, customers, and shareholders alike.
If you’re a business leader, especially in a mid-sized company (500–5,000 employees), these myths just don’t represent risk, they represent a call to reinvite your leadership teams ways of doing things to ensure your organization remains future-ready over time. Yes, the future is complex. Yes, it’s exhausting to consider all the variables, all the scenarios. Even so, good governance and efficient and effective leader decision-making processes can be achieved. It will, though, take an uncommon level of open-mindedness, creativity, courage, and commitment. You up for it?
More to come on the themes associated with each of these myths as well as:
Over the next several weeks I’ll be unpacking each of these myths with stories, research, and actionable insights, all meant to help you and your fellow executives lead with more clarity and confidence.
Follow along. Share with your peers and colleagues, and let’s consciously create a better future for all stakeholders, especially us humans.
To learn how to assess your organization’s adaptive readiness and, in turn, build executive decision-making processes rooted in timely, relevant, and actionable insight, follow and connect with me here on LinkedIn. Also, be sure to subscribe to the Future of Work Advisors Newsletter.