10 Myths About the Future of Work That Are Holding Leaders Back

For C-suite executives committed to systemic organizational transformation in the Age of Ai & Perpetual Disruption

By Al Adamsen

Introduction

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CEOs and COOs to CHROs, CTOs, and Heads of Ai or Transformation, the unfortunate reality is that many still hold on to outdated beliefs — myths, really — about what the future of work will look like and how best to prepare.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.

Myth 1: Ai is transforming jobs at the same speed

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.

Myth 2: There’s a talent shortage everywhere

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.

Myth 3: Jobs = full-time roles inside big companies

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.

Myth 4: Ai will make better decisions for us

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. 

Myth 5: Going fast is always better

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.

Myth 6: More information means more clarity

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. .

Myth 7: Governance only needs light tweaking

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.

Myth 8: More Ai and tech = better productivity

Reality: Collaborative overload is a thing.  Overwhelm is real, and it’s not getting better in most organizations.  In fact, quite the opposite.  The resulting upward trend in employee angst is not only stemming from human-to-human interaction, it’s driven by unrelenting human-technology interaction.  From the numerous communication and collaboration platforms, to Ai actually assigning tasks and serving as “managers” of certain processes, in many organizations, expecting new tech adoption might be past a point of diminishing returns.  

What to Do: Evaluate your Ai and technology investments on the people within your organization, and not only from a productivity perspective.  Look into the impacts on workload, culture, well-being, and inclusion.  Recognize that humans are not limitless containers of work capacity, and that ultimately executive leadership teams need to continually optimize their organizations with this constraining truth as a key variable.  Sleep, nutrition, relationships, community, personal relationships, recreation, etc. are not going away, thus honor the human experience at work and plan realistically. 

Myth 9: Ai will eliminate work

Reality: Ai will eliminate certain jobs.  This is certain.  It’s already happened with certain job families, and it will happen with others in the near future.  In fact, Ai, robotic automation, and technology in general will continue to change the nature of work and jobs forever, and it will do so at an ever-faster pace.  This is something we, as leaders, not only have to get used to, we have to plan around it.  In addition, while Ai certainly makes doing certain work faster and better, in other cases it actually creates more work.  This often entails working with the Ai itself.  In other words, the work is shifting, it’s evolving, it's not disappearing.  Too few leadership teams recognize this, and they’re overestimating the efficiency gains of Ai over the near term, while underestimating the impact of Ai over the medium and longer terms.

What to Do: Don’t assume that Ai will automatically reduce or eliminate work, thus quickly increasing the capacity and/or productivity of existing employees.  This has to be studied.  People Analytics has a role to play here, so leverage that capability.  If you don’t have such a capability, build or buy it.  Not understanding how Ai will impact work itself, as well as the employee experience, means that decisions will be based on unnecessarily risky assumptions, guesses really.  This is irresponsible.  Get the necessary insight, consider it with a cross-functional team, and make wise, well informed decisions.

Myth 10: In the age of AI, employees are less important.

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..

What to Do Now

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:

• How to improve cross-functional collaboration and ongoing organizational adaptation
  
  
• Governance and decision-making processes that reduce risk and increase the probability that investments and other decisions reap the desired return
  
  
• How to formulate, measure, and manage Employee Experience Strategies over time in ways that sustain or enhance culture, well-being, and performance..
  
  
• How futuring helps leadership teams better understand how the very nature of work is shifting in their organizations and, in turn, how those shifts inform Work/Workforce Planning – workforce size, org design, learning strategy, process improvement, tech investment prioritization, etc.
  
  

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.

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