In the EY Megatrends 2026 and beyond report, there is a reference to research where the authors warned: “The concentration of job losses among entry-level positions disrupts traditional skill development pathways.”

When I read this, it stayed with me.

This was also something we discussed in our AI Powered Decisions class —not only in terms of new graduate hiring, but also its effect on leadership pipeline risk. It also raised questions about middle management, as we imagined less experienced employees with strong AI collaboration skills potentially surpassing their managers.

But for me, this topic feels quite personal.

Traditional entry-level roles—like the one I followed when I joined Andersen right after college—were not just jobs. They were almost like schools where you learn how to think, how to navigate uncertainty, how to build judgment. Personally, that early experience shaped much more than my technical skills.

And that’s why this shift feels both exciting and unsettling.

Today, because skills and business needs are changing so quickly organizations are being forced to rethink work as a dynamic system rather than a sequence of fixed jobs. Harvard Business Impact insight on The Fluid Future of Work describes role evolution as increasingly nonlinear, with responsibilities fragmenting, fusing, or disappearing. In this context, learning and development is also shifting—from closing existing skills gaps to working based on future capability needs.

As I think more about it, I realize that we are not just introducing AI into our companies—we are redesigning what an organization actually is.

And the question becomes “how”.

• How do we prevent leadership pipeline risk and redesign employee experience in the “superfluid enterprise” we are becoming?
• If AI is changing the entry points of the career ladder, do we still design organizations around ladders at all?
• What if traditional succession planning matters less and capability continuity matters more?
• What does succession mean when roles evolve faster than succession pipelines?

Organizations still need to intentionally develop next-generation leaders, but the old model—“hire juniors, move up the ladder, identify the next step based on title”—may be weakening. AI is changing the nature of work, the pace of skill depreciation, and even the structure of organizations themselves.

World Economic Forum highlights rising demand for both AI-related skills and human capabilities such as creative thinking, resilience, flexibility, talent management, teaching, and mentoring.

EY also points leaders toward reimagining talent and operating models rather than trying to preserve legacy structures.

So succession becomes less about “Who is the successor for role X?” and more about “How do we ensure the next generation develops judgment, business context, leadership capacity, and AI fluency—even if they don’t follow a traditional role sequence?”

From what I’ve read so far, organizations can utilize rotations, projects, continuous learning, teaming of new and experienced talent, co-working with AI, and collaboration across functions or geographies—rather than staying in silos—to develop their leadership pipelines.

This way, the new career cycle can look something like: Learning → capability building → moving across work → building judgment → leading.

And still, I keep coming back to one thought.

If that first experience—the one that shapes how you think and grow—is changing, then we cannot leave it to chance.

Having started my own career through that traditional path, I find myself hoping that we can be just as intentional—if not more—in designing how the next generation grows into leadership.

Further reading:

• EY Megatrends 2026 and beyond report EY Megatrends
• Harvard Business Publishing – The Fluid Future of Work: Rethinking Roles in the Age of Intelligent Machines Link
• World Economic Forum – Jobs and Future of Work Link
• World Economic Forum – Future of Jobs Report Link
• Deloitte – Skills-based organizations and workforce design Link
• BCG – Skills-based organizations Link
• McKinsey – AI and workforce / leadership insights Link

Initially published on Linkedin