Early in my consulting career, I built a business case for an eProcurement system. No AI, just me, a spreadsheet and more late nights than I would like to admit. I did the research, built the model line by line and wrote down every assumption. Before it went to the client, my senior manager reviewed my work and asked question after question.
Then came the presentation to the CFO. She asked one question: “How will the ROI be realized?” She wasn’t asking about the spreadsheet. She was testing whether the plan was realistic. I walked her through the thinking behind the numbers, well beyond what was on the page. I could do that because I had done the work and because my manager’s questions had prepared me for hers.
Fast-forward thirty years. Whether I am mentoring young professionals or catching up with colleagues from my consulting days, we still tell those stories: the all-nighters to prepare for a facilitated session, writing and rewriting executive communications until the tone and positioning were right, the deck rebuilt at 2 a.m. We laugh about them, but the lessons still ring true. That was where we learned how to work, and how to think critically. Doing the work, and having a manager review it, were the bottom two rungs of the career ladder.
Now picture this. A young analyst asks AI to build the same business case. In an hour, she has clean slides, a tidy model and a confident ROI. No one reviews the model or the assumptions. She presents it, and the CFO asks the same question: “How will the ROI be realized?”
Silence. The AI assumed it. She never checked, and if she’s honest, she wouldn’t have known how.
The problem isn’t that she used AI. Every professional, at every level should. The problem is that she trusted it and no one reviewed her work.
The work that taught us how to work is disappearing. McKinsey lists the tasks AI is absorbing: research, documentation, data cleanup, basic coding, preliminary analysis. The list goes on. That was the training ground. Slogging through those assignments is how we learned what good looks like, and it’s what lets us challenge AI today. If you have never done the work, how do you know when AI is wrong? How do you know if you wrote a “good” prompt?
AI has raised the bar: young professionals are expected to deliver more, sooner, with less of the on-the-job learning that builds business chops. As Fast Company put it, AI can speed up early-career performance, “but it cannot accelerate the accumulation of judgment.”
The analyst wasn’t the only one who skipped a step. Her manager did too. My manager’s questions were half my education, and they are what prepared me for the CFO’s.
Today, young professionals tell me their managers rarely make time to review work. Teams are leaner, calendars are full, and remote work has ended the side-by-side review. And to boot, AI-generated work looks finished, even when the thinking isn’t. In a Harvard and BCG study cited by Fast Company, consultants using AI produced more polished work, yet junior consultants struggled to explain where their recommendations might break down. Polish is not proof of rigor.
That changes part of the manager’s job. Repetition used to build judgment. Now AI absorbs the repetition, so judgment has to be built deliberately, and the review is the main place left to build it. AI can’t do that review for you. It can check the formulas, but it can’t explain the business context behind the answer or connect the dots that make an analyst better. Skip the review, and we remove the last rung. The cost won’t show up in the next deck. It will show up years later, when we promote people into roles that demand instincts no one helped them form. If we don’t build those instincts now, when will we?
If you are early in your career, learn to do the work, use AI to be efficient, and consider the following questions before submitting your work.
Remember, AI doesn’t replace experience, human judgment and context.
If you manage young professionals, you are the answer to question five above. As managers, we play an integral role in shaping future leaders.
To this day, I remember the tough questions asked and the learning moments. When the next executive asks a question, the answer shouldn’t depend on who wrote the prompt. It should depend on who did the work, and who took the time to review it.
Stay curious. Learn to do the work. After all, the company hired you, not the machine.
Eighty-three million jobs will be disrupted by 2027, according to the World Economic Forum. Sixty-nine million new roles will be created. Every headline treats that net loss of 14 million jobs as the story. It isn’t. The real story is the turbulence in between and it is reshaping every stage of the career lifecycle at once.
Entry-level roles are contracting. Labor economists call this the ‘lost rung’, the entry point new graduates are reaching for and finding missing.
Mid-career professionals are being told to reskill without a roadmap. Parents and students are making education decisions in a labor market that no longer resembles the one the curriculum was built for.
The disruption is not arriving. It has arrived and most organizations, and most individuals, do not have a strategy (yet).
From Paradox to Inflection Point
This is not the first time the labor market has demanded a new mindset. In 2014, I began writing about what I called the Talent Paradox, a labor market defined by contradiction. Organizations struggled to find critical skills, while millions of capable workers struggled to find meaningful roles. That paradox shaped seven years of research, writing, and advisory work.
What we are navigating now is categorically different. The organizational response will lead to flatter structures, redesigned roles, and compressed hierarchies. The question is whether it is happening by design or by default.
The Workforce Is Transforming, Faster Than Expected
Artificial intelligence has moved from a promising capability to a structural force reshaping entire industries. Between 2015 and 2023, global investment in AI increased more than twentyfold. Today, generative AI is being adopted faster than any prior enterprise technology, with more than 60 percent of organizations already using it in some capacity.
This shift is not theoretical. The 14 million net loss masks deeper disruption, leaving experienced professionals and new graduates navigating a labor market that no longer resembles the one they prepared for.
Entry‑level hiring has contracted sharply across sectors including consulting, technology, and financial services. Roles that once served as training grounds for early‑career talent are being redesigned or eliminated as AI absorbs routine analysis, reporting, and coordination work.
Mid-career professionals face a different version of the same pressure. Research from MIT and OpenAI indicates that up to 49 percent of tasks performed by knowledge workers such as analysts, marketers, HR practitioners, lawyers, consultants, or project managers are highly exposed to AI-driven augmentation or automation. These professionals are being asked to rapidly develop capabilities in data literacy, prompt engineering, workflow automation, and AI-enabled decision making, most often without clear role definitions, skill benchmarks, or structured pathways to follow.
And parents and students are facing the same uncertainty, navigating decisions about majors, institutions, and career trajectories in a market that is evolving faster than any counselor, ranking system, or historical precedent can reliably guide. What should a student study when many of today’s considered-safe majors are being reshaped faster than universities can update curricula?
This is not sequential disruption, where one group stabilizes before the next is affected. It is convergent, and that convergence is what renders the normal adaptive mechanisms, mentorship, career ladders, institutional guidance, inadequate together.
Why This Matters Now
Organizations are moving quickly on AI adoption, but far slower on workforce redesign, skills pathways, and talent strategy. The result is a widening gap between technological ambition and organizational readiness.
Leaders recognize the need to act. Most lack both the diagnostic view and the roadmap to do so. Individuals face the same uncertainty as disruption accelerates without a clear path to navigate it.
Technological ambition without organizational redesign is not a strategy. It is a deferral.
This blog exists to help leaders and individuals understand what is changing, why it matters, and how to respond.
What You Can Expect
Perspectives was launched to offer practical, research-informed insights on the forces reshaping work, talent, and opportunity.
For executives: how to lead job redesign, rebuild talent architecture, and close the gap between AI adoption and organizational readiness before it becomes a competitive liability.
For mid-career professionals: how to assess your own exposure honestly, identify the capabilities that matter in this specific transition, and position for the roles that are emerging rather than defending the ones that are contracting.
For families: clear, research-grounded frameworks for making education and career decisions in a market that has outpaced the institutions built to guide them, without panic, and without false certainty.
The commitment is straightforward: concise analysis, disciplined thinking, and a clear separation of signal from noise. The goal is not to speculate about the future—but to help leaders and individuals act strategically within it.
A New Chapter for a New Workforce Reality
The Talent Paradox was about understanding tension in a shifting labor market. What follows is about navigating the next decade, one where the cost of strategic misalignment is greater than at any point in recent workforce history.
Welcome to the next chapter.
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