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 Rungs AI Is Removing
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 Last Rung: The Missing Reviewer
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?
Putting the Rungs Back
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.
For young professionals: 5 questions before you hit send
- Where did this number come from? Trace every key figure to a source. If AI can’t point to one, it’s an assumption, not a fact.
- What is it assuming? Ask AI to list its assumptions on growth, costs and timing. Understand what each one means for the business.
- Does it pass the gut check? Compare it with last year’s actuals or a back-of-the-envelope estimate. If it’s off by a factor of ten, find out why.
- Could I defend this without AI in the room? If you are asked, “How did you get there?”, can you walk them through it? If not, you’re not done.
- Who else has checked this? Before it goes up the line, schedule 30 minutes with your manager to review your work. Ask what AI doesn’t know about the business. It sees the data you gave it, not the hallway conversations.
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.
- Review the reasoning and ask questions: Why, How, Where does this break?
- Make time. A 30-minute walkthrough costs far less than a flawed analysis in front of the executive team.
- Explain your reasoning, don’t just give the answer. When you push back, say why. That is what turns late nights into lessons.
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.
Sources
- Hancock and Seiler, Building expertise in the age of AI: Who trains the next generation?, McKinsey Quarterly, July 14, 2026
- Ahuja, AI is reshaping career ladders, Fast Company, March 24, 2026