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AI has been expected to remove some of the bottom rungs on the corporate ladder. As software absorbs routine analysis, research, drafting, and administrative work, companies have had fewer reasons to hire large classes of junior employees. Recent evidence suggests that assumption may be changing. Some major employers are hiring again, and companies using AI extensively are reporting greater demand for entry-level workers.
KPMG (Klynveld Peat Marwick Goerdeler) offers an especially interesting example. The professional services company recently brought nearly 1,000 interns to its Lakehouse training center in Orlando for intensive development in critical thinking, judgment, communication, and interpersonal skills. KPMG’s own research found that interns ranked critical thinking and problem solving as the most important capabilities they wanted to demonstrate while working alongside AI.
KPMG may be responding to a problem that extends far beyond accounting and consulting. Entry-level jobs have always produced more than inexpensive labor. They provide the experiences through which organizations create their next generation of experts, managers, and decision makers.
AI can remove the work that teaches people how to work
Consider what traditionally happens during the first few years of a career. Junior employees research unfamiliar problems, prepare first drafts, make mistakes, receive corrections, observe customers, and explain their reasoning to people with greater expertise. Much of that work can look inefficient because learning itself is inefficient.
Generative AI changes the calculation. A senior employee who once delegated research or preliminary analysis to a junior colleague can now ask an AI system to produce it in seconds. The immediate productivity gain is obvious. The developmental cost is much harder to see.
A recent study of software engineers identified precisely this risk. Researchers interviewed junior and senior software engineers and found evidence that generative AI was absorbing some of the work through which novices traditionally developed expertise. They described the loss of “productive struggle,” the difficult process of working through problems that helps beginners construct the knowledge they later need to solve more complicated ones.
The study is small, involving 14 interviews in South Korea, so its findings should not be treated as proof of what is happening across every profession. It raises an important organizational question, however. If AI continually transfers entry-level work from beginners to experienced employees working with machines, where will future experienced employees come from?
Productivity today can create a capability problem tomorrow
Organizations naturally measure AI by what it saves. A task takes 20 minutes instead of two hours. One employee produces what previously required three. Projects move faster and staffing requirements decline.
Those measures capture current output. They rarely capture the expertise the organization would have developed while producing that output. A junior analyst struggling through an assignment may be slower than AI, but that assignment is simultaneously producing an analysis and a more capable analyst.
KPMG appears to recognize that distinction. Its Lakehouse is explicitly designed as a learning and development environment, and the company says it is expanding immersive, in-person experiences as AI increases the importance of human judgment and other skills. KPMG has also emphasized mentorship, hands-on work, feedback, and real-world application as part of preparing employees to work alongside AI.
Redesign entry-level work around learning
Companies do not need to preserve routine work simply because generations of employees learned from doing it. They do need to identify what developmental function that work performed before automating it away.
That means designing early-career roles around experiences AI cannot easily provide by itself. Junior employees can frame problems before prompting an AI system, verify its work, explain why they accepted or rejected its recommendations, interact directly with customers, observe experienced colleagues making consequential decisions, and assume progressively greater responsibility for outcomes.
This also changes what leaders should measure. The fastest junior employee may be the person who delegates the most thinking to AI. A more useful question is whether that employee is becoming capable of handling increasingly difficult work with less supervision.
AI can make an organization more productive while simultaneously weakening its ability to reproduce expertise. Those outcomes will not appear on the same dashboard or on the same timetable. KPMG’s investment in interns offers a useful reminder that organizations still need beginners, because somewhere inside today’s entry-level workforce are the seasoned experts they will depend on tomorrow.
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