For decades, the career ladder at the Big Four accounting and consulting firms followed a well-worn, if unglamorous, path. Junior employees cut their teeth on repetitive, time-consuming tasks—drafting documentation, preparing slide decks, entering data, reconciling accounts, and performing quality checks. This so-called “grunt work” was widely seen as essential, instilling the discipline and reasoning skills needed to eventually lead teams as directors and partners.
That model is now being disrupted by the rapid adoption of artificial intelligence (AI) agents. Senior leaders across the Big Four believe that routine work will increasingly be handled by AI, freeing junior staff to focus earlier on strategic, higher-value assignments.
But the shift has triggered a fundamental question: if junior employees no longer spend years doing foundational work, how will they develop the deep understanding and professional judgment that traditionally came from repetition and hands-on experience?
Concerns are growing across academic and professional circles. Experts in accounting education warn that promoting employees without a solid grasp of the work at the base of the pyramid could introduce risks—for firms and for clients. Decisions made at senior levels, they argue, depend on an intuitive understanding of processes that was historically built through years of doing the work firsthand.
Even within the firms, the uncertainty is being openly acknowledged. Talent and workforce leaders admit that while AI is changing how experience is acquired, there is no universally agreed replacement for the traditional learning model.
Certified Cyber Crime Investigator Course Launched by Centre for Police Technology
Faster output, but questions over depth of thinking
AI agents can now scan vast datasets in seconds and generate summaries and recommendations—tasks that once took junior employees days or even weeks. While this efficiency is widely welcomed, experts caution that it comes with a cognitive risk.
There is growing concern that professionals may develop an illusion of understanding by reviewing AI-generated outputs without fully grasping the logic behind them. Others warn of over-reliance on AI, where users gradually lose confidence in their own judgment and analytical instincts.
As a result, Big Four firms are rethinking how learning should happen. The emerging view is that junior staff may no longer need to build everything from scratch, but they must learn to interrogate AI outputs—to question assumptions, understand how conclusions are reached, and identify where human judgment must override automated recommendations.
Teaching the ‘why’, not just the task
Firms say they are shifting emphasis from task execution to underlying reasoning. Early-career professionals are being trained to understand when AI is an appropriate tool and when human insight is indispensable.
Several firms have rolled out structured AI training programmes that combine technical instruction with human skills such as communication, judgment, and critical questioning. The logic is straightforward: as machines accelerate execution, the human role must focus on context, interpretation, and decision-making.
Talent leaders argue that foundational skills still matter, even if the way they are acquired changes. Rather than spending years on manual processes, juniors are expected to learn how different parts of the work fit together—and how to challenge results produced by AI systems.
New work, new skills
Some leaders believe the old learning model may not have been the only—or even the best—way to develop future leaders. AI is already reshaping the nature of Big Four work. Consulting teams are being pushed toward large-scale transformation projects, deeper sector expertise, and closer strategic partnerships with clients. In accounting, efficiency gains are making room for more advisory and strategic roles.
As part of this shift, junior employees are being exposed to clients and decision-making much earlier in their careers. Firms argue that this can accelerate development rather than dilute it—provided young professionals are given responsibility for explaining and defending AI-assisted analyses.
According to this view, AI enables early-career staff to move into higher-value work sooner, while still being held accountable for outcomes and reasoning.
An open question remains
Yet a central uncertainty remains unresolved: can this new model truly replace the depth of understanding that once came from years of hands-on, repetitive work?
The answer may only become clear when a generation of AI-native professionals rises to the top and takes on leadership roles. Until then, the Big Four are engaged in a large-scale experiment—one in which AI represents both an opportunity and a risk.
What is at stake is not just productivity, but how the next generation of leaders will be shaped in an AI-first workplace.
About the author – Rehan Khan is a law student and legal journalist with a keen interest in cybercrime, digital fraud, and emerging technology laws. He writes on the intersection of law, cybersecurity, and online safety, focusing on developments that impact individuals and institutions in India.
