Summary: AI is not killing consulting careers. It is exposing a much bigger challenge: the traditional apprenticeship model that produced generations of consultants, executives and business leaders is under pressure. The firms that adapt how expertise is developed may have a significant advantage over the next decade.
The Wrong Question
Much of the discussion around AI and consulting focuses on productivity. How many analysts will firms need? How much faster can projects be delivered? What happens to utilisation rates, margins and the traditional pyramid structure?
These are important questions, but they may not be the most important ones.
The deeper issue is not whether AI can replace consultants. It is whether AI changes how consultants become experts. For decades, consulting firms have relied on a highly effective apprenticeship model that converted talented graduates into trusted advisors, industry leaders and future partners. The same activities that generated client revenue also developed consulting capability. As AI increasingly automates parts of that process, the industry faces a more fundamental challenge than productivity alone.
The real question is not whether AI can produce the work. It is whether consulting can continue producing the people.
The Apprenticeship Was The Product
The success of management consulting was never simply about delivering analysis. Its real achievement was creating a system that accelerated professional development faster than almost any other industry.
Graduates joined consulting firms with intelligence and potential, but rarely with deep expertise. Over time, they learned how organisations operate, how executives make decisions and how complex problems are solved. They developed those capabilities through repeated exposure to client work. Research, modelling, problem solving, stakeholder management and presentation development were not just deliverables; they were the mechanism through which consultants learned their craft.
Importantly, this process was often inefficient from a purely operational perspective. Junior consultants spent days building analyses that more experienced colleagues could complete much faster. Presentations went through countless revisions. Workstreams were challenged, rebuilt and refined repeatedly before reaching a client. Yet that inefficiency was not waste. It was training.
What clients purchased as consulting services also functioned as a leadership development engine. The profession’s influence across business, private equity, technology and the public sector is partly a consequence of how effectively it developed talent. The apprenticeship model was not a by-product of consulting. It was one of its most valuable outputs.
AI Changes How Expertise Is Built
Generative AI changes this dynamic because many of the activities that historically developed consulting capability can now be completed in a fraction of the time.
Research, synthesis, benchmarking, financial modelling and first-draft presentation development are increasingly supported by AI tools. Every major consulting firm is investing heavily in internal platforms designed to accelerate delivery, improve knowledge management and reduce manual effort. The productivity benefits are obvious. Teams can move faster, operate with fewer people and spend more time engaging with clients.
The challenge is that many of the tasks being automated were also the tasks through which junior consultants learned.
Building a presentation from scratch teaches different lessons than reviewing an AI-generated version. Conducting original research develops different instincts than validating machine-produced outputs. The value of these activities was never just the deliverable itself. It was the judgment developed during the process.
This is where much of the current discussion misses the point. The question is not whether AI can create a good slide deck. The question is whether reviewing that slide deck develops the same level of commercial judgment, critical thinking and problem-solving capability as building it yourself.
The answer remains unclear.
Expertise Is Replacing Leverage
At the same time, it would be wrong to suggest AI is solely responsible for the pressure on the traditional consulting model.
The market has been moving in this direction for years. Clients have become increasingly selective about where they spend consulting budgets and what they are willing to pay for. Many organisations are less interested in funding large teams of generalist consultants and more interested in accessing expertise that is difficult to build internally.
In sectors such as healthcare, industrials, energy, financial services and technology, buyers increasingly expect consultants to bring industry knowledge, implementation experience and a clear point of view. The value proposition has shifted from analytical capacity toward practical expertise.
This helps explain the growth of specialist boutiques over the last decade. Many have built successful businesses not through scale, but through depth. Their advantage is not the size of their teams. It is the quality of their expertise.
AI accelerates this trend because it makes information easier to access. When every firm can generate research, analysis and presentations more efficiently, expertise becomes more valuable relative to execution. The market therefore moves further toward specialists and away from generic leverage.
In many ways, AI is exposing a shift that was already underway.
The Future Consultant Looks Different
This shift has important implications for how consulting careers develop.
Historically, firms could recruit exceptional graduates and teach them almost everything they needed to know through structured apprenticeship. The emphasis was on problem solving, communication and structured thinking. Industry expertise often came later.
That sequence may be changing.
The most valuable consultants of the next decade are likely to combine AI-enabled productivity with genuine depth of expertise. Sector knowledge, implementation experience and specialised understanding may become more important earlier in a career than they were historically.
This does not mean generalist problem-solving skills become irrelevant. Quite the opposite. The ability to structure problems, communicate clearly and navigate ambiguity remains critical. However, those skills may increasingly need to sit alongside domain expertise rather than substitute for it.
The junior consultants who thrive will not simply be the best prompt engineers. They will be the people who understand enough about an industry, function or client problem to know when the AI output is incomplete, misleading or wrong.
As AI becomes better at producing answers, the value increasingly shifts toward knowing which answers matter.
The Bigger Challenge For Consulting Firms
Much of the industry conversation focuses on efficiency gains. The more difficult challenge is capability creation.
Consulting firms have always depended on a pipeline of talent development. Today’s analysts become tomorrow’s managers. Managers become directors. Directors become partners. The model works because expertise compounds over time.
If firms require fewer junior consultants, and if much of the traditional learning environment becomes automated, they will eventually need new mechanisms for developing judgment. That is not a technology challenge. It is an organisational one.
Judgment cannot simply be delivered through online training programmes or embedded within AI systems. It is developed through exposure, repetition, accountability and feedback. Historically, client delivery created that environment naturally. Future consulting operating models may need to create it more deliberately.
This may become one of the defining leadership challenges for consulting firms over the next decade.
The Real Question Facing Consulting
The debate about whether AI will replace consultants is likely to continue. It is also likely to miss the point.
Clients will continue to need people who can frame problems, challenge assumptions, manage stakeholders, navigate uncertainty and lead change. Those capabilities remain difficult to automate because they depend on judgment, trust and accountability rather than information alone.
The harder question is how the profession develops people who possess those qualities.
For decades, consulting firms solved that problem through apprenticeship. Junior consultants accumulated judgment through repetition, exposure and responsibility. As more of the execution layer becomes automated, the industry will need a new mechanism for creating that judgment.
Clients will continue to pay for expertise, trust and accountability.
What remains unclear is how the next generation will acquire them.
Ben Appleton is the founder of Strat-Bridge, a specialist executive search partner to the strategy consulting industry. He works with global consulting firms and senior leaders across the UK, Germany, Switzerland, and beyond — helping them build capability at the Partner and Director level.
