AI has affected the consulting market in many ways. A full assessment would cover strategic positioning, leverage economics, technology and product choices, governance, competitive dynamics and the changing role of the partner. This piece considers one narrower question. If AI changes the work through which consultants develop expertise and judgment, how should firms develop the next generation?
In this article, we examine how AI is changing the apprenticeship through which firms develop people able to direct, challenge and take responsibility for the work. The response is to be more deliberate about the capabilities future leaders will need, and to design AI-augmented work so that junior consultants still practise forming views, exercising judgment and learning through review. Without that, firms risk accumulating capability debt.
A delivery model that also develops people
The consulting delivery model has long done two jobs at once. It has generated revenue while developing professionals. The traditional pyramid was not simply an economic model. Partners won work and managed senior client relationships. Directors, managers and juniors did much of the execution. The same staffing structure that generated revenue also gave juniors the repeated work and feedback through which they became managers and eventually partners.
AI changes that arrangement. Tasks that previously justified substantial billable effort can now be completed much faster, while systems, workflows, models and data allow smaller teams to produce more. Some of the work AI removes was never especially developmental. Removing it may improve apprenticeship if the released capacity is reinvested in more challenging work, earlier client exposure and better review. The risk is allowing automation to remove developmental work without replacing it.
Client expectations sharpen the commercial tension. Source Global Research found that 82% of UK and US clients expect AI to speed consulting projects, but clients were almost evenly divided on whether it would make the work better or more generic.1 The question is whether teams use the time and capacity AI releases to produce work that is more useful, rigorous and attuned to the client situation.
In the MCA's 2026 survey of more than 1,000 consultants, 77% said their firm had either integrated AI into its systems or enabled employees to use AI models. Seventy-eight per cent identified digital technology and AI as drivers of growth.2 These figures do not describe every firm or every type of work. They do make the capability question current rather than distant.
The effects will be uneven across firms, practices and client situations, depending on the nature of the client need and the work required to address it.
of more than 1,000 consultants say their firm has integrated AI or enabled employees to use AI models.
of UK and US clients expect AI to speed projects, split evenly on whether work gets better or more generic.
hires across 80-plus member firms in 2025; graduate recruitment up 10%, apprentice hiring up 31%.
What apprenticeship produced, and what is at risk
A natural response is to ask which capabilities will remain permanently human. However, given that the boundary of what AI can do will continue to move, the more useful question is developmental. Apprenticeship should develop people who can supervise, integrate and take responsibility for work to a professional standard, even when it is AI-augmented.
Consulting apprenticeship did this through volume, repetition and feedback. Juniors structured messy problems from scratch, built hypothesis-driven analysis, drafted and modelled, recognised patterns, read client rooms and formed judgments about what mattered in a sea of evidence.
Problem structuring is vulnerable when AI becomes the default first action. The previous loop required a junior consultant to face a messy problem, propose a starting frame and revise it in response to challenge. When AI supplies a credible first structure on request, that loop may never begin. Evaluating and editing a structure is valuable, but it depends on some ability to create one.
View-taking is vulnerable too. Consulting often requires people to form a view before they have complete information, defend it under questioning and revise it when they are wrong. AI can present balanced perspectives quickly and fluently. That can make it easier to sound informed about every angle without committing to a position that must withstand challenge.
A third capability is the developed instinct for spotting what is missing or wrong in fluent material. AI output can appear complete and persuasive when the reasoning is shallow, generic or incorrect. Juniors who only review polished output will not develop the same instinct as people who have built first cuts themselves and learned what their own drafts were missing.
Client judgment is developed in the same way. Review cycles with managers and partners teach people to anticipate client perspectives, decide what to say and leave unsaid, and turn analysis into a position that can be used in a live situation. Leaner teams and faster production can reduce those moments.
An experiment involving 758 BCG consultants illustrates how sharply performance can vary between tasks. On tasks within GPT-4's capabilities, consultants using it completed work more than 25% faster, with quality scores more than 40% higher. On a task outside those capabilities, AI use reduced the likelihood of a correct answer. Conducted in 2023 using GPT-4, this was a bounded experiment rather than a test of the partner role. The frontier has moved since. Its enduring point is that AI performance can differ sharply from task to task.3
What becomes more important
AI does not make these capabilities newly valuable. It changes their relative weight. The work becomes less about producing a plausible first answer and more about deciding which question matters, what can be provisionally assumed, what must be tested and which trade-off the team should recommend. It is making balanced progress when there is no complete dataset or mechanically correct answer.
Contextual judgment matters for the same reason. It is knowing when an answer that is technically correct is wrong for this client, at this moment, in this situation. These capabilities need not be permanently beyond AI to matter. Firms need people who can exercise them now, and development takes time.
Development will need to be designed
Juniors need to do meaningful cognitive work before AI enters the process. An analyst might build the first issue tree, storyline or hypothesis set before using AI to stress-test it, identify missing branches or generate alternatives. In that role, AI acts as critic and amplifier rather than substitute.
Tasks should not all be treated alike. Work with high learning value or greater risk should normally involve a human first attempt and structured review. This may include setting hypotheses, designing client interviews or building arguments from incomplete evidence. Work with lower learning value and risk, including routine data pulls and formatting, can be automated more aggressively. The point is not to preserve every task. It is to make the developmental purpose of the work explicit.
Three choices matter. Juniors should be able to explain what they took from AI output, what they rejected and why. Reviewers need to see the pre-AI cut, initial hypotheses, prompts and rejected alternatives, not only a polished artefact. Firms also need to decide how protected developmental time and AI capacity will be funded. Some of that work is an investment in future capability, not simply a margin cost.
On a recent transformation engagement I led, analysts spent the first ninety minutes developing their own issue tree and hypotheses before using AI. The tool generated additional branches, counterarguments and a first pass at relevant evidence. In review, the manager found that one persuasive branch rested on an assumption none of the analysts had tested. Because the original reasoning was visible, the team could see the gap. The output was faster, but the more useful result was that each analyst had formed a position before encountering the machine's.
The risk of doing nothing
Inaction is not neutral. Firms may gain near-term efficiency while accumulating capability debt. Teams that cannot explain or defend the logic behind AI-generated work can produce recommendations that look polished but do not withstand challenge. That can lead to rework, poor client conversations and a loss of confidence in the senior team's assurance of the work. Over time, managers and partners may have less depth from which to supervise AI-augmented work credibly.
In a conversation with a partner I have been coaching, we explored a live engagement where a competitor had promised a first draft in an afternoon using AI, while she was under pressure to improve her margin. Rather than let the team begin with the tool, she asked each analyst to bring an initial view and its key uncertainties to review. The team could describe several AI-generated options, but could not say which they recommended or what trade-off they were prepared to make. The point was not to reject AI. It was to ensure the team learned to take responsibility for the position it put forward.
This cannot be left to individual leadership choice alone. Pricing, staffing targets, utilisation measures and partner incentives can make short-term efficiency rational even where it weakens future capability. Firms that want a developmental stance need to decide how it will be funded and rewarded.
Uneven exposure
Consulting fees pay for several distinct client needs. These include expertise the client cannot economically develop in-house, capacity at peak, third-party legitimacy for politically difficult internal decisions, accountability transfer when something controversial is being recommended, and signalling that an issue is being taken seriously.
AI threatens these needs unevenly. The expertise and capacity elements are most directly exposed. Legitimacy, accountability and signalling may retain value, and in some circumstances become more important, as analytical work becomes cheaper and more available.
Practice models will be affected differently too. A heavily leveraged practice, in which scale comes from teams doing substantial analysis and production work, faces different pressures from a specialist advisory practice with lower leverage, or work where value rests more heavily on senior relationships, judgment and accountability.
Some firms may decide that a leaner model, supplemented by senior hiring, is rational. But firms that rely on internally developed managers and partners cannot simply assume those capabilities will still emerge.
The early-talent picture is not one of uniform contraction. The MCA's 2026 Annual Industry Report, drawing on data from more than 80 member firms, recorded more than 5,400 hires in 2025. Graduate recruitment had risen by 10% and apprentice hiring by 31% on a like-for-like basis.4 This does not settle how particular firms will change, but it reinforces the importance of business model, practice and client situation.
Wider implications
If apprenticeship requires more design than it once did, firms may need to reconsider hiring, rotations, shadowing, early client exposure, career pathways and the allocation of partner attention. The distribution of developmental work will become a strategic issue. Some cohorts may receive rich experience of problem solving, client exposure and structured review. Others may become efficient operators of AI-enabled workflows with fewer opportunities to practise judgment. Over time, that can shape promotion pipelines and the availability of credible future leaders.
This is not a complete model of the future consulting firm. It identifies a capability problem that can otherwise remain hidden inside a productivity story.
Managing partners should start with one live engagement and understand what AI is doing to the work itself. They should ask four questions.
Which tasks is it compressing and which is it relocating?
What should the team do for itself before turning to the tools?
What learning should this engagement produce for the juniors involved?
What would allow the senior team to see the reasoning behind the final output, rather than simply admire its fluency?
Making those choices explicit in one case is more useful than holding vague views about AI in general. The firms that develop the next generation well will be those that make deliberate judgments about what to preserve, what to redesign and what kind of professionals they are trying to build.
Sources
Saba Arab coaches senior executives, partners and enterprise leaders through senior transitions, and advises leadership teams on AI disruption, leadership models and capability. Start a conversation.
