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McKinsey & Company is requiring some junior candidates to use an in-house AI assistant during interviews, a move that shows how deeply artificial intelligence is integrated into consulting workflows.
The pilot, first reported by the Financial Times, comes as consulting firms re-evaluate staffing levels, compensation increases and productivity expectations in the wake of slowing demand and increased adoption of AI. For CFOs, this shift highlights how technology is intersecting with the consulting industry’s up-or-out career model.
How AI is reshaping performance expectations
In the pilot, some graduate candidates were asked to use McKinsey’s AI tool, Lilli, during case interviews. The FT reported that interviewers assessed applicants on how they prompted the system, evaluated its results and applied the conclusions to client scenarios. This focus aligns with how junior consultants work in the field, where AI supports research, benchmarking, and early-stage analysis.
Consulting firms like McKinsey operate on this “up-or-out” career system, where employees are expected to advance or retire within a set period of time. In this working model, junior consultants provide analytical capabilities, mid-level managers integrate communications and manage delivery, and senior leaders focus on customer relationships and business development.
As any finance leader who has worked at these companies knows, consultants are regularly evaluated and those who fail to meet expectations are encouraged to leave before being fired. This model allows companies to manage compensation growth, maintain leverage ratios, and keep employee numbers in line with customer demand.
As AI absorbs more junior-level analytical work, performance expectations may change early in a consultant’s career. The output levels associated with later stages of development are In theory With AI support, you can now reach us faster. From an outside perspective, this can shorten standard promotion timelines and increase pressure on junior consultants to demonstrate judgment, background, and communication skills sooner.
Consultants who struggle to adapt to or meet the expectations of AI-assisted workflows are likely to leave their jobs sooner than their predecessors. Even if overall demand among clients stabilizes, the base of the consulting pyramid could narrow as companies adjust hiring and evaluation criteria to reflect increased productivity per employee.
Impact on the alumni flywheel
The Up or Out system also supports long-running alumni flywheels. McKinsey has built perhaps the largest and most influential alumni network in the private sector, with former consultants frequently moving across industries into executive, finance, and board roles. Referred to by some as the “McKinsey Mafia,” these graduates often become purchasers of consulting services once they gain budgetary authority, reinforcing demand for their services over time.
From a CFO’s perspective, the alumni flywheel extends the return on investment in training beyond the paid work offered by companies like McKinsey. Although many consultants retire after two to four years, their familiarity with consulting methodologies, teams, and delivery models often carries over into subsequent executive roles. As these graduates gain budget authority, that familiarity can reduce friction in future consulting assignments and support iterative business models.
At the same time, the former consultant’s operational track record is gaining support and attention. Boards and investors are questioning how consistent strategy-focused training translates into execution in complex real-world operating environments.
The argument points to Laxman Narasimhan’s short tenure as CEO of Starbucks after a career at McKinsey & Co. Similar questions persist in the legacy of former McKinsey partner Jim McNerney, whose tenure at Boeing was later criticized for emphasizing financial and strategic priorities over operational resilience and engineering rigor.
These examples reflect broader tensions within consulting business models. As AI reshapes advisory work and increases expectations for measurable outcomes, clients and boards are likely to place greater emphasis on operational performance, judgment, and execution over things like analytical skills. McKinsey’s focus on recruiting candidates with liberal arts backgrounds, which executives say can bring more fresh, nonlinear thinking, is a recognition that traditional strategy training alone won’t necessarily be enough as AI absorbs much of the structured analysis work.
While the scope of McKinsey’s hiring experiment remains limited, it reflects broader adjustments underway across consulting and the economy. As AI becomes more embedded in daily operations both inside and outside of consulting, companies in this business are recalibrating the way they hire, evaluate, and promote talent. These changes are forcing consulting firms like McKinsey to rethink how they balance efficiency, experience, and reliability as AI becomes entrenched in customer service.
