McKinsey & Company is requiring some junior candidates to use an internal AI assistant during interviews, a move that signals how deeply artificial intelligence is becoming embedded in consulting workflows.
The pilot, first reported by the Financial Times, comes as consulting firms reassess staffing levels, compensation growth and productivity expectations amid slower demand and broader AI adoption. For CFOs, the shift highlights how technology is intersecting with consulting’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. FT reporting explains that interviewers evaluated applicants on how they prompted the system, assessed its outputs and applied conclusions to a client scenario. The focus aligns with how junior consultants increasingly work on the job, where AI supports research, benchmarking and early-stage analysis.
Consulting firms like McKinsey operate under this “up-or-out” career system, in which employees are expected to advance within a defined time frame or leave. In this work model, junior consultants provide analytical capacity, mid-level managers synthesize communication and manage delivery and senior leaders focus on client relationships and business development.
As finance leaders who have worked for these firms know, consultants are reviewed regularly, and those who fail to meet expectations are encouraged to leave before being let go. The model allows firms to manage compensation growth, maintain leverage ratios and keep headcount aligned with client demand.
As AI absorbs more of that junior-level analytical work, performance expectations may shift earlier in a consultant’s career. Output levels once associated with later stages of development can theoretically now be reached sooner with AI support. From the outside looking in, this will likely compress the timeline for a standard promotion and increase pressure on junior consultants to show judgment, context and communication skills earlier.
Consultants who struggle to adapt to AI-assisted workflows or meet those expectations could exit earlier than previous employees. Even if overall demand among clients stabilizes, the base of the consulting pyramid may narrow as firms adjust hiring and evaluation standards to reflect higher productivity per employee.
The impact on the alumni flywheel
Up-or-out systems also support a long-running alumni flywheel. McKinsey has arguably built one of the largest and most influential alumni networks in the private sector, with former consultants frequently moving into executive, finance and board roles across industries. Those alumni, referred to by some as the “McKinsey Mafia”, often become buyers of consulting services once they hold budget authority, reinforcing the demand for their services over time.
From a CFO perspective, the alumni flywheel extends the return on training investment beyond the billable work offered by firms like McKinsey. Many consultants leave after two to four years, but their familiarity with consulting methods, teams and delivery models often carries into later executive roles. When those alumni gain budget authority, that familiarity can reduce friction in future consulting engagements and support that repeat business model.
At the same time, the operating track record of former consultants has drawn support and scrutiny. Boards and investors have questioned how consistently strategy-focused training translates into execution in complex, real-world operating environments.
The short tenure of Laxman Narasimhan as CEO of Starbucks, following a career that included time at McKinsey, has been cited in that debate. Similar questions have followed the legacy of Jim McNerney, a former McKinsey partner whose tenure at Boeing was later criticized for emphasizing financial and strategic priorities over operational resilience and engineering rigor.
Those examples reflect a broader tension within the consulting business model, because as AI reshapes advisory work and raises expectations for measurable outcomes, clients and boards will likely place greater value on operating performance, judgment and execution over things like analytical skills alone. McKinsey’s increased emphasis on recruiting candidates from liberal arts backgrounds, which firm leaders have said can bring more novel, non-linear thinking, is an acknowledgment that traditional strategy training is not always sufficient as AI absorbs more of the structured analysis work.
McKinsey’s hiring experiment remains limited in scope, but it reflects broader adjustments underway across consulting and the greater economy. As AI becomes embedded in daily work in and outside of consulting, firms in this business are recalibrating how they hire, evaluate and advance talent. Those shifts are forcing consulting firms like McKinsey to rethink how they balance efficiency, experience and credibility as AI becomes entrenched in client services.
