A New Kind of Interview
In select final-round interviews, McKinsey has asked graduate candidates to use its in-house AI assistant, known internally as Lilli, to work through a consulting case study with the tool’s help. Candidates are evaluated not merely on technical AI expertise, but on their ability to prompt the chatbot effectively, assess its outputs, and translate those outputs into a structured response that reflects judgment, contextual reasoning, and communication skills. The exercise is meant to mirror how consultants actually collaborate with AI on client projects rather than to screen for coding ability.
This so-called “AI interview” is embedded within McKinsey’s notoriously demanding recruitment process, alongside traditional assessments of structured problem-solving, personal impact, leadership, and values. Evaluators are looking for applicants who display curiosity and critical judgment when working with AI assistance, in essence, those who can treat the tool as a thinking partner rather than a shortcut.
Why This Matters
McKinsey’s experiment underscores a broader transition in professional services hiring: AI fluency is moving from optional to foundational. While the firm still values classic consulting skills, analytical rigor, structured communication, and leadership potential, it now actively examines how candidates work with real-world AI systems, anticipating that future consultants will use such tools daily.
The shift also reflects industry trends where major consultancies are embedding AI deeply into operations. McKinsey has dramatically expanded its internal deployment of generative AI assistants, growing the number of AI “agents” deployed across the firm by hundreds of percent in a short span, and expects further integration into its workflows.
Implications for Candidates and the Consulting Model
For applicants, this adjustment signals that mastering how to collaborate with AI, not just understand it theoretically, may be as important as traditional case interview preparation. Candidates who can demonstrate nuanced prompting, critical evaluation of AI outputs, and thoughtful synthesis are likely to stand out in this new assessment environment.
From an organizational perspective, McKinsey’s move also offers a window into how consulting work is transforming. As AI takes on more routine research and analytical tasks, human consultants are expected to focus more on strategic judgment, client context, and higher-level communication, skills that are harder for AI to replicate. This rebalancing is likely to drive changes not only in hiring but also in performance expectations and career progression models within firms.
A Broader Industry Signal
McKinsey isn’t alone in exploring AI-enhanced hiring. Other firms and industries are experimenting with AI tools in candidate assessments, though approaches vary widely. Some companies emphasize independent problem-solving with AI support while others integrate AI probes deeper into technical screens or leadership evaluations. The broader question for employers now is how to calibrate AI use in ways that complement human judgment without introducing bias or diluting core competencies.
Maya Lindqvist is not a person. No notebook, no deadlines, no face behind the name — just a byline this newsroom publishes under. Here is the production line underneath it, because a name beside a portrait reads like a journalist, and this one is not one.
The models. Writing: gpt-5.6-luna and qwen3-max. Out on the live web: gpt-5.6-luna and gpt-5.6-terra. Pictures: gpt-image-1 and gpt-image-1-mini. Swap one in the newsroom and this line swaps with it — it is read off the machines, not typed here.
How a story is made
- Research. The searching model reads around the story, pointed at primary sources — the filing, the post, the repository — rather than at somebody else's write-up of them.
- Writing. The writing model drafts it against what was found, at Maya Lindqvist's usual length and in Maya Lindqvist's usual register.
- The loop. A reviewer reads the draft and sends it back with notes. Then reads it again. A piece can go round several times before it leaves the building.
- Enrichment. A quotation has to appear word for word on the page it is taken from. A chart may only use figures that appear in the source it cites. Whatever fails is dropped, and the reason is kept.
- Fact check. A last pass hunts for claims the article makes and its sources do not.
- A human stop. Sensitive subjects are held for a person to read before publication, and a person can kill any of it at any point.
If that sounds less like a newsroom and more like a factory: quite. It is called Press Factory.
This article was generated using AI and published automatically without human pre-publication review.
How this article was made
The article was produced by the Grandmonts Media News Engine using automated research, drafting and verification workflows. No human editor reviewed the article before publication. Grandmonts Media remains responsible for the published content. Errors can be reported at office@grandmonts.cz.