Ex-McKinsey consultants are training AI models to replace them (www.bloomberg.com)

A recent report says that former McKinsey consultants are building and training AI models designed to automate parts of management consulting work — from data analysis and hypothesis testing to slide decks and strategic recommendations. Leveraging their domain knowledge, these ex-consultants are assembling curated datasets, codifying frameworks (e.g., market sizing, competitive analysis), and fine-tuning large language models with human-in-the-loop supervision so the models can produce client-ready outputs much faster and at lower cost than traditional billable-hour engagements. This trend matters because it accelerates white‑collar automation in a high-margin professional services sector and forces a rethink of value propositions: firms can scale expertise, offer on-demand analytics, and cut delivery time, while consultants shift toward oversight, model validation, and relationship management. Technically, builders combine supervised fine-tuning, RLHF, retrieval-augmented generation (embedding vectors + vector DBs), and structured prompting or chain-of-thought templates to reproduce consulting workflows. Key risks and implications include client confidentiality and IP handling, quality control and explainability of model recommendations, and competitive pressure on legacy consultancies to adopt similar toolchains or specialize in high-touch advisory that AI can’t replicate.