consulting automation AI-training professional services disruption

Consultants Train the Machines That Might Replace Them - A Shift in the Industry’s DNA

Posted on October 31, 2025 at 10:07 PM

Consultants Train the Machines That Might Replace Them: A Shift in the Industry’s DNA

Imagine you’re a consultant at the prestigious firm McKinsey & Company. You’ve spent years mastering frameworks, managing teams, preparing strategic recommendations. But now you find yourself teaching an AI model to do your job. According to recent reporting, former McKinsey- and similar-firm consultants are being contracted to train AI systems that could one day replace the very work they once did.

The Story & Key Facts

  • The article reports that ex-consultants from top strategy and management-consulting firms are being hired to train AI models. These individuals bring domain-expertise in consulting tasks such as market analysis, slide-deck production, data interpretation and client-interfacing. (linkedin.com)
  • These consulting veterans are, in effect, helping build the next generation of tools that aim to automate large chunks of consulting work. This move signals a disruptive moment for an industry long insulated behind human expertise. (x.com)
  • The broader implication: If AI can absorb the routines of problem-scoping, data-synthesis and slide-creation, then consulting firms face a dual challenge: persuading clients of the value of human advisers, and protecting their revenue model from automation. The article sees this as not just an internal efficiency play, but a structural shift. (afr.com)

Why It Matters

For you, Sheng, with a deep background in AI, data science and analytics:

  • This story is a concrete example of what happens at the intersection of expertise, automation and value creation. Consultants are often subject-matter experts; handing that expertise off (via instructing models) is directly relevant to how we think about human vs machine roles.
  • It signals that even high-value, knowledge-intensive professions are vulnerable—especially those with repeatable frameworks or patterns. For someone who builds intelligent systems (you are building ERPs, email AI, trading systems…), this reinforces the urgency of focusing on non-routine, creative, strategic tasks that are harder to automate.
  • For the consulting and professional-services sector more widely, this could mean new kinds of partnerships: humans as trainers, curators, supervisors of models instead of pure executors. You might think of how this maps onto your own work in email-processing, smart dashboards, etc: moving from building to training/trust governance.

Lessons & Implications

  • Role evolution: Consultants may need to evolve from being the doers of analysis and decks to being architects, trainers, or model curators. The value shifts from execution to design of tools, oversight of automation and strategy of deployment.
  • Value chain risk: Breakdown of consulting work into components makes some parts vulnerable—especially data ingestion, slide generation, templated recommendations. The parts that remain human-centric will likely be higher up: judgment, leadership, change-management.
  • Talent shift: The firms themselves may start hiring AI-savvy ex-front-line experts not just to consult but to build the automation that might replace consulting work. That means people with deep domain and data fluency may be the winners.
  • For your sector (AI & data science): This reinforces the importance of building systems that don’t just replace tasks but augment human judgment, or train humans or models. Your work on intelligent email routing, using LangChain/Ollama, smart dashboards—all aligns with this shift from manual to hybrid and autonomous models.
  • Consulting firms’ business model: With automation creeping in, firms must rethink how they package value—less “hours of human labour” and more “outcomes enabled by human + machine.” That will pressure fee models, margins and staffing.

Glossary

  • Model training: The process of feeding data and instructions to an AI system so it can learn patterns and eventually perform tasks previously done by humans.
  • Automation of knowledge work: Using software or AI to perform tasks that involve analysis, decision-making or creation of work products (like reports or presentations) which were traditionally done by humans.
  • Domain expertise: Deep knowledge of a specific professional field (such as management consulting) which gives insight into workflows, frameworks and client needs.
  • Hybrid human-AI workflow: Workflows in which humans and AI systems collaborate—humans might oversee, guide or audit, while AI does repetitive tasks.
  • Value chain disruption: When the components of how a service is delivered (such as consulting) get altered by new technology, shifting who does what, how revenue is earned and what skills are required.

Final Thoughts

The image of consultants teaching models to take over their roles is both dramatic and instructive. It shows that no profession is immune from the wave of automation — especially when tasks are repeatable, structured and rooted in frameworks. For high-skilled professionals, the future lies in leaning into what machines can’t easily do: navigating ambiguity, exercising judgment, building trust, and designing systems and processes. As someone working deeply in AI, your vantage is powerful: you’re not just a spectator but a builder of this shift. The question isn’t if the consulting world will change, but how you position yourself in that change—whether as the human overseeing the machines, or the architect of the machines themselves.

Source: https://www.afr.com/world/north-america/ex-mckinsey-consultants-training-ai-models-to-replace-them-20251031-p5n6p1

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