The Great Repackaging
McKinsey, BCG, Bain, Deloitte, and Accenture have all launched dedicated AI advisory practices. Goldman Sachs is advising clients on AI strategy alongside M&A. Every major consulting firm now has an "AI Transformation" offering.
Here is the problem: most of these practices are staffed by strategy consultants who have repackaged existing frameworks with AI terminology. The maturity model is the same. The change management methodology is the same. The deliverables are the same. Only the buzzwords have changed.
This matters because AI advisory requires something that traditional consulting firms structurally lack: hands-on experience building and shipping AI systems at scale.
The Knowledge Gap Is Real
Understanding AI strategy requires understanding AI systems. Not theoretically, but practically. When a partner at a major consulting firm presents an AI roadmap, can they answer:
- What is the latency implication of adding a retrieval-augmented generation layer to this workflow?
- How should you structure your evaluation dataset for a domain-specific fine-tune?
- What are the failure modes of chaining multiple LLM calls, and how do you build resilience against them?
- When does it make sense to use Claude 3.5 Sonnet versus a fine-tuned Llama 3 variant for this use case?
These are not academic questions. They directly impact the feasibility, cost, and timeline of any AI initiative. Advisors who cannot engage at this level are guessing, and their clients are paying premium rates for those guesses.
What Enterprises Actually Need From AI Advisors
Technical depth paired with business judgment. The value of advisory is not in telling a company that AI is important. Everyone knows that. The value is in helping them make specific, high-stakes decisions: which use cases to prioritize, what architecture to adopt, whether to build or buy, which vendors to trust, and how to structure the team.
Operating experience, not theoretical frameworks. An advisor who has scaled an ML platform from prototype to serving millions of users brings pattern recognition that no amount of research can replicate. They know which shortcuts work, which corners cannot be cut, and what breaks at scale.
Independence from implementation revenue. The biggest conflict of interest in AI advisory is that the firms giving strategic advice are the same ones selling implementation services. When your advisor's revenue depends on you building a complex custom solution, their objectivity on the build vs. buy question is compromised.
The Market Is Shifting
We are seeing a growing segment of enterprise buyers who have been burned by traditional consulting AI engagements. They paid seven figures for a strategy document and a roadmap, then discovered that the recommendations were technically naive or vendor-influenced.
These companies are increasingly seeking advisors who combine:
- Experience at top-tier companies (the pattern recognition of having operated at scale)
- Deep technical fluency (the ability to evaluate architecture decisions, not just business cases)
- Independence from implementation (no incentive to recommend complexity)
The AI advisory market is going to be enormous. The question is whether it will be dominated by repackaged management consulting or by a new category of technically-fluent strategic advisors. We have a clear view on which model creates more value.