Topic
9 essays in this series
AI doesn't make the consulting pyramid more efficient. It puts the pyramid under structural pressure it was not designed to absorb. The firms that adapt earliest will not optimize the old model — they will begin replacing it.
The conversation about enterprise AI adoption has been dominated by technology questions. What has received less attention — and what is almost always the actual constraint — is organizational readiness.
The market has overweighted prompt engineering, treating the quality of the prompt as the primary determinant of AI output quality. It is not. The prompt is the input specification — output quality is determined by model quality, data quality, context quality, and evaluation rigor.
AI-native is becoming the kind of phrase that means everything and therefore nothing. The organizations that will benefit from AI as a structural competitive advantage are those that have built something more substantive than a portfolio of AI tools sitting on top of traditional processes.
Most data governance programs were designed to manage data at the pace of policy-making, not the pace of operations. In environments where AI systems are consuming and generating data continuously, that gap is becoming critical.
The traditional consulting leverage model has been stable for decades. AI is dismantling its base faster than the firms that depend on it are willing to acknowledge.
Output and judgment are being treated as if they exist on the same scale — as if producing more output at higher speed is equivalent to making better decisions. They are not the same thing.
AI does not replace work. It replaces specific cognitive tasks within work. Everything that requires judgment about what the output means and what to do with it remains human — and the enterprise response to AI has consistently blurred this distinction.
Every AI initiative runs into the same obstacle around six to eight weeks in, with the same expression of surprise. The models do not work the way the demos suggested — not because the technology is deficient, but because the data is not what the organization assumed it was.