For decades, the architecture of corporate decision-making rested on a reliable, if expensive, foundation: when a bank needed strategic direction, it called McKinsey; when it needed market intelligence, it called Gartner. That arrangement — built on proprietary data, exclusive access, and the premium of human expertise — is now being interrogated as artificial intelligence reshapes the economics of knowledge itself. The question being asked in boardrooms from Frankfurt to Singapore is no longer simply whether AI can do what these firms do. It is something more unsettling: whether the very need for that kind of institutional intermediary is dissolving.
The debate has grown loud enough to be inescapable. Commentators across the financial and technology sectors have framed artificial intelligence as a direct competitor to the large consulting and research houses — capable of synthesizing vast datasets, generating strategic scenarios, benchmarking competitors, and delivering actionable frameworks in a fraction of the time and at a fraction of the cost. That framing is not wrong. AI systems today can produce outputs that, in structure and breadth, resemble the deliverables that teams of highly paid analysts once spent weeks assembling. The disruption at that operational layer is real and accelerating.
But according to Chris Skinner, the influential fintech commentator and author behind The Finanser, this framing fundamentally misses the point. The argument that AI will replace McKinsey and Gartner treats the problem as one of substitution — as if the central issue is whether a machine can perform the same task as a consultant at lower cost. Skinner's position, as articulated in his recent analysis, is that this conversation is looking at the wrong level of change entirely. The bigger shift is not about replacement. It is about the structural conditions that made those firms indispensable in the first place — and whether those conditions still exist.
That is a distinction worth pausing on. McKinsey built its authority not merely on analytical capacity but on something harder to replicate: the asymmetry of information. It had access to cross-industry pattern recognition, proprietary benchmarking data, and a network of relationships that no single client organization could assemble independently. Gartner's power rested on similar dynamics — the aggregation of research across thousands of technology vendors and enterprise buyers, distilled into frameworks like the Magic Quadrant that became industry standards. Both firms monetized, in essence, the gap between what their clients knew and what they needed to know.
Artificial intelligence does not merely close that gap incrementally. It challenges the economic logic that created the gap in the first place. When a Chief Information Officer at a mid-tier European bank can instruct an AI system to survey competitive positioning across a peer group, synthesize analyst reports, map regulatory risk, and model strategic options — all within a single working session — the informational asymmetry that justified a seven-figure consulting retainer begins to look structurally different. The question is not whether the AI output equals a McKinsey deck in prestige. The question is whether the bank's decision-making improves enough, quickly enough, that the marginal value of the traditional engagement collapses.
For financial institutions specifically, these dynamics carry particular weight. Banking has always been among the most information-intensive industries on the planet, and it has historically been one of the most generous consumers of high-end consulting services. Regulatory complexity, digital transformation pressures, and the perpetual need to benchmark technology investments against peers created a steady and lucrative demand for precisely the kind of structured, externally validated analysis that McKinsey and Gartner delivered. As AI tools become embedded in the operating infrastructure of banks and fintechs, that demand does not disappear — but its shape changes. Organizations may still want external validation and human judgment. They may be less willing to pay for the underlying research assembly that AI can now automate.
The more nuanced reading of this disruption — and the one that Skinner's framing gestures toward — is that the firms which survive and remain relevant will be those that understand what AI cannot easily replicate: genuine contextual judgment, accountability, relationships built on trust over time, and the ability to navigate organizational politics in ways that no language model is equipped to manage. A board of directors under regulatory scrutiny does not simply want an answer. It wants an answer it can defend, delivered by a named institution prepared to stand behind it. That dimension of the consulting relationship has no obvious AI equivalent — yet.
What This Means for the Industry
The disruption of the management consulting and research sector by artificial intelligence is best understood not as a simple substitution event but as a structural repricing of where human expertise adds irreplaceable value. For banks and financial institutions evaluating their advisory and research spending, the practical implication is a coming bifurcation: commodity research and analytical groundwork will increasingly migrate to AI-native tools, while genuinely high-stakes, high-accountability advisory engagements may retain their human premium — though at a smaller scale and a higher bar. Firms like McKinsey and Gartner are not facing extinction. They are facing the far more demanding challenge of proving, in an environment where information asymmetry is eroding rapidly, exactly what they are worth.
Written by the editorial team — independent journalism powered by Codego Press.