Why AI will not kill strategy consulting — but it will democratise it for the 99% who currently don't buy
Imagine you are a Chief Strategy Officer at a major company. You have just commissioned a consulting engagement. Twelve weeks. Eight slides per workstream. A senior partner on the cover page. The bill will land somewhere north of two million dollars. Now ask yourself: what exactly are you buying?
The honest answer — is that you are mostly buying time. Analyst time spent building models. Associate time spent writing and rewriting slide commentary. Manager time spent coordinating across workstreams. Partner time spent reviewing, challenging, and presenting. You are buying human hours applied to the task of gathering information, making sense of it, and packaging it into something a board can act on. The outcome you really want is change.
That model is not broken. But it is suddenly, deeply exposed. Because the thing that made human hours the only way to do this work — the scarcity of the underlying capability — no longer applies in the way it once did.
The business model was always built on scarcity
To appreciate what is changing, you have to appreciate what the major strategy firms actually built. McKinsey, BCG, and Bain did not simply commercialise advisory services when they were founded across the mid-twentieth century. They created the intellectual architecture of modern business strategy itself.
BCG gave companies a formal vocabulary for thinking about competitive advantage and portfolio allocation. McKinsey institutionalised the idea that business problems could be decomposed into mutually exclusive, collectively exhaustive components — and that this kind of structured thinking was worth paying significant sums to access. Bain contributed frameworks for governance and decision-making that companies adopted as permanent operating procedure.
These were not incidental deliverables. They were the foundations of a knowledge economy. And the economic engine underneath them was a simple and powerful idea: that the best strategic thinking in the world was concentrated in a small number of firms, and that the only way to access it was to pay those firms for their people's time.
The core transaction was one of information arbitrage. A consulting firm would observe a successful practice at one organisation, generalise it into a transferable principle, and then sell that principle — adapted for local context — to the next client. Multiply this across hundreds of engagements per year, across dozens of industries, and the accumulated pattern-recognition inside a top consulting firm became genuinely difficult to replicate. The moat was real.
It was also always contingent on a particular constraint: that producing high-quality strategic analysis was slow, expensive, and required rare human expertise. Remove that constraint, and the entire pricing logic starts to wobble.
Seven things consulting sells — and four that are now in trouble
If you break down what a major strategy engagement actually provides, you can identify seven distinct types of value. Some are deeply human, relational, and hard to replicate.
Others are, it turns out, surprisingly mechanical.
The first is pattern transfer — taking what worked at a comparable company and applying it to yours, with enough contextual adjustment to make it feel bespoke.
The second is competitive benchmarking — giving you an externally validated read on how your performance, cost base, or operating model compares to peers you would never otherwise have visibility into.
The third is the ability to rapidly synthesise large volumes of information — market data, expert interviews, academic literature — and distill it into a coherent analytical view.
The fourth is surge capacity — the ability to deploy a skilled analytical team on a specific problem without carrying the fixed cost of having those people on payroll permanently.
The fifth is political cover — the external validation that helps a CEO or board sponsor a difficult decision without appearing to have arrived at the conclusion alone.
The sixth is implementation muscle — the hands-on support that turns a strategic recommendation into an operational change with people.
And the seventh is a kind of institutionalised candour — the willingness of an external party to say things that no internal stakeholder is positioned to say without career risk.
Here is the uncomfortable truth: at least four of those seven are now structurally exposed to AI disruption. Pattern transfer, competitive benchmarking, research synthesis, and surge analytical capacity — the four that have historically accounted for the largest proportion of consulting revenue — are precisely the functions where AI capability is advancing fastest. A large language model trained on decades of business literature does not need to have conducted fifty previous engagements to recognise that a particular cost structure looks unusual for the industry, or to know what the leading companies in a sector have typically done to solve a given class of problem.
The remaining three — political cover, implementation support, and the courage to speak uncomfortable truths — are fundamentally human in character. They depend on trust, relationships, and the social dynamics of organisations. AI does not erode these. But they alone cannot sustain a billing model premised on the cost of analytical labour.
The incentive structure that nobody talks about openly
There is a structural problem baked into the consulting model that has persisted for decades largely because clients lacked an alternative. The economics of time-and-materials billing mean that a consulting firm's revenue is directly proportional to the length of the engagement — not to the quality or speed of the insight produced. A team that could theoretically complete an analysis in three weeks has no financial incentive to do so if the engagement is scoped for twelve.
This creates a quiet tension at the heart of every consulting relationship. The firm's incentive is duration (if not one, then potentially over multiple engagements). The client's interest is resolution and outcome. The gap between those two things has historically been papered over by the genuine scarcity of the underlying capability — if there was no other way to get the analysis done, the pricing was the pricing.
That justification is weakening. The cost of a two-month senior consulting engagement is comparable to the annual fully-loaded cost of a sophisticated AI capability inside a strategy function. When a Chief Strategy Officer can ask that question explicitly — not as a hypothetical but as a live procurement comparison — the conversation in the boardroom starts to shift.
There are other fault lines too. Consulting engagements are notorious for producing analysis that is handed over at the end of a project and then slowly forgotten, because the people who built it leave and the people who commissioned it move on. The recommendation and the implementation live in different worlds. The slide deck lands. The organisation changes, or it does not. Nobody measures which.
The artefact is not going away — but it is about to change fundamentally
At this point it is tempting to reach a particular conclusion: that AI will eventually displace the need for formal strategic outputs altogether. That the board presentation, the investment memo, the competitive benchmarking report — all of this will become obsolete in a world where answers can be queried conversationally from an AI system.
This conclusion is wrong; Senior decision-makers — boards, investment committees, management teams — do not just need information. They need a mechanism for shared deliberation and considered decision making, even when more operational decisions are taken by machines.
A document, a dashboard, or a presentation creates a common object that a room full of people can examine, challenge, and commit to together. It freezes a view of the world at a specific point in time, assigns accountability for that view, and creates a record against which future decisions can be measured as a point of departure. That function is not a relic of pre-digital working practices. It is a fundamental feature of how organisations make consequential decisions.
What AI changes is not the need for that artefact — it is almost everything else about how the artefact gets made. The time required to produce a rigorous competitive analysis drops from weeks to hours. The depth of research that can be embedded in a single document increases by an order of magnitude. The number of scenarios that can be modelled before a document is finalised expands dramatically. And — most significantly — the artefact itself begins to shift from something static to something that can be kept alive.
A traditional strategy document begins depreciating the moment it is printed. The market moves. Competitors respond. Macro conditions shift. Two months later, the benchmarking data at the back of the deck is already partially obsolete. What AI enables — and what the most forward-thinking organisations are beginning to experiment with — is a different kind of strategic output: one that updates as the underlying data changes, rather than capturing a single frozen moment and then slowly ageing in a shared drive.
Three ways AI is actually entering the market right now
The transition is not happening all at once. Observing how organisations and advisory firms are actually adopting AI in strategy work, a clear sequence is emerging — three distinct waves, each more structurally significant than the last.
The first wave is what you might call the embedded tool. AI enters the workflow at the level of the individual document or application — an LLM panel inside PowerPoint that reformats a slide on command, populates a data table, generates a chart from a brief description, or adjusts a layout based on a verbal instruction. This is not strategic transformation. It is friction removal. But it is real, it is already happening, and its effect on analyst and associate productivity is measurable. Think of it as the modern AI equivalent of Thinkcell — a tool that does not change the fundamental nature of the work, but eliminates a meaningful slice of the low-value mechanical effort involved in doing it.
The second wave is the strategy platform — an AI-native environment that unifies the full consulting workflow under one roof. Research, framework application, synthesis, scenario modelling, document generation, and collaborative review all happening within a single integrated system, rather than being stitched together across email threads, shared drives, PowerPoint files, and browser tabs. The analogy here is what Harvey and Legora have done for legal work — creating a coherent operating environment for knowledge workers that treats AI as a genuine collaborator rather than an add-on. The equivalent for corporate strategy does not yet have a dominant player, but the category is clearly forming. When it matures, the effect on consulting economics will be substantial: the same level of analytical output will require significantly fewer hours of human effort.
The third wave is the one that represents a genuine change in kind rather than degree. Call it the dynamic dashboard — a shift from producing point-in-time strategic analysis to maintaining continuous strategic intelligence. Rather than commissioning a competitive landscape review every twelve to eighteen months, an organisation builds a system that monitors competitive signals, market data, and operating performance on an ongoing basis, surfacing relevant changes and recalibrating the strategic picture in real time. The board does not review analysis that was produced last quarter. It queries a living system that has been watching the market every day since the last meeting.
This third wave is the most consequential because it does not just make the existing model cheaper — it makes a fundamentally different model viable. The shift from episodic analysis to continuous intelligence is the equivalent of moving from commissioning a bespoke weather report every six months to having a permanently running forecast. One of those is a deliverable. The other is intelligent strategic infrastructure for company to make better, faster decisions.
What this means for the firms, and for their clients
None of this means that the major strategy firms are going away. The relational and political dimensions of their work — the ability to build trust with a CEO over years, to speak uncomfortable truths in a board setting, to mobilise a client organisation around a difficult change — these capabilities are not going to be automated. They depend on human judgment, social credibility, and institutional relationships that take decades to build.
But the economics of the model are going to have to change. Billing for analyst hours spent producing research that AI can now produce faster and more comprehensively is not a sustainable value proposition. The firms that adapt will be those that redraw the line between what they do and what their clients can now do for themselves — concentrating their human effort on the genuinely irreplaceable parts of the work, and rebuilding their commercial model around outcomes rather than time.
For clients — particularly Chief Strategy Officers and their teams — the implication is almost the reverse. The risk is not that you will pay too much for consulting. The risk is that you will continue to outsource strategic intelligence that you now have the tools to own. An internal strategy function equipped with AI-native workflows, connected to live market and competitive data, and capable of maintaining a continuously updated view of the business environment is not a replacement for external advisory. But it is a fundamentally different starting point for the conversation — one in which the client arrives at the table with a richer, more current picture of the landscape than has historically been possible.
The organisations that grasp this transition earliest will not just save money on consulting fees. They will make faster, better-informed decisions. They will catch competitive movements earlier. They will spend less time in the quarterly planning cycle trying to reconstruct a picture of the world from data that is already three months old. The strategic advantage of continuous intelligence over episodic analysis is not a marginal efficiency gain. It is a different way of developing and executing strategy.
The real question is not whether to use AI — it is what kind of strategy function you want to be
The debate about AI in consulting tends to get framed as a question about cost: can AI do this work more cheaply? That framing misses the more important question, which is about capability and speed. The issue is not just that AI makes the existing type of strategic analysis less expensive. It is that AI makes a type of strategic analysis possible that was not previously viable at all — analysis that is continuous, connected, and self-updating rather than periodic, packaged, and perishable.
For most of the past century, even the best-resourced organisations could only afford a few strategic snapshots per year. The cost and complexity of producing rigorous analysis meant that strategy was, by necessity, an intermittent activity. The rest of the time, executives were flying on instruments — using intuition, incomplete data, and the residual insight from the last engagement to navigate a world that had already moved on.
That constraint is lifting. The question for every organisation is whether they are going to treat this as an opportunity to reduce their consulting spend, or as an opportunity to raise the quality of their strategic decision-making to a level that was previously out of reach. The firms and strategy functions that ask the second question — and build accordingly — are the ones that will look back on this moment as the point at which they permanently changed how the organisation thinks.
The engagement model had a remarkable run. What replaces it will start being a cheaper to deliver version of the same thing, with the benefits captured initially by incumbents. The market will then expand as the 99% of underserved enterprises get access too. A democratisation of insight, planning and change. And the product will start to change too, from being a project you commission, to a live output that is used by humans and AI agents.
At Calvyn we are seeking to change the way the world lives and works by empowering organisations with the AI to elevate their work.
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