In September 2022, two months before OpenAI introduced ChatGPT, I proposed a different operating model for consulting. The original post imagined a self-service platform through which clients could deploy a firm's research, frameworks, analytical models, diagnostics, benchmarking tools, bots and experts. Capabilities could be bought individually or in bundles. Revenue would combine access, usage and expert-support fees. Human involvement would fall as far as the client wanted.
The phrase I used was:
with as little human intervention as desired
Nearly four years later, much of that direction is recognisable, but the operating model I proposed remains incomplete. Looking closely at the gap is more useful than simply asking whether the prediction was right, because it reveals which parts of consulting can become software and which require a different commercial and organisational design.
What held
The central proposition was that AI would change both how consulting work was produced and what consulting firms sold. It could reduce delivery labour while allowing firms to package their accumulated methods and expertise into reusable products.
Both are now visible. HBR describes research, modelling and analysis as junior consulting work exposed to automation. The firms themselves are turning accumulated knowledge and delivery methods into reusable technology:
- McKinsey says Lilli searches and synthesises its knowledge base, and that versions of the underlying architecture are being adapted for clients.
- EY launched EY.ai after US$1.4 billion of investment.
- Deloitte describes Zora AI as role-based agents that can operate with client and third-party agents.
- IBM describes Enterprise Advantage as a consulting service built from reusable AI assets and expertise.
These are not identical products. Some are internal platforms. Some are services assembled around software. Some help the client build its own environment. But the movement is clear: consulting delivery is becoming more modular, repeatable and software-mediated.
The 2022 warning that this would be a "big lift" also held. Turning a people business into a hybrid product-and-services business forces decisions about what can be standardised, how tools connect to client systems, who approves automated output, how software revenue interacts with project revenue and what the firm is prepared to stand behind.
Those decisions require an operating-model change alongside the technical build.
What has not held, at least not yet
The original post went further. It proposed a consulting app store with subscription, usage and expert-support pricing. Major firms do not publicly break out material revenue from that model. Their product descriptions still combine technology with implementation, engineering, governance, change and expert support. Deloitte says a Zora AI implementation is generally measured in weeks or months. IBM calls Enterprise Advantage an asset-based consulting service, not a stand-alone product.
Reusable and modular delivery has become more visible than a material, self-service advisory business with the proposed economics. That may change. But there is an important difference between making expertise accessible through software and building a low-touch software business from expertise. The first is well under way. The second remains much harder to see at scale.
What I overstated
The 2022 post called the challenge existential. That was too broad if it meant the large firms themselves were likely to disappear. Accenture reported fiscal 2025 revenue of US$69.7 billion, including US$2.7 billion of generative- and agentic-AI revenue and US$5.9 billion of related bookings. One firm does not settle the future of an industry. It does show why "AI destroys consulting" is the wrong frame.
AI creates demand for strategy, implementation, workflow redesign, engineering, governance and change at the same time as it compresses parts of traditional delivery. The sharper threat is to the assumptions underneath the leverage model:
- That substantial junior labour is required to produce high-quality analysis
- That team size remains a reasonable proxy for value
- That generic frameworks stay proprietary
- That clients need to buy the whole engagement
- That the consulting firm keeps control of the client interface
The firms may grow while the model that built them changes underneath them.
What the original model missed
The phrase “human intervention” combined activities with quite different purposes. Producing an answer requires labour; acting on a consequential answer requires judgement and accountability. AI is reducing the production requirement faster than it is resolving who verifies the recommendation, decides that it fits the client and stands behind the conclusion.
A client can generate a market scan, acquisition screen, operating-model proposal or transformation plan in minutes. In low-consequence work, a useful first answer may be enough. A board recommendation, restructuring plan, regulatory remediation strategy or transaction thesis carries a different burden. Someone still has to decide whether the evidence is sound, whether the assumptions survive challenge, whether the recommendation fits the institution and whether the conclusion can be defended.
The document was the visible result of a larger process involving evidence gathering, interviews, challenge and judgement. In consequential work, an institution was also prepared to put its name behind the conclusion. As a plausible draft becomes inexpensive to produce, firms need to make the value of those surrounding capabilities explicit and price them accordingly.
Where accountability matters
Accountability is not the product in every consulting purchase. A systems migration may be a capacity and engineering problem. A benchmark may be valuable because the data are proprietary. An enterprise-platform implementation may depend on scarce technical skill.
The accountability argument is strongest where an executive must make and defend a consequential decision to a board, regulator, investor, court or workforce. In those situations, the client may be buying:
- A documented decision process
- Independent challenge to internal assumptions
- Verification by experienced practitioners
- Evidence and traceability
- Institutional credibility
- A named person prepared to defend the reasoning
Law and audit make the separation explicit. ABA guidance says lawyers using generative AI retain their duties of competence, confidentiality, supervision and candour. PCAOB standards leave the auditor responsible for forming and expressing an opinion. The tool can help produce the work. It does not inherit the professional duty attached to it.
The Five Scarcities
The 2022 logic was:
Analysis is becoming cheaper, so firms should distribute analysis more efficiently.
The better logic is:
Analysis is becoming cheaper, so firms must identify which surrounding capabilities remain scarce.
Call them The Five Scarcities. They are the layers most likely to remain valuable when the artifact becomes cheap.
Proprietary inputs. Data, benchmarks, case patterns and domain knowledge that a general-purpose model cannot readily reproduce from public information.
Reusable execution. Software, agents and workflows that perform defined work at lower marginal cost.
Client integration. The engineering and operating work required to connect the capability to the client's systems, controls, incentives and roles.
Verification. The evidence, evaluation, traceability and expert review required before the output can be trusted.
Accountable judgement. A person or institution prepared to interpret, endorse and defend the conclusion when the stakes justify it.
Different services will rely on different layers. A benchmark may live mainly in proprietary inputs. A managed workflow may depend on execution and integration. A regulatory assessment may depend on verification. A board recommendation may place most of its value in judgement.
Firms should assess the mix for each offering, automate repeatable production where the economics support it, and deliberately fund the integration, verification and judgement the client still needs.
The second thing I missed
The 2022 model assumed that the consulting firm would own the platform. IBM now explicitly helps clients build their own tailored internal AI platforms. Model providers and enterprise software companies are moving closer to deployment. The consulting firm can productise its expertise and still become one capability provider inside somebody else's environment.
That changes the strategic question. It is no longer:
How many applications can the firm assemble?
It is:
Which capabilities remain differentiated when the client or technology provider controls the platform?
The original prediction anticipated much of the direction of change, but it bundled together low-touch production, low-touch accountability and consulting-firm ownership of the client interface. Those are separate commercial propositions, and progress on one does not establish the other two.
For consulting leaders, the practical implication is to define what each offering actually provides: proprietary inputs, reusable execution, client integration, verification or accountable judgement. That determines what can be productised, what the firm is prepared to stand behind and which capabilities remain differentiated if the client or technology provider owns the environment. My original model treated the artifact as more of the product than it was; the revised model has to account for the full decision and delivery process.