A New Model for Consulting Companies
Advisory consulting firms must shift their center of gravity toward AI-focused transformation to stay competitive in a rapidly evolving market. As AI makes analytical and domain-specific expertise more accessible, pure advisory will become an increasingly difficult place to build a business. Conversely, the act of embedding AI into an enterprise’s fabric (not just in enterprise technology, but also the company’s culture and organization structure) is where a large portion of next decade’s consulting revenue will reside.
However, many advisory firms are not built to do it and must evolve their business model.
Consulting Industry Will Continue to Thrive Through AI Paradigm Shift…
In a 2025 WSJ article, a McKinsey partner described AI as existential to the consulting industry (but in a good way). Other more casual observers on social media have predicted that AI will lead to the demise of the consulting industry.
I don’t believe that to be the case. The industry has grown through prior technology paradigm shifts. Each new paradigm (e.g., mainframes, ERP, internet, SaaS) opened a gap between what the technology promised and what a company could absorb. I expect the same to be true with AI.
Indeed, we are already starting to see some evidence. Accenture’s “advanced AI” (excluding classical AI and data work) revenue tripled in FY2025 to $2.7B. Similarly, in April 2026, BCG reported that AI and tech-focused revenues grew by 25%. In both cases, overall revenues for both companies increased by 7%, suggesting that AI was not cannibalizing existing revenue lines.
However, there is also a recognition that AI will be the primary driver of growth in the industry — Market Data Forecast estimates that the global AI services market is at $30B today, and is expected to grow at a CAGR of ~36% through 2034, versus ~4% growth for the global business management consulting services industry.
Transition to AI Services Is Stressing Existing Models
However, the transition to AI services will introduce multiple stressors to how consulting services are delivered today.
Advising versus Building
AI is moving where the scarcity sits, and the value with it. Advisory work used to rest on deep vertical knowledge, teams grinding through large data sets, and drafting the recommendation. A competent analyst with the right tools can now do most of that in an afternoon.
Meanwhile boards and management want their companies to become AI Native. That means deploying agents, consolidating fragmented data, redesigning workflows, and managing the organizational change that follows. None of it is advice. Therefore, the bottleneck is shifting from “knowing what to do” to helping companies “build it.”
Competitive Landscape Is Becoming More Crowded
The second change is competitive. Historically, different types of firms served different segments of the consulting value chain: pure advisory, transformation, and technology implementation. As the bottleneck shifts away from pure advisory on AI services, those lines are starting to dissolve. Advisory firms are building engineering and deployment arms. We are seeing this in the uptick of job postings at leading advisory firms for data scientists and AI engineers. Simultaneously, system integrators are selling AI strategy work.
Product companies and startups are crowding in this space from the other direction. The frontier labs have moved into enterprise services directly, partnering with consultancies and PE firms to get there faster. Anthropic announced a $1.5B joint venture on May 4, 2026 with Blackstone, Hellman & Friedman, and Goldman Sachs, now operating as Ode. A week later, OpenAI launched the Deployment Company, a majority-owned subsidiary backed by more than $4B from 19 investors, including McKinsey, Bain, and Capgemini. Venture-backed firms like Distyl AI are selling forward-deployed engineering into the same accounts.
Everyone is converging on the same space from different directions.
Different Delivery Model
The third change is to the delivery model itself.
The consulting pyramid was a staffing model and a training pipeline at the same time, with a wide base of analysts doing research, analysis, and slide production. Managers in the middle coordinated the work and supported a thin layer of partners, who held relationships with the clients, on the top.
AI has either taken or accelerated a lot of the work done at the base. McKinsey’s Lilli handles 500,000 prompts a month and cuts research time by about 30%; BCG’s Deckster drafts and polishes decks.
Consequently, graduate consulting postings fell 44% year over year through 2024, and roles requiring fewer than three years of experience dropped from 41% to 26% of openings.
In other words, as HBR put it, the pyramid is becoming an obelisk.
Fee structure is also moving with the team structure, because it has to. Teams are moving from day rates to outcome and recurring based fee structures. McKinsey now books 25% of global fees on outcome-based contracts. Accenture’s managed services account for 54% of new bookings ($44.2B against $37B from consulting).
What This Means
Advisory firms that want to stay relevant will have to build capabilities they don’t have: technical delivery, implementation, and probably managed services. That changes who they hire, how they price, and how they pay their partners.
Revenue will shift from large upfront project fees toward something recurring — closer to how SaaS software companies price than how consulting firms bill. That transition is already visible in the numbers above: 54% of Accenture’s new bookings, a quarter of McKinsey’s global fees. Partner compensation has to follow, rewarding retained revenue rather than in-year origination. Firms that change the pricing model without changing the compensation model will find their partners quietly selling the old thing.
The talent model is the harder version of the same problem. Fewer analysts means a smaller pipeline for managers. It also means fewer partners in a decade, because the pyramid trained people as a byproduct of billing them. No firm has a working replacement, and under fee pressure this is an easy decision to defer for now.
And there’s a credibility problem. Firms that spent decades advising on transformation without ever delivering it will have to prove they can execute. Clients will ask the reasonable question: why should I trust you to build something you’ve never built?