Consulting has priced one thing for a century: skilled time. AI compresses skilled time. That single fact runs through every revenue and pricing development the 2026 edition of Spaik's State of AI in Consulting documents: AI work is growing as a share of firm revenue, clients increasingly want systems built rather than recommendations delivered, and fees are drifting from hours toward outcomes. This article sets out the disclosed numbers, the mechanism, and the four models emerging on the other side.
Key takeaways
- Gartner expects the AI services market to grow from $436 billion in 2025 to $759 billion in 2027, a 31.9% compound annual growth rate.
- BCG's AI- and technology-focused services rose from 20% of $13.5 billion revenue in 2024 to 40% of $14.4 billion in 2025. Bain reports about 30% of its 2025 business as technology- and AI-enabled, with more than 2,500 AI projects delivered. McKinsey reports a 60,000-strong workforce that includes 25,000 AI agents.
- Clients are shifting from buying advice to buying execution: systems built and running. AI is pushing them toward outcome-based fees, which weakens the billable-hours model.
- Four business models are emerging to replace daily and hourly rates: outcome-based, consulting-as-a-service, asset-based, and premium handmade consulting. Most firms will run more than one.
AI is taking a growing share of consulting revenue
The latest figures from the major firms point in one direction: AI is taking a growing share of their projects and their income. The market they compete in is expanding quickly at the same time.
| Firm or source | What is disclosed | As reported |
|---|---|---|
| Gartner[1] | AI services market size | $436 billion in 2025, expected to reach $759 billion in 2027 (31.9% CAGR) |
| BCG[2] | Revenue and share from technology advisory including AI | 2024: $13.5 billion, 20% technology advisory. 2025: $14.4 billion, 40% AI- and technology-focused services |
| BCG X | Headcount of the unit building custom AI systems for clients | More than 3,000 employees |
| Bain & Company[3] | Share of business that is technology- and AI-enabled | About 30% in 2025; more than 2,500 AI projects delivered to date |
| McKinsey & Company[4] | QuantumBlack headcount; total workforce | QuantumBlack: more than 1,700 staff. Workforce of 60,000 including 25,000 AI agents |
BCG's disclosure is the most telling because it is a mix shift, not just growth: the AI and technology share doubled in a year while total revenue grew far more modestly. That means AI work is not only additive; it is displacing what used to be sold as pure advisory. The revenue line looks healthy. What sits underneath it is a different product.
Clients are buying execution, not advice
The growing AI share reflects a change in what clients pay for. They are no longer buying advice alone; they want systems built and running. BCG's CEO Christoph Schweizer describes the firm's role in exactly those terms: redesigning entire workflows and upskilling organisations so that AI shows up in the P&L and in how people work, not just in token consumption, with a growing share of value-based projects.[2]
This is why the large firms built delivery units rather than practice groups. BCG X, QuantumBlack and their equivalents exist because a strategy deck about AI does not command the fee that a deployed system does, and because the client increasingly asks for the second before paying for the first.
Why the billable hour is weakening
The billable hour priced scarcity: a finite supply of skilled time, sold by the unit. AI makes the same deliverable take less time. If a firm keeps billing hours, its revenue per engagement falls even as its margin per hour rises; if it keeps the price, the client can see that the hours no longer justify it. Either way the unit of sale stops describing the value. The report's key finding is that AI is pushing clients toward outcome-based fees, which weakens the billable-hours model from the demand side as well.
McKinsey leadership indicated in November 2025 that roughly 25% of the firm's fees are now tied to outcomes rather than billable hours.[5]
Four emerging business models
A collective white paper published in 2026 by the HEC Paris Alumni Club Consulting & Coaching, and reproduced in Spaik's report, maps the move from today's daily or hourly rate to four models for the AI age.[6] Spaik did not produce this framework; we find it the clearest available summary of where pricing is heading.
The trade-off behind each model
| Model | What the client pays for | What the firm gains | What the firm takes on |
|---|---|---|---|
| Outcome-based | Actual results: savings, revenue increase, EBITDA impact | Alignment with the client and a premium when results land | Delivery risk moves onto the firm; measurement and attribution must be agreed up front |
| Consulting-as-a-service (CaaS) | A combination of experts, platforms and partial automations on recurring billing | Predictable, recurring revenue and a standing client relationship | Software-style obligations: uptime, maintenance, continuous improvement |
| Asset-based consulting | Reusable assets: automated dashboards, diagnostics, specialised agents, libraries | Margin that scales without headcount; IP that compounds | Upfront investment in assets that may date quickly as models change |
| Premium, handmade consulting | White-glove, no-AI advice based on unique, differentiated experience | Scarcity pricing for judgment that cannot be automated | Limited scale; the premium depends on expertise that is genuinely rare |
The models are not mutually exclusive, and the strongest firms will run a portfolio: asset-based tooling that makes outcome-based engagements deliverable at a margin, wrapped in a recurring service, with a small premium practice for the questions that are still genuinely handmade. What none of them preserves is the idea that the unit of sale is an hour. Firms that change the unit of sale before their clients force the change keep the pricing power; firms that wait discover their hours have been repriced for them.
What this means for consulting firms
Three consequences follow. First, the choice of business model now drives the choice of structure: a firm selling outcomes and assets needs the AI-fluent middle and the governance layer described in how AI is reshaping the consulting pyramid, not a wide base of billable juniors. Second, the revenue that AI creates is contested revenue: the labs, scale-ups and AI-native firms described in are AI labs becoming consulting firms? are competing for exactly the implementation work that is growing fastest. Third, and most practically, the shift from advice to execution means a firm's AI capability is now part of the product clients evaluate, which is why capability building has moved from an HR line to a commercial one.
This analysis expands chapter 3[7] of The State of AI in Consulting 2026.
Notes and sources
- Gartner, AI services market forecast: $436 billion (2025) to $759 billion (2027), 31.9% CAGR
- Boston Consulting Group, revenue and service-mix disclosures for 2024 and 2025; BCG X headcount; remarks by CEO Christoph Schweizer
- Bain & Company, statements on technology- and AI-enabled work as a share of 2025 business and AI projects delivered
- McKinsey & Company, statements on QuantumBlack headcount and total workforce including AI agents
- McKinsey leadership remarks on the share of fees tied to outcomes, November 2025 (as cited on Spaik's consulting page)
- ConseilIA: le nouvel âge du Conseil Augmenté, collective white paper, HEC Paris Alumni Club Consulting & Coaching, 2026 (source of the four-model framework)
- Spaik, The State of AI in Consulting 2026, chapter 3
Figures attributed to third parties are their own reported data; Spaik did not produce those statistics. Where the text offers an interpretation, it is Spaik's own.
Continue reading
- The State of AI in Consulting 2026The full report this analysis is drawn fromRead
- How AI is reshaping the consulting pyramidThe structure that follows from the modelRead
- Are AI labs becoming consulting firms?The new competitors for the same revenueRead
- AI advisory and implementationRedesigning the engagement model with SpaikRead
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