It has been three years since AI started reorganizing enterprise budgets, and the picture has clarified enough to set out in full. Five distinct delivery models are competing for the same dollars, and none of them is yet the dominant winner. Who actually delivers production AI to the businesses that need it most is still an open question.
The five delivery models, named
The five models are worth naming explicitly, because most conversations in this space proceed as if there are only two or three.
The first is Big-4 transformation. McKinsey QuantumBlack, Accenture's AI practice, Deloitte AI & Data, BCG X. The shape of the engagement is familiar: a partner-led roadmap, opportunity prioritization workshops, a pilot proposal at month six. The deliverable is a recommendation. The customer they sell to is a global enterprise with an in-house technology function and an offshore engineering capacity that can take the recommendation forward.
The second is the AI boutique. Faculty in the UK, Mosaic, Plan A Technologies, a growing class of fifteen-to-fifty person firms staffed by ML engineers who will build something custom. The model is bespoke. You describe the problem precisely, they spec a system, six months later they hand it back. The customer is a tech-forward leader who can specify the problem precisely, has internal engineering to maintain the result, and can wait six months.
The third is the bolted-on SaaS copilot. Microsoft Copilot for Microsoft 365. Salesforce Einstein. Glean, Writer, Notion AI. The model is software-shaped: per-seat fee, generic AI capability layered on top of an existing application. The customer is one whose primary work happens inside the application that gets the copilot. This works well for documents, decks, and spreadsheets. It works less well for the work that happens between systems, which is most of the work that needs automating.
The fourth is the in-house AI team. Hire two ML engineers and an MLOps person, give them eighteen months, deploy something. This is the model that actually works at scale, which is why the largest companies all default to it. It assumes a customer who can hire ML engineers at four-hundred-thousand-dollar fully-loaded compensation, can offer them a problem interesting enough to choose your insurance brokerage over a frontier lab, and can wait eighteen months for the first thing to ship.
The fifth and newest is the AI-enabled roll-up. Buy a fragmented services market at services multiples, deploy AI to compress costs, then exit at software multiples. General Catalyst has dedicated $1.5B of the $8B fund it raised in late 2024 to its creation strategy, which incubates AI-native software companies in specific verticals and uses them as acquisition vehicles to buy established firms. According to Newcomer's reporting on the strategy, Thrive Capital has a dedicated vehicle, Thrive Holdings, with more than $1 billion at its disposal, and Lightspeed partners have made roll-up plays in engineering services and healthcare.
None of the five fits the largest segment
Each of these five models is well-shaped for a customer it serves well. None of them is well-shaped for the business that needs production AI but does not have an AI team, and that describes most of the market: the Census Bureau's Business Trends and Outlook Survey put overall AI use at 17 to 20 percent of US businesses between December 2025 and May 2026, with adoption concentrated in the largest firms.
How the Big-4 firms are restructuring under AI pressure
The Big-4 firms are restructuring around AI, and the pace shows in their own numbers. McKinsey now reports 25 percent of projects priced against outcomes rather than time, and Lilli, the firm's internal LLM-powered tool, is used by more than 75 percent of employees monthly. McKinsey has also trimmed about 10 percent of its global workforce over the past 18 months, largely through attrition and tougher performance management, down to roughly 40,000 people, and Fortune reports that 40 percent of the firm's portfolio now involves helping clients adopt and scale AI and related technologies. PwC plans to hire about a third fewer US entry-level associates in fiscal 2028 than it did in 2025, according to internal projections reported by Business Insider. EY has hired 61,000 technologists since 2023, roughly fifteen percent of the workforce, and is openly exploring what it calls service-as-a-software.
The boutiques are scaling but staying small relative to the demand. The SaaS copilots are succeeding at the easy horizontal use cases (document Q&A, meeting summarization, drafting assistance) but have not penetrated the operations layer where most of the actual work sits. The in-house teams remain a Fortune-500 phenomenon, because the labor-market math does not work for everyone else. The roll-ups are early enough that the financial thesis is unproven, and the natural-experiment from Concentrix's deep AI deployment (still trading at single-digit EBITDA multiples after deploying gen-AI to a thousand-plus customers) suggests the multiples may not transfer the way the playbook assumes.
The customer in the middle, between every model
What none of these five models has yet shown is a path to delivering production AI to non-tech operating businesses at the scale the market needs. The Big-4 firms handle standardized deployments at volume inside enterprises with technology teams, the boutiques handle small numbers of bespoke builds for the most sophisticated customers, the copilots cover the easy horizontal cases, and the in-house teams and the roll-ups serve the companies that can hire them and the companies that got bought. The customer in the middle, the one running on QuickBooks or NetSuite or a legacy ERP with a manual process layer that consumes a meaningful share of employee time, falls between every model.
None of this is a complaint about the existing models, each of which serves a customer well. The point is simply that none of them serves the largest segment, and that this segment has the most to gain from the technology actually being installed.
What a delivery model has to look like to reach this customer is a longer argument, one that starts from a thirty-five year old paper, works through the economics of consulting under AI pressure, takes up Diogo Santos's framing of the third layer, and ends with the operational model that serves the segment the existing five do not. The foundation is the case for a system of action.