Case Study 01 · Big Four Professional Services
A $40M AI portfolio stalled at commercialization. Not at engineering.
The outcome
$40M
Cost center → revenue-generating division. Multiple Fortune 50 deployments within 18 months.
The Challenge
180 engineers. Deep learning. ML-based risk models. Clinical workflow automation. And no commercial architecture.
A global Big Four professional services firm operating a Platform and Product Engineering division. $40M+ annual operating budget. A team of 180+ spanning product, engineering, design, data science, and product marketing. The mandate: build and commercialize AI products across Supply Chain, FinTech, and Healthcare for a Fortune 50 client base.
The engineering team was not the bottleneck. The division had technically credible work across every vertical. The bottleneck was structural: there was no commercial architecture.
Each product was being built without a pricing structure that enterprise procurement could approve. There was no packaging that mapped to how Fortune 50 buyers allocate budget. Procurement committees were reviewing products that had no defined consumption unit, no margin floor, and no contract pathway that legal teams recognized. A 180-person team producing work that stalled at the commercialization stage — not the development stage.
What We Did
Rebuilt the commercial model from the P&L down.
The intervention required redefining what “done” meant. A product was not done when engineering shipped it. It was done when procurement could buy it.
01
Commercial governance before development
Established a governance structure requiring every AI initiative to define its pricing tier, procurement pathway, and margin floor before advancing from proof-of-concept to development.
02
Two-Part Tariff pricing architecture
Introduced platform access fee plus consumption variable — aligning the division’s cost structure with how Fortune 50 clients budget for AI infrastructure.
03
Procurement simulation sessions
Ran live simulations against Fortune 50 buying criteria before deals entered formal review. De-risked the contract stage in advance rather than reacting to objections during it.
The Outcome
60 days.
Products stalled in procurement for 12+ months cleared in 60-day commercial cycles.
Multiple AI products moved from internal proof-of-concept to active Fortune 50 deployments across three verticals within 18 months. The $40M portfolio shifted from cost-center framing to a revenue-generating product division. The 180-person team moved from producing research assets to producing deployable, procurement-approved products.
The replicable insight
The commercial model must be designed alongside the technical architecture — not after it. If your commercial team is entering the picture after engineering ships, you are already 12 months behind schedule and approximately $2–4M into sunk development cost with no revenue to show for it.
If your AI products are stalling at procurement, the problem is structural.
A 30-minute conversation identifies whether the constraint is commercial architecture — and whether we’re the right team to fix it.
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