From internal cost to new revenue line — how an IT company productized its own AI deployments
The company was running AI deployments internally and treating them as a cost, while simultaneously competing on price in a mature market of basic services. We turned that experience into an offering: new positioning, a technical layer connecting AI with client data, and a commercial model based on recurring subscriptions. Five clients from the existing portfolio were qualified for the first wave.
Competence treated as cost, not product
The company was running AI tool deployments internally with concrete results: an order-processing agent, a knowledge base for service, and a developer team working standard. All this work was treated as an internal cost. Meanwhile, the company's business model relied on services in a mature market with margins under pressure. Competing on price for the same services as other providers created no advantage. Putting these two facts together led to a conclusion the company hadn't reached on its own: the experience gained from its own transformation was a product clients were willing to pay for — and the advantage was knowledge of their systems and data that no AI tool vendor had.
What we didFrom internal deployment to market offering
- Reframed the company's positioning — from an implementation vendor to a partner capable of connecting client systems with AI tools.
- Built a technical layer enabling access to client data — an interface allowing the AI tool to reach documents, orders, counterparties, and inventory levels without requesting dedicated reports.
- Addressed data confidentiality with deployment variants matched to sensitivity levels, up to models running in the client's own infrastructure.
- Defined a four-stage commercial model: diagnosis, deployment, training, subscription maintenance — previously, the company sold only one-off projects.
- Verified unit cost before selling — launched in promotional mode with one client to measure actual consumption and compare billing models: subscription with a shared pool versus pay-per-use.
- Agreed on a sales approach — conversations led by diagnostic questions rather than solution presentations.
What changed
Five clients from the existing portfolio were qualified for the first wave, each with a diagnosed business problem. An offering based on recurring monthly revenue was created, alongside the existing one-off projects. Internal AI deployments stopped being a cost and became both a sales reference and the foundation for a new service line. The model's target: several thousand euros in recurring monthly revenue at ten serviced clients.
“If you sell technology, your own AI deployment has double value: it improves your work and becomes proof of competence your competition doesn't have. But note the order: the company first deployed internally and measured unit cost, and only then started selling. The reverse order is the most common reason this kind of offering proves unprofitable within months.”
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