AI App Delivery
You Bought the AI Licences. Now What?
Most enterprises have signed the AI contract. Far fewer have shipped a production workflow. Here is the engineering gap — and how to close it.
Across the enterprise market, AI adoption has followed a familiar pattern: a licence agreement is signed at board level, seats are distributed, and usage plateaus within a quarter. The tooling is rarely the problem. The missing piece is engineering — the integration, data plumbing, testing and governance that turn a general-purpose assistant into a dependable business system.
Why licences stall
- No connection to the systems where work actually happens — ERP, CRM, ticketing, document stores.
- No agreed use cases with measurable outcomes, so value is anecdotal.
- Security and data-protection teams are brought in late and, reasonably, say no.
- No one owns testing, so outputs are trusted inconsistently or not at all.
From seat count to shipped workflows
Our CAPS model treats each AI initiative as a delivery project with a defined scope, owner and acceptance criteria. We begin with a licence and readiness assessment, map high-value workflows, then co-build with your teams — reviewing AI-generated code, testing model behaviour and putting governance in place before anything reaches production.
An AI licence is a capability. A tested, governed workflow is an asset.
If your organisation is paying for AI that is not yet earning its keep, the fastest route forward is usually a short, scoped engagement that proves value on one workflow and creates the pattern for the next ten.