AI strategy prioritised by business value
80% of enterprise AI projects never reach production. We help you separate the noise from the real value and choose the use cases that will actually make it.
The problem we solve.
80% of enterprise AI projects never reach production. The usual reason isn't technical: it's strategic. Use cases are chosen for novelty rather than value; infrastructure is over-invested in before validating; operational impact is ignored.
We help you build a realistic AI roadmap, prioritised by business value and connected to your real execution capacity. No hype, no empty slides.
What is included
- AI and data maturity diagnosis
- Inventory of prioritised use cases (value / effort / risk)
- Stack and architecture recommendation
- Risk analysis (privacy, regulation, ethics)
- Measurable KPIs for each initiative
- Adoption plan with quick wins in 90 days
Our process for AI Strategy.
Discovery
Interviews with business, IT and operations. Mapping of processes where AI has the most leverage.
Use cases
We generate a broad catalogue and prioritise it with our own framework (value / effort / risk).
Architecture
We recommend stack, models and deployment approach. An honest make-vs-buy.
Roadmap
A 12-month plan with quick wins, milestones and an operating model to scale.
The framework we use
Every use case gets three scores: value (impact on revenue, cost or experience), effort (data availability, technical complexity) and risk (privacy, regulation, third-party dependency).
Only cases in the high-value / manageable-effort / controlled-risk quadrant make it to the roadmap. The rest are documented but left out of the plan: the discipline of not doing the wrong thing is worth more than the enthusiasm for doing everything at once.