Build an AI strategy
that actually creates value.

It makes it possible to determine where AI can genuinely create value before committing significant investment. We analyse processes, data, tools, constraints and the organisation's maturity in order to identify the most relevant opportunities.

When you want to integrate AI but do not know where to start, when several use cases are under consideration, or when a first project has not produced the expected results. The audit turns a general ambition into concrete decisions.

No. An AI strategy must incorporate the business processes, the economic value, the data, the users, security, skills and governance. The technology is only selected once these elements have been understood.

It depends on the scope, but management, business teams, the IT department/CTO and data managers are generally involved. Cross-referencing viewpoints avoids building a roadmap that is technically interesting but disconnected from operations.

We can work from interviews, procedures, examples of tasks, the tools used, the available data and business indicators. Everything does not have to be perfectly documented before we start.

Depending on the scope: a map of opportunities, prioritised use cases, a feasibility analysis, an impact estimate, technical recommendations, risks, a target architecture and an implementation roadmap.

We generally cross-reference potential value, feasibility, data availability, integration complexity, risk, cost and implementation timeline. The aim is to distinguish quick wins from foundational projects.

We start from current volumes, time spent, costs, error rates or revenue opportunities, then set out measurable assumptions. ROI remains an estimate as long as the solution has not been tested; that is why a POC can be recommended on some cases.

Yes. When a use case looks promising but carries significant uncertainty, a POC makes it possible to measure its feasibility and its performance on a limited scope before deciding on industrialisation.

Our approach is designed to select the technology according to the need. Proprietary models, open source, AWS, Google Cloud, Azure or private infrastructure can be considered depending on performance, cost, integration and confidentiality constraints.