Diag Data AI Bpifrance: an impact-oriented audit methodology

Artificial intelligence opens up numerous opportunities for businesses, yet it's still crucial to identify projects that are genuinely useful and suited to business challenges.

Bpifrance's AI Data Diag helps structure this reflection and build a clear, prioritised, and actionable roadmap.

The AI Data Diag is a scheme supported by Bpifrance, intended for SMEs and mid-caps that wish to structure their strategy around data and artificial intelligence.

This programme aims to help businesses identify tangible opportunities for value creation through AI, whilst defining an implementation pathway consistent with their level of maturity.

The scheme provides for several days of intervention spread over a few weeks to carry out an assessment of the existing situation, qualify the most relevant use cases, and build an operational roadmap. (bpifrance.fr)

At Galadrim, we approach this diagnostic as a decision-support mission. The objective is to bring forth AI projects capable of delivering a tangible impact on the company's operations, organisation, and performance.

Our approach is organised into 3 stages:

The first phase of the diagnostic aims to understand the company's operational functioning and the challenges faced by the teams.

We organise interviews with business managers, operational teams, and decision-makers to map existing processes, data flows, and the main daily pain points.

These exchanges follow a semi-structured format, allowing for the collection of both qualitative and quantitative data. We specifically analyse repetitive tasks, management difficulties, processed volumes, time spent on certain operations, and the financial impacts associated with certain processes.

This stage allows for building a precise vision of automation opportunities and estimating the potential ROI of the various identified use cases.

Once the interviews are completed, we consolidate the collected information with the diagnostic sponsor to align strategic challenges and business priorities.

We then continue the work in-house to define, for each use case, the most suitable technological approach. Depending on the context, this may involve developing a bespoke solution, integrating existing tools, or a hybrid approach combining several technological components.

Our methodology is based on our expertise built on hundreds of completed projects, as well as on the recommendations of the joint white paper published by Siparex and Bpifrance concerning AI adoption strategies in businesses.

Each use case is then evaluated according to several criteria such as business impact, technical complexity, data availability, deployment speed, and team adoption capacity.

This analysis allows for prioritising initiatives through an impact/effort matrix to identify the most relevant projects in the short, medium, and long term.

The diagnostic concludes with the drafting of a structured report that is actionable by the teams.

The deliverable brings together the synthesis of interviews, identified use cases, their priority level, ROI hypotheses, and associated technical and organisational recommendations.

We pay particular attention to the operational nature of this document. The objective is to provide a clear basis for quickly moving forward with the project, whether it involves framing a POC, launching a pilot, or starting a development project.

The AI Data Diag is primarily aimed at French SMEs and mid-caps meeting the eligibility criteria defined by Bpifrance.

Detailed information regarding the scheme, support arrangements, and funding conditions are available on the official page of Bpifrance - Diag Data IA.

In recent years, we have supported organisations with varied profiles in structuring their Data & AI strategy, ranging from industrial SMEs to technology companies and consulting firms. This diversity of contexts allows us to adapt our recommendations to operational realities, business constraints, and the maturity level of each organisation.

The success of an AI project relies primarily on a thorough understanding of business processes, available data, and operational constraints.

At Galadrim, we envision the AI Data Diag as a structuring tool enabling the transformation of technological opportunities into concrete, prioritised, and actionable projects.