The share of French companies using at least one AI technology rose from 10% in 2024 to 18% in 2025 according to INSEE, while 55% of micro and small to medium-sized enterprises (TPE-PME) report using generative AIs by the end of 2025. These figures mask a structural imbalance: only 17% of them use it regularly. Measuring this gap between experimentation and actual integration helps understand where the true lever of digital transformation through technological innovation lies.
Gap between reported adoption and regular use of AI in business
| Indicator | 2024 | 2025 | Source |
|---|---|---|---|
| French companies using at least one AI technology | 10% | 18% | INSEE |
| TPE-PME reporting using AI for professional purposes | – | 26% | Barometer France Num (CREDOC/DGE) |
| TPE-PME using generative AIs | 31% | 55% | Bpifrance Le Lab |
| TPE-PME with regular use of generative AI | – | 17% | Bpifrance Le Lab |
The table highlights a massive curiosity dynamic, but integration into business processes remains minority. The doubling of reported AI use between 2024 and 2025 does not translate into an equivalent rise in daily operational uses.
This difference of over 30 points between reported adoption (55%) and regular use (17%) indicates a structural blockage. Companies test a tool, achieve a one-off result, and then fail to anchor it in an existing workflow. Resources like those documented on digitalinnovators.be help map the concrete steps between prototype and effective deployment of a technological innovation.
Digital transformation of SMEs: why experimentation is not enough
Testing ChatGPT to draft an email does not constitute digital transformation. The challenge lies in the articulation between the tool and the business process it needs to modify. An SME that automates the drafting of quotes without rethinking its validation circuit achieves only a cosmetic gain.
Three factors explain the blockage between testing and integration:
- The absence of structured data internally: a generative AI model produces poor results if it is not fed with clean and organized data. Most SMEs do not have a unified customer database or documentation of their processes.
- The lack of intermediate technical skills: between the manager who decides and the external provider who develops, there is often a missing profile capable of translating a business need into a technical specification. This profile is not a developer, but a digital coordinator.
- The underestimation of organizational change: deploying a project management tool or a CRM changes roles, decision circuits, and the distribution of responsibilities. Without support, teams bypass the tool within weeks.
Technological innovation only produces value if the business process is redesigned around it. Technology alone is just an additional cost.
Digital maturity in France: data that puts enthusiasm into perspective
The progress of France towards the European average (around 20% of companies using AI) is real. In just two years, the national rate has risen from 10% to 18%. This dynamic hides considerable disparities depending on the size of the company and the sector of activity.
Large companies have dedicated budgets for innovation, data teams, and partnerships with cloud or AI solution providers. SMEs, on the other hand, operate with heterogeneous tools, shared spreadsheets, and manual processes. The operational gap between experimentation and integration primarily affects structures with fewer than 50 employees.
Digitalization and SME leaders
The France Num barometer shows that 26% of TPE-PME use AI for professional purposes in 2025, which is double the previous year. This acceleration reflects a growing awareness among leaders, but also the trend surrounding free generative AI.
A free and one-off use does not constitute an investment in digital transformation. The difference lies in the ability to move from an isolated tool to a connected system: CRM linked to the billing tool, customer data feeding a predictive model, automation of logistics tracking.
Integrating technological innovation into an existing process: measurable steps
Rather than listing generic best practices, it is more useful to identify indicators that allow us to know if a technological innovation is having a real effect on activity.
Measure before deploying
Before introducing a new tool, documenting the real time consumed by the targeted process provides a reliable comparison base. For example, measure the average time between receiving a customer request and sending the quote. Without this initial data, it is impossible to quantify a gain after deployment.
Connect tools to each other
The effectiveness of a cloud technology or automation tool depends on its ability to exchange data with other components of the information system. A tool not connected to existing data creates an additional silo, not a transformation.
Integration technologies based on APIs allow linking an ERP to a customer relationship management tool or a data analysis platform. This interconnection is the technical foundation without which innovation remains confined to individual use.
Evaluate at 90 days
A 90-day cycle after deployment allows for comparing initial objectives with measured results. The adoption rate by teams, the time saved on the targeted process, and the number of errors avoided form three concrete indicators. If none of these three indicators improve, the tool is not suitable for the company’s context.
The gap between 55% reported adoption and 17% regular use of generative AI in French TPE-PME summarizes the state of digital transformation in 2025. The challenge is no longer to adopt a technology but to integrate it sustainably into a measurable business process. The companies that cross this threshold are those that document their processes, connect their tools, and evaluate results over short cycles.