AI in France: Companies Confront the Challenge of Metrics

The Profitability Challenges of AI in France

In France, artificial intelligence, although it has moved beyond the stage of promise, struggles to demonstrate its profitability. Indeed, 54% of French companies are finding it difficult to monetize their investments in AI. This issue primarily lies in the use of inappropriate metrics that do not reflect the true economic stakes for businesses.

AI is now in an execution phase, and French companies, like those across the rest of Europe, are ramping up announcements and experiments. However, despite increasing investments, a certain disenchantment is setting in. Leaders are struggling to identify tangible economic returns, which is particularly evident in France.

A recent study by the Ponemon Institute, conducted for OpenText, shows that less than half of French companies (46%) are confident in their ability to demonstrate a return on investment from their AI initiatives. This figure is significantly lower than that observed in North America, where 61% of companies express such confidence. These results highlight the need for solid governance, clear measurement frameworks, large-scale execution, and well-structured information management to fully harness the potential of AI.

A Gap Between Ambition and Reality

The contrast between leaders' ambitions and operational reality is striking. According to a PwC study, 67% of executives believe their information systems are ready for AI. However, this opinion is not shared by Chief Information Officers (CIOs), who point to well-known obstacles: insufficient data quality, accumulated technical debt, and the complexity of application environments.

This divergence in perception fuels a persistent misunderstanding. AI is often perceived as widely deployed, whereas in reality, less than a quarter of French companies have industrialized it on a large scale. Only 21% use it in a structured manner in critical functions such as sales, marketing, or customer service. The initial enthusiasm for innovation is gradually giving way to a realization: the real bottleneck now lies in execution.

The Illusion of Technical Indicators

In this context, how AI performance is measured becomes crucial. Too often, organizations rely on technical or activity-related indicators, such as the number of use cases, the volume of automation, or the intensity of tool usage. These metrics, like the percentage of code generated by AI, may provide reassurance, but they do not answer the central question posed by executive committees: how does AI concretely improve business performance?

Worse still, these indicators can mask counterproductive effects. Rapid but poorly managed adoption can increase IT complexity, deepen technical debt, or undermine application security. Teams may appear more productive without any real improvement in service quality, operational reliability, or customer satisfaction.

The Concrete Expectations of Executives

For top management, AI is not an end in itself. It must serve very concrete priorities: accelerating time-to-market, securing operations, enhancing resilience to incidents, and managing the growing complexity of information and regulation. In other words, AI is judged by its ability to improve the overall functioning of the company. It must contribute to more reliable systems, shorter decision cycles, and better business continuity. This is where its economic credibility is at stake.

Rethinking Performance Indicators

To break out of the current impasse, companies must rethink their indicators and adopt a framework more closely aligned with business challenges. Three dimensions stand out.

The first dimension is the ability to deliver faster without compromising existing systems. Shorter and more frequent deployment cycles reflect an organization capable of using AI to streamline processes while maintaining high-quality standards.

The second is operational robustness. Fewer incidents, faster service recovery, and fewer patches in production are clear signals that AI is helping to stabilize digital environments rather than complicating their management.

Finally, organizational maturity. The Ponemon-OpenText study shows that most organizations (54% in France) struggle to support innovation and business transformation because IT and business objectives are not aligned. Without this alignment, as well as solid data quality and effective AI governance, AI remains confined to promising pilots that are difficult to industrialize.

From Hype to Economic Proof

The current phase marks a turning point. The excitement around AI is fading, giving way to a stronger demand for results. Executives now expect proof, not just technological demonstrations. To meet this expectation, companies must learn to translate AI contributions into clear, comparable, and actionable performance indicators.

Those that succeed in this shift in perspective will not only experiment with AI. They will be able to demonstrate, with supporting data, that this technology genuinely contributes to growth, cost control, and business resilience. In a tense economic context, this ability to objectively measure value will make all the difference.