Now elevated to the rank of priority by boards of directors, AI has become a competitive imperative and represents a decisive investment for the future of French companies.
Although few people can accurately predict the long-term impact of artificial intelligence (AI) on humanity, the reality lies somewhere between predictions of fiction, like the Skynet network from the Terminator saga, and solving every problem we have ever faced. AI can be a real asset for businesses, provided they stop navigating by sight. Now elevated to the rank of priority by boards of directors, AI has become a competitive imperative and represents a decisive investment for the future of French companies. In fact, the Business Delegation took up the subject by submitting a report in the Senate on April 28, 2026, revealing that 55% of French SMEs are already using AI. However, despite this momentum, returns on investment are uneven and the failure rate of first pilot projects remains high.
This gap is explained by a paradox: artificial intelligence, designed to simplify work, also introduces unprecedented operational complexity. The key is to transform infrastructure through observability.
AI failures are the consequence of saturated infrastructure
If there is anything to remember, it is that AI does not evolve in isolation. It operates within an already saturated digital ecosystem. Today, most network traffic is generated not by humans, but by systems communicating with each other. AI adds a powerful new layer to this already complex environment, intensifying demand for data, computing power and connectivity. As a result, most AI initiatives fail due to an invisible point of failure within a long, interconnected digital chain, bringing down the entire system. Because dependencies between systems and applications are often buried deep within services and application programming interfaces (APIs), it becomes almost impossible to determine what went wrong.
So without complete visibility into what’s really happening, leaders find themselves driving one of the most complex transformations in business history with, at best, partial information and, at worst, simply blind faith in their systems. Especially since, when an AI initiative fails, the consequences don’t just affect the technical team. Indeed, the reputational risk falls directly on the shoulders of the managers who carried it.
The most successful organizations in the AI era are not those that spend the most or move the fastest, but rather those that replace uncertainty with clarity, through end-to-end visibility.
Securing AI Adoption
One of the fundamental keys to reducing the risks of AI adoption is observability. This approach involves in-depth analysis of workloads at the data packet level, which provides greater insight and eliminates blind spots. When teams capture activity in real time, they gain the power to identify and contain performance issues, security breaches or even system failures in advance.
Visibility also extends to behaviors. Today, the clandestine use of generative AI, also called “shadow AI”, is widespread within companies. Indeed, the report filed by La Délégation aux Entreprises shows that 32% of VSE-SMEs in France do not aim to use AI; an institutional choice which pushes employees towards uncontrolled use of this technology. However, without clear visibility on the uses that are made of artificial intelligence, managers are not able to clearly distinguish between productive experimentation and uncontrolled risk.
Towards more complexity
Thus, taking the issue of dependencies into consideration is essential, especially because artificial intelligence systems rely on vast networks of services, APIs and infrastructure. Indeed, when a component breaks down, teams must immediately understand what is subsequently impacted. Lack of real-time mapping of these connections could lead to small incidents that could escalate into major outages and directly impact customers and critical operations.
Unfortunately, defining and achieving return on investment (ROI) remains one of the most misunderstood aspects of AI. Spending massively without knowing if the applications bring a real business impact transforms the strategy into a real poker move. Trust comes not from simply tracking usage or raw results, but from the ability to directly tie performance to business goals.
In the age of AI, complexity is not a temporary phase but the permanent condition of modern digital commerce. Erasing it is impossible, so we must learn to manage it by making the invisible visible and hiding its negative impact. The sooner this truth is taken into account by managers, the sooner they will have the capacity to know at any time what is happening at the heart of their systems. Certainty is now the true competitive advantage.
At a time when Europe is grappling with questions about transparency and operational resilience, it is imperative for leaders to properly integrate AI into their environments. Navigating this complex environment using an observability approach helps spot problems before they propagate and identify failures before customers notice them. Finally, if cinema has often portrayed AI as a destructive tool, it is now up to leaders to prove that fiction was wrong. The future of AI will be written in the field of innovation, where the machine remains, finally, at the service of humans.
