AI Advances, But Information Systems Struggle to Keep Up

The Rise of AI Against Inadequate Information Systems

Artificial intelligence (AI) is no longer just a technological challenge for modern businesses. Today, the real issue lies in the ability of information systems to sustainably support this technology. As companies multiply AI use cases and tools become more accessible, the promise of quick gains is firmly established. However, another reality is emerging: most information systems are not structured to absorb this new complexity.

Although the gap may not be immediately visible, it becomes apparent when companies attempt to go beyond basic operations. The systems, while functioning and generating revenue, show their limits as soon as it comes to connecting data, industrializing uses, or evolving existing setups. Dependencies reveal themselves, flows become difficult to manage, and every evolution turns into a sensitive project.

The Illusion of Technological Mastery

In the face of the rise of AI, the common reflex is to focus on tools, models, and use cases. Attention is often directed towards the ability to quickly produce value, test, and deploy. AI is perceived as a new building block to integrate into a system supposedly capable of absorbing it. This approach allows for rapid progress, initial experiments, and gives the impression of transforming the organization.

However, this strategy rests on an assumption that is rarely questioned: that of an information system sufficiently mastered to support these new uses over time. Transitioning from a prototype to a reliable use, connected to business processes, requires handling coherent data, orchestrating flows, and exposing services in a stable manner. These prerequisites directly depend on the structure of the existing system.

Another often underestimated aspect is the introduction of new costs by AI, sometimes significant: model consumption, data processing, infrastructure. Without a fine mastery of the system that supports them, these costs quickly become difficult to anticipate and even harder to rationalize. What emerges then is not a limitation of the tools, but a limitation of the foundation, directly conditioning the ability to make AI more than a series of isolated initiatives.

A System We Think We Control… But We Understand Poorly

In many organizations, the information system gives an impression of stability. The tools are in place, processes are running, and activities are well supported. This mastery relies more on daily use than on a real understanding of the whole. In reality, this foundation is often much more fragile than it appears.

Many systems have been built over several years, sometimes decades, through successive layers of solutions. A central block concentrates critical flows, around which business tools, marketing platforms, data layers, and specific interfaces revolve. Data circulates from one system to another, duplicates, and transforms. Mapping logics compensate for discrepancies between tools, while intermediate files or manual processing take over when systems are no longer sufficient.

Behind structured interfaces, part of the operation still relies on extractions, CSV files, or Excel processing. Each evolution then involves dealing with multiple dependencies, difficult-to-read flows, aging or minimally evolving components. What seemed manageable on a daily basis becomes a point of tension as soon as it comes to evolving the system as a whole.

In this context, wanting to integrate new uses, particularly around AI, often means adding complexity to a system that is already complex. Without a clear vision of the information system, initiatives remain isolated, difficult to stabilize, and even harder to industrialize.

Urbanizing to Regain Control

In the face of this complexity, the most common reaction is to act quickly: replace an aging component, add a new tool, launch a redesign. On paper, these decisions seem logical. In practice, they do not address the underlying problem. They add to an already difficult-to-read system and sometimes reinforce existing dependencies. This is a pattern often observed: each decision made without a global vision shifts the problem more than it addresses it.

Regaining control requires a change in posture. It is no longer about stacking solutions but about restoring coherence to the whole. This begins with a precise reading of the existing setup. Understanding real flows, identifying dependencies, clarifying the role of each component. A technical mapping then becomes a central tool, not to document, but to make the system readable.

From this foundation, a target architecture vision can emerge. A projection of the system that takes into account business constraints, data issues, and evolution needs. A form of land-use plan for the information system, which allows for structuring responsibilities, limiting redundancies, and better organizing interactions between components.

In practice, this transformation does not occur in a rupture. It unfolds over time, through a gradual transformation plan towards this target. It often involves encapsulating existing components, implementing decoupling layers, and stabilizing exchanges via controlled interfaces. Gradually, the system becomes more modular, more observable, and more manageable.

This type of approach relies less on tool choices than on the ability to understand the system as a whole and prioritize evolutions. Urbanizing ultimately means restoring coherence to a system that has progressively lost it. And without this coherence, transformations, including those related to artificial intelligence, remain inherently fragile.

The Real Challenge Is Not to Do AI, But to Make It Sustainable

Artificial intelligence will not only reshuffle the cards based on the ability to innovate. It will primarily reveal the gaps between organizations. Some will be able to connect their systems, stabilize their data, and embed their uses over time. Others will remain stuck at the experimentation stage, hindered by a system too opaque to be effectively managed.

This is already what we observe. The issue is no longer just about doing AI. It is about whether the information system is capable of supporting it. This shift brings back to the forefront issues that were thought to be mastered: understanding of the information system, architectural coherence, mastery of flows and data. Not as technical challenges, but as conditions for transformation.

For CIOs, the challenge is clear. It is no longer just about supporting innovation, but about structuring a system capable of evolving over time without creating new dependencies. It is on this capacity that the success of future transformations will largely depend.