The result of accumulated trade-offs, technical debt inhibits innovation and business transformation. As classic approaches begin to show their limits, artificial intelligence is opening up a practical way to analyse, absorb and counteract this now-strategic burden.
Technical debt has become a critical concern in information systems. It refers to the accumulation of trade-offs, temporary solutions and suboptimal choices made over time to deliver solutions more quickly, reduce costs or respond to urgent business needs.
Often accepted, sometimes endured, it inevitably generates a future cost: increased complexity, system fragility, more cumbersome maintenance, and above all, a major hindrance to innovation.
Technical debt takes a number of forms: architectural debt, when poorly designed or now-obsolete systems no longer meet current usage requirements; pure technical debt, due to badly structured or poorly optimised code; process debt, linked to patchy documentation or the loss of critical knowledge; and the often-overlooked security debt, which exposes businesses to increasingly critical cyber risks.
A structural trap
The consequences for information systems departments are serious. Internally, the responsiveness of IT departments is diminished and digital transformation slows down. In financial terms, hidden costs explode: maintenance, corrections, lost productivity, etc. And operationally, failures, security breaches and reduced competitiveness become ever-present threats.
A recent study by HFS Research estimates that the 2,000 largest global businesses have accumulated technical debt of between 1,500 and 2,000 billion dollars. Despite considerable modernisation budgets (almost 30% of IT budgets), only three in ten organisations have actually modernised their core applications. For the rest, “transformation” all too often boils down to repackaging the existing system.
But why this collective failure? Simply, because technical debt has become a structural trap. A significant part of the IT services market is built around maintaining rather than eliminating it, prolonging instead of reducing complexity.
Outsourcing or automating around the edges does not solve the problem – the debt is displaced but not absorbed. The more time passes, the more things change – the cost of a real transformation increases, and the spiral becomes self-perpetuating.
AI: a major lever
This is where artificial intelligence could be game-changing. AI is a major lever for analysing, mapping and diagnosing technical debt, in order to start paying it down and avoid recreating another.
AI: another important tool in the IS department’s vast toolbox.
For example, it makes it possible to automate testing before overhauling architecture or migrating to the cloud. It can review, rewrite and optimise existing code without modifying its functionality. It can generate missing code, suggest corrective measures and transcribe applications from one language to another.
It also helps to reduce process debt by generating documentation and leveraging existing knowledge, even after the experts have left the business. In the area of cybersecurity, AI can enhance ethical hacking procedures by simulating a far larger volume of attacks than human teams could manage.
The benefits seem clear: time and therefore cost savings; a better ROI (return on investment) for IS departments; and above all, more reliable, higher-quality systems.
At Axians, the VINCI Energies ICT brand, we have first-hand experience of this with GUTENBRaiN, an AI tool we developed with Actemium, the VINCI Energies industry brand, to analyse huge quantities of hard-copy and digital industrial blueprints in order to create active, always-current databases for customers in the oil and gas sector.
Another promising lever exists in “low-code” and “non-code” tools, which use proven technological building blocks to limit the creation of new debts.
Governance and sovereignty
AI governance is essential. AI is a wonderful assistant, but will never replace the human touch, because human oversight and validation are indispensable in avoiding the “black box effect”.
Sovereignty is another core concern: in a context where most major data and AI players are American, European businesses must take control of hosting and using their data, even if this means leaving the cloud or prioritising sovereign AI solutions.
Artificial intelligence must not be seen as a miracle solution but as another important tool in the IS department’s vast toolbox. Its strength lies in its ability to amplify what already exists: accelerating analysis, making processes more reliable, elucidating decision-making and facilitating transformations.
But it must be integrated methodically, choosing the right use cases and combining it with tried-and-tested practice the IS department has already established in architecture, governance and software engineering.
This alliance between human expertise, robust methods and innovative technologies is what will truly allow us to retake control of technical debt.
03/16/2026
