Technological debt persists and hinders industrial innovation by siloing systems. Without reliable digital continuity, AI cannot fully deliver its value.
With the rise of AI, many manufacturers are discovering an uncomfortable reality: their ability to innovate is hampered by decades of IT complexity. It is not the ambitions that are lacking, but it is the foundations that are lacking. Dispersed data, compartmentalized systems, desynchronized product models… Before even wanting to automate, one imperative is essential: reestablish reliable digital continuity. But how to achieve this?
According to the Frost & Sullivan study on the Connected Product Lifecycle model, 93% of manufacturers say they still suffer from data silos. This figure highlights a paradox: while manufacturers want to accelerate on AI, their technological environments struggle to circulate information. The data exists, but it remains fragmented, difficult to use and, sometimes, unreliable.
This situation logically creates deep frustrations. To remedy this, experience shows that the most effective approaches are based on a progressive approach. Before multiplying POCs (Proof of Concept) or stacking new AI bricks, managers must focus on the essential: producing reliable, contextualized and updated data. In other words, they must build digital continuity which is still too often lacking.
What debt are we talking about?
Industrial technological debt can be explained by several specific dynamics:
® The rapid evolution of equipment and products: Machines now include electronics, software, automation and digital services. This complexity is accompanied by an explosion of data which, often, flows into heterogeneous environments, without any real overall coherence;
® Organizations still too compartmentalized: Although collaborations between the design office, production or logistics are increasing, they remain imperfectly structured. This translates concretely on the ground into discrepancies between the technical nomenclatures defined by engineering and the reality of work in production. Workshops often use their own systems, blocking the feedback of information and preventing immediate alignment of teams during a modification;
® Excessive customization of the IT system: This is an IT architecture debt. Many manufacturers have built their information system over the course of projects by stacking through local business tools, parallel repositories, ERP customizations, etc. If these choices respond to a short-term operational need, they freeze the architecture in the long term, making it difficult to evolve and not conducive to the deployment of optimization or AI projects.
Getting out of tech debt: a three-step approach
Getting out of tech debt doesn’t mean replacing everything or launching a massive transformation program. In industry, these approaches often prove unrealistic. Existing systems carry critical processes, historical data and business rules that run the factory on a daily basis. The challenge is therefore not to wipe the slate clean, but to gradually regain control over three pillars: data, processes and people.
The first step is to identify debt points that are really detrimental to performance because not all debt is equal. For example, a misaligned product bill of material between engineering, production and purchasing generates cascading errors, delays and additional costs. These are the critical areas that must be treated as a priority.
The second step concerns information governance. Many manufacturers have large volumes of data, but little is reliable, contextualized and usable. Clarifying the “sources of truth” then becomes crucial: who owns the product data? Who validates a modification? How are changes propagated to other professions? Without this framework, the company gets stuck in a vicious cycle of re-entries, gaps between systems and decisions based on incomplete information.
The third step is more about organization than technology. This involves reconnecting the teams (engineering, methods, workshop, purchasing, quality, maintenance, logistics, etc.) around the same information chain. This digital continuity is precisely based on the ability to circulate reliable, up-to-date and accessible information at each stage of the product life cycle. Thus, field feedback can feed into product developments, while regulatory or operational constraints are seamlessly integrated into workflows.
The illusion of “shortcut” by Artificial Intelligence
If some manufacturers plan to rely on AI to compensate or hide this debt, the calculation is risky. Certainly, AI can help detect inconsistencies or automate documentary searches. But applied to fragmented or biased data, algorithms cannot reason correctly: on the contrary, they tend to reproduce, or even amplify, dysfunctions.
In short, without digital continuity, manufacturers will continue to multiply technological initiatives without ever capturing their real value. The challenge is not to inject intelligence into existing siled systems, but first to make these systems capable of producing and sharing trusted data.
