Industrial Maintenance Revolution: How Data and AI Turn Costs into Savings

Industrial maintenance amounts to 22.6 billion euros in expenditures per year in France and supports over 420,000 jobs, according to AFIM. However, in many companies, it only comes to the attention of executive committees at the time of a breakdown, treated more as a technical hassle than a management decision. Those in the industry who continuously manage it using reliable data gain a significant advantage that their competitors often fail to recognize. Achieving this involves integrating data centralization, connected sensors, and artificial intelligence, using solutions like Fracttal.

Maintenance costs impact margins as long as it’s reactive

An unplanned machine shutdown is costly in several ways: halted production, idle teams, and delayed orders. As long as maintenance is reactive, triggered only after a failure occurs, the costs are unforeseen and unmanageable. It is then seen merely as an expense to be minimized, never as a potential source of savings. This reactive approach caps the contribution of maintenance to the profitability of the factory.

Moreover, the cost of a breakdown often exceeds the cost of the repair itself. An unexpectedly halted machine incurs late penalties, excess energy consumption when malfunctioning, and overtime to catch up on lost production. These costs are never itemized on an invoice for corrective maintenance, partially explaining why this expense is undervalued in budgetary decisions.

Transitioning from emergency repairs to preventive and then condition-based maintenance changes this dynamic. Instead of reacting to incidents, the goal becomes to plan interventions at the right time based on the actual condition of the equipment. This shift does not diminish the role of technicians; it provides them with a reliable basis for decision-making, where previously they had to rely on experience alone to compensate for lack of information. Access to timely, usable data is essential for this to work.

Moving beyond blind management by centralizing data

On the ground, maintenance information often remains fragmented among non-communicative tools: spreadsheets, emails, paper records, isolated business software. These data silos force teams to search before they can act, obstructing any holistic view of the asset portfolio. Time spent piecing together the history of an intervention subtracts from time available for maintenance itself, and any missing information increases the risk of poorly informed decisions.

A Computerized Maintenance Management System (CMMS) consolidates this information within a single environment. IoT sensors automatically update the status of machines, reducing manual logging and error-prone re-entry. Maintenance actions and asset history are recorded continuously, allowing audits and regulatory compliance to rely on pre-consolidated data rather than last-minute reconstructions. Moreover, a well-integrated CMMS with its ERP ecosystem, software, and applications enables optimal management of maintenance data. Where previously data dispersion posed a financial risk during checks or liability assessments, centralized maintenance data allows managers to continuously monitor their asset portfolio.

This common data foundation also changes the nature of decisions made. A maintenance manager no longer bases decisions on estimates or team recollections but on verifiable, shared data. Technicians, for their part, can access a machine’s complete history from the field before intervening, without needing to return to the office. Solutions like Fracttal One are built around this principle of data centralization.

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AI supports teams and predicts failures

The next step incorporates AI directly into the maintenance tool. In the field, a technician can request a summary about a piece of equipment or a past intervention instead of digging through documents. For management, planning and dashboards are no longer manually assembled; they are generated from the collected data. AI is not meant to replace teams but to take over information retrieval and repetitive tasks, freeing up technicians for hands-on work.

For the manager, the benefits extend beyond convenience. Having up-to-date, real-time data illuminates decisions that were previously based on guesses: whether to repair or replace, to postpone or advance an intervention, to use a contractor or an internal team. Decisions are based on equipment history rather than intuition, reducing uncertainty in planning.

This data also feeds into predictive maintenance, which forecasts failures before they immobilize a machine. Practically, analysis of sensor readings—such as vibrations, temperature, or power consumption—indicates anomalies well before they escalate into failures. Intervening at the right time prevents both sudden stops and premature replacement of still-functional assets, a significant decision when made without visibility into actual wear and tear. AI also helps fine-tune spare parts inventory, ensuring the right parts are on hand without tying up funds in excessive stock. It also optimizes technicians’ routes, resulting in shorter trips and reduced fuel costs.

The outcomes are quantifiable. The hotel group Iberostar reports a 42% reduction in inventory costs after centralizing and automating its maintenance with Fracttal.

Maintenance thus shifts from a begrudgingly incurred expense to a source of measurable savings: fewer shutdowns, better-calibrated stocks and routes, and longer-lasting assets. The question remains how many in the industry will adopt data-driven maintenance before it becomes a standard practice in the sector.

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