26/03/2026
Artificial intelligence (AI) is gaining more and more ground in industry, consolidating its role as an essential tool in the management and optimization of production processes. Its application in planning, the efficient use of resources, corrective and preventive maintenance, not to mention troubleshooting, contributes decisively to reducing risks, especially those related to machinery breakdown and loss of earnings.
The types of maintenance that are carried out today are:
- Corrective: maintenance work that’s done when a breakdown has already occurred.
- Preventive measures: maintenance that’s scheduled to avoid future failures.
- Predictive: scheduled maintenance based on particular operating conditions.
- Prescriptive: maintenance work supported by AI analysis.
Prescriptive maintenance is the evolution of predictive maintenance, integrating AI, machine learning, big data, and IoT sensors to analyze large volumes of data in real time. One benefit of this is its ability to offer specific recommendations on what to do with equipment and when to intervene, enhancing corrective and preventive maintenance levels.
Unlike predictive maintenance, prescriptive maintenance not only anticipates possible failures, but also optimizes disruptions, reduces performance losses, and provides automatic indications based on historical data and previous experiences, thereby serving as a complement to predictive maintenance.
The more data that’s collected, the more robust and reliable the models will be. Given the high volume of information, manual management isn’t feasible; which is why AI is key to detecting patterns, anomalies, and trends that are impossible to identify using traditional methods.
Having analyzed these data, the system itself will indicate what type of maintenance to carry out and when, based on the data obtained and the experience or learning that the system is acquiring. This data is from the plant itself, from other plants, or data provided by the manufacturer.
To implement this system, it’s necessary to have certain elements installed within the process:
– IoT sensors and gateways that facilitate monitoring and the controlling of multiple parameters. Unlike analog sensors, they incorporate circuits compatible with standard communication systems, which enable the extraction and sending of data to centralized databases.
These sensors are capable of measuring temperature, humidity, vibrations, pressure, electrical current, acceleration, optical images, etc.
These data are recorded and later analyzed to make decisions supported by the human team.
– An analysis platform. It’s essential to have advanced monitoring and data acquisition digital platforms in place that that can collect and analyze information in real time, identifying more precise patterns and capable of proposing recommended actions via smart algorithms.
There are currently multiple solutions available in the market that are adaptable to different industrial sectors and processes, such as, for example: Siemens, Oracle, Honeywell, many of them with AI engines like Google Gemini.
– Data and models are crucial for the proper functioning of these systems, with quality and consistency of the compiled data being of vital importance. A broad database, combining data from various sources, feeds the analytical models, allowing for more precise recognition of patterns, which in turn generates more reliable recommendations on events or failures, and is also able to automate many of the tasks.
– Team training is essential to ensure effective implementation. Technicians and engineers must be properly trained on how to collect data, interpret the information that the system provides, and subsequently validate same in order to take action.
Although AI provides great support, the final decision always rests with human staff, which is why it’s essential to promote a more strategic and proactive digital culture.
Using AI allows for cross-referencing of data from multiple machines, manufacturers’ manuals, sensors, and technical diagnostics, generating deep and continuously updated knowledge. This constant learning improves the diagnosis, the prediction, and the efficiency of the interventions.
In addition, these systems can automate administrative tasks like the generation of work orders, management of the availability of resources, and workloads or processes, as well as the consultation of external documentation, schemes, or technical support. This significantly reduces management and maintenance time.
These systems adapt to any type of industry and process. They can be implemented to a greater or lesser extent within the production processes, the investment can be gradual, seeking increasingly better results. Some portfolio clients already use AI-assisted systems and are reporting very positive feedback and improved KPIs.
Another AI-powered industry-focused model is that of assisted maintenance. Specifically, we’re talking here about the assistance that this technology (augmented reality, virtual reality, or 3D technology) provides in maintenance or repair operations, helping technicians to reduce operating times and boost overall quality. This type of assistance can be provided directly by the supplier or manufacturer, achieving better repair ratios, as diagnostic times and operating times are shortened. Cost reduction is also an important consideration, given that technicians don’t have to go to the plant in person, and availability is improved.
This approach seeks synergies between AI and other advanced technologies, maintaining the essential role of the human specialist, who provides context, technical knowledge, and critical judgment.
Conclusion
From an insurance perspective, these tools provide great value as they enhance asset protection, optimize processes, and boost a company’s reliability, all of which are highly valued factors in industrial risk management. From a risk engineering perspective, when we analyze aspects like machinery breakdown and the consequential loss of profits, having reliable systems in place that improve the management and maintenance activity of the equipment is something we value positively.
Using AI in industry is now a reality. This technology is a great ally in reducing and mitigating risks through the application of prescriptive maintenance.
Javier San Frutos
Risk engineer -Engineering area – Mapfre Global Risks
