Artificial intelligence is becoming increasingly relevant in fleet management, particularly in maintenance. However, before discussing breakdown prediction or automation, there is a more fundamental challenge: ensuring that vehicle information is organised, integrated and available to support decision-making.
A study conducted by Centro Studi Fleet&Mobility, in collaboration with Targa Telematics and Escargo, surveyed more than 120 Italian mobility operators and revealed several relevant insights into this reality.
69% want a better understanding of vehicle downtime
According to the study, 69% of operators believe that integrated data management significantly improves the information available about vehicle downtime.
Knowing that a vehicle was out of service is not enough. Fleet managers need to understand why it was unavailable and at which stage the main delays occurred.
Downtime may be caused by the repair itself, but it can also result from waiting for a quotation, internal approval processes, unavailable parts or communication failures between teams and suppliers.
When data is scattered across emails, spreadsheets, invoices and different platforms, identifying the true source of the problem becomes difficult.
66% believe better information management can reduce downtime
The study also indicates that 66% of operators believe intelligent information management can reduce vehicle downtime.
This finding shows that not all delays are technical. Vehicles often remain out of service because information does not circulate quickly enough between the fleet manager, the repair workshop, suppliers and other teams.
Centralising vehicle history, costs, alerts and the status of each maintenance intervention can accelerate decision-making and reduce administrative tasks.
However, technology alone cannot solve every problem. It is equally important to establish clear processes, responsibilities and criteria for recording information.
64% see value in data integration and analysis
For 64% of respondents, integrating and analysing data through artificial intelligence represents an important source of value in maintenance management.
To identify patterns, knowing the date of the most recent service is not enough. Mileage, vehicle age, breakdown history, costs, downtime and type of use must all be analysed together.
An isolated repair may not indicate a problem. However, the recurrence of the same fault may reveal an ongoing issue, inappropriate vehicle use or the need to replace the vehicle.
Artificial intelligence can accelerate this analysis. However, the results will always depend on the quality of the data being used.
Up to a 30% reduction in maintenance costs
According to the published data, the use of artificial intelligence could contribute to a reduction of up to 30% in maintenance costs and 13% in vehicle downtime.
These figures should be understood as potential outcomes, as the actual impact will depend on the size of the fleet, the type of operation, the age of the vehicles and the quality of the available information.
Nevertheless, they demonstrate that savings do not come exclusively from preventing breakdowns. They may also result from reducing manual tasks, identifying repeated repairs, comparing suppliers and making more informed decisions about fleet renewal.
Progress begins with organised data
Before implementing advanced artificial intelligence systems, a fleet should be able to answer several fundamental questions:
- What is the complete history of each vehicle?
- How much does each maintenance intervention really cost?
- How long does each vehicle remain unavailable?
- Which problems occur most frequently?
A fleet management platform can provide the foundation for this evolution. By bringing together information about maintenance, costs, mileage, documents and alerts, JAT Fleet helps transform scattered data into a clearer view of each vehicle’s performance.
Artificial intelligence is likely to play an increasingly important role in maintenance. However, its value begins before prediction.
It begins with the ability to understand what has already happened and use that information to make better decisions.
