Seven future areas where AI could boost fleet SMR

We’re highlighting seven future areas where artificial intelligence (AI) could boost fleet service, maintenance and repair (SMR) performance, by examining how the technology could be applied to our 1link Service Network platform.

Fabrizio Margaroli, Director of Data Science at our parent organisation, Corpay, said:

“In 2025, everybody is talking about AI but knowledge about its capabilities is limited and the hype is big. Really, it is an umbrella term for several technologies developed since the 1950s that mimic human intelligence.

“Depending on the specific problem you need to solve, you can apply different types of AI methods. However, they all tend to require intensive processing power. It’s a fact that extracting value from data requires concerted effort.

“In many cases, the simplest solutions are the best. A lot of attention is given to perhaps more high profile, wide-ranging AI applications but for most businesses, its most effective use comes from applying the technology to specific problems.”

Craig Evans, Senior Business Intelligence and Data Analytics Manager, detailed seven areas where AI is being investigated by us for use in 1link Service Network –

1. Digital SMR booking processes

He said: “When choosing an SMR supplier, AI will enable fleets to build in many more variables than they currently use. That means not just labour rates and parts discounts but factors such as average vehicle off road and booking lead times. AI would allow you to monitor the performance of those providers in real time to identify the best based on your needs.”

2. Lack of real world SMR data on new models

“We’re going through a perhaps unprecedented moment when it comes to the number of new models, especially electric vehicles, that are arriving on the market. Forecasting SMR cost for these is difficult but AI means that we could quite quickly aggregate anonymised platform data from our community of fleet customers to provide an answer rapidly.”

3. SMR budget forecasting

“AI would give you the ability to set an SMR budget for your vehicle and then monitor this in real time against real world performance. When the actual costs deviate from the forecast, AI can highlight issues and provide the opportunity to make changes to your spending.”

4. Maximising maintenance controller time

“Authorisation rules – where conditions are set by fleet operators so that SMR jobsheets can be automatically approved by 1link Service Network – are often only rejected by the platform because they have failed to meet one or two comparatively unimportant factors. These are then referred to a maintenance controller but really, that person’s expertise is not usually required. AI can help to make sure that only the problem jobs that need attention are referred to human experts, maximising their effectiveness.”

5. Predicting real world component wear rates

“Fleets often base their component wear rates expectations on manufacturer forecasts, which sometimes prove to be accurate and sometimes don’t. Again, by accessing anonymised community data aggregated across our platform, fleets will be able to see where wear rates depart from what are considered normal bounds, investigate the causes and take action.”

6. Strategic defleeting

“Mistiming vehicle disposal can be expensive, and AI can help to identify the optimum time for sale. The ability to crunch a large amount of data in real time can show when vehicle replacement cycles can usefully be flexed to allow for earlier or later defleeting to maximise values.”

7. Highlighting trends outside of normal costs

“This is something of a catchall area but AI can aggregate community data across the platform to potentially identify any unexpected costs. These could be major component failures but also something as simple as wiper blades, which are not a major cost on an individual basis but can quickly add up when calculated across a fleet.”

Craig added that we are looking at each of these seven ideas in respect of applying them to 1link Service Network but would not launch new products until they were sure that their effectiveness was assured.

“We have been continually developing the platform for more than two decades and are always very careful when it comes to adding new features to ensure they genuinely deliver for fleets. This applies as much to these AI-derived ideas as to any other.”

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