Fleet data quality becomes critical as AI adoption grows, warns FleetCheck

Fleet operators looking to use artificial intelligence could end up making costly decisions if the data feeding those systems is inaccurate, according to fleet software provider FleetCheck.

The warning comes as more fleets begin experimenting with AI to automate administration, identify operational trends and generate recommendations around vehicle utilisation, costs and compliance.

Peter Golding, chief executive of FleetCheck, said the long-established principle of "garbage in, garbage out" remains just as relevant in the age of AI.

Speaking after discussions at the recent Association of Fleet Professionals conference, Golding highlighted three areas where AI is increasingly being used in fleet management: automating repetitive tasks, analysing historical and real-time fleet data, and generating recommendations based on available information.

However, he warned that AI systems have no way of knowing whether the data they are analysing is accurate.

"The effectiveness of AI in fleet situations, especially predictive and generative applications, depends on your information being reliable," said Golding.

The warning serves as a reminder that fleet data quality is becoming increasingly important as operators adopt new technology. Inaccurate fuel records, mileage data, maintenance information or compliance records could lead to misleading insights and poor decision-making.

For fleets considering AI, the message is simple: audit and improve your data first. The quality of any AI recommendation will only ever be as good as the fleet information behind it.

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