Fleet managers face problems if they rely too much on artificial intelligence (AI) tools, without ensuring that the data they are giving it is accurate.
While creating opportunities for greater efficiency, Industry software provider FleetCheck has warned fleets that poor quality data could lead to misleading recommendations and flawed decision-making.
Peter Golding, chief executive of FleetCheck, said the long-established IT principle of "garbage in, garbage out" remains just as relevant when using AI.
"We’re encountering some instances where fleets are starting to experiment with AI, which is very much to be encouraged in search of new efficiencies, but the data they are using is fundamentally flawed”, said Golding. “At best, this means the output is nonsense and will be ignored by the fleet manager but, at worst, can lead to strategic errors that are expensive and even chaotic."
At the recent Association of Fleet Professionals Conference, three main uses of AI in fleet management were highlighted:
* Intelligent automation for repetitive tasks
* Predictive AI, identifying patterns from historical and real-time data
* Generative AI, creating recommendations from available information
Golding said: "The effectiveness of AI in fleet situations, especially the predicative and generative applications, depend on your information being reliable, otherwise the whole exercise becomes potentially nonsensical."
Golding commented that AI has no way of knowing if data is poor and will respond to prompts in exactly the same way as if the numbers are correct. "It’s unlikely it will look at a fuel expenditure upload, for example, and tell you it looks flaky. The technology effectively trusts you to get it right.”
“However, it’s also worth remembering that large language model are passive – they are designed to do what you ask, so, if you are unsure about data, it can be a good exercise to pose clarifying questions about anything they are unsure about.
"In many cases, this can produce a much better final result.”
