AI AND ITS IMPACT ON PUBLIC SECTOR FLEETS

 

At the heart of digital transformation is artificial intelligence (AI), and although it gets a lot of airtime and is a term that’s widely used, many people don’t necessarily understand what it means and how it’s set to be a big part of the industry’s future.

 

Milton Keynes, August 4th 2020 - Countless definitions of AI have been proposed over the years but the common theme in all of them is that computers with the right software can solve the kind of problems that humans solve - identifying patterns in data and making decisions based on what their algorithms learn, outside of human intervention. For example, AI-driven voice assistants, such as Amazon Alex and Google Assistant, improve their response models and comprehension with each new query, so their word error rate continues to fall.

AI plays an essential role in many industries; including for public sector fleets, where the benefits are vast. Many people within the sector would associate it with self-driving cars, but in fact leveraging AI in transportation, logistics and service provision helps perform many different tasks and activities.

Traffic light systems, for example, use a form of AI to automatically detect congestion and make continuous adjustments to maximise traffic flow – all without operator intervention. This is a textbook case of how AI technology can create a safer, more efficient environment for road users.

A large proportion of the road users benefiting from the power of AI are those running commercial vehicles, who are reliant on an efficient transport system to do their jobs in moving goods from A to B. For years, operators have been able to generate and access vast amounts of data using telematics – everything from vehicle location, to speed and fuel consumption. However, by placing AI behind the scenes, technological capabilities can be enhanced, enabling operators to achieve more sophisticated analysis, and subsequently take advantage of a new wave of emerging opportunities.

One such opportunity for operators is automated insight and prediction, which can save valuable time. For example, internal and external vehicle camera systems using AI not only record footage – which an operator would have traditionally needed to manually shuffle through – it can also monitor and automatically alert the operator if problems arise. The moment a camera captures and pinpoints an incident, such as accidents and cases of dangerous driving, and uploads it to the cloud, it automatically gets reviewed, analysed, and categorised. This enables operators to act immediately, such as informing drivers to use caution or adjust their driving accordingly. By filtering out inconsequential events, it removes a time-consuming task and, as a result, operators can respond quicker to uphold safety, which is always the priority.

Other benefits include AI’s ability to identify and differentiate common documents that are stored in the cloud. For example, a supplier invoice against a driver’s license or a speeding fine notification. It does this by spotting key features relative to each document before storing it in the appropriate place for the required action. This a more efficient and speedy administration process by removing the manual action, which is the last thing any fleet operator or manager wants to be doing.

In the future, AI solutions could even help operators shift towards a greener fleet by helping reduce the total cost of ownership and, of course, emissions. For example, with access to the right data, operators will be able to find the most effective way to transition fleets into the world of electric, through AI analysis of each vehicle’s trip data. Specifically, what electric vehicles would be suited for the routes being undertaken, what their cost of ownership would be versus a combustion engine vehicle, what charging infrastructure would be required and where it should be placed.

The recurring theme is that AI can save operators valuable time and effort for more efficient and relevant data discovery, allowing them to focus on the more important issues – strategy and growth. In the future, thanks to AI, we expect fleet operations will go from reactive to proactive, processes from manual to autonomous, and services from standardised to personalised, which all makes for better internal and external working relationships.

 

 

-ENDS-