AI and EVs: a helping hand in the transition

As the push toward electrification grows, AI is emerging as a powerful tool. With the ability to use data to simulate EV performance across different scenarios, it is helping fleet managers make EV decisions with more precision and confidence

Artificial intelligence (AI) is the new buzz technology in fleet, with promises to transform how vehicles are managed and how decisions around decarbonisation are made.

According to research from Webfleet, 48 per cent of UK fleet managers are either already using AI or plan to do so within the next five years. The study, conducted among 1,800 fleet managers in 15 countries, shows that 15 per cent are already using AI, 33 per cent are actively planning adoption, and 43 per cent are considering it – highlighting a clear shift towards data-driven fleet operations.

The research breaks down how fleet managers expect to use AI, with 58 per cent expecting to use it to optimise route planning and logistics and 51 per cent planning to use it to enhance safety, behaviour analysis, and asset management. Fifty per cent predict it will help reduce operational costs, and 47 per cent believe AI will streamline administrative and compliance tasks.

How can AI help fleet managers?

Fleet management is becoming increasingly complex, involving everything from fuel efficiency and vehicle maintenance to driver behaviour and reaching net-zero. AI is being used to manage these variables in real-time, offering predictive insights and automation that can improve both performance and cost control.

In terms of the transition to electric vehicles, AI-powered tools can analyse fleet data – such as trip lengths, charging infrastructure availability and vehicle usage patterns – to assess which vehicles can be cost-effectively replaced by EVs. These systems can simulate EV performance across different scenarios, calculate total cost of ownership, and flag any operational constraints.

This capability is especially valuable for organisations unsure of how to begin their decarbonisation journey. For small and medium-sized enterprises (SMEs) in particular – many of whom may lack in-house fleet expertise – AI offers a level of analysis and foresight that was previously inaccessible, levelling the playing field when it comes to sustainability planning.

AI in action

Many fleet technology companies are now adopting AI in their solutions. Dynamon for example has a new Decarbonisation Planning Report product, which can produce a complete plan for moving to electric or alternative fuel vehicles, with the help of AI. Fleets need to supply their telematics or routing data to Dynamon and the company then uses its predictive software and AI to formulate the report.

Webfleet has launched Fleet Advisor – an AI-powered solution designed to simplify how fleet teams access and act on vital operational data. Fleet Advisor combines generative AI with real-time fleet insights to answer business-critical questions in seconds. The tool enables users to type a query – such as mileage trends, idling times or fuel consumption – and receive clear, visual answers with context, suggestions and next-step recommendations. 

Geotab also has a new tool called ACE, which is an artificial intelligence copilot designed for fleets. It has access to an expansive array of data, including predictive safety analytics, predictive maintenance, trip data, zone activity, electric vehicle statistics, exception events, GPS tracking, and more. This allows it to provide clear answers to complex questions while remembering past interactions to improve future responses.

Samsara’s platform uses AI which is trained on more than 14 trillion data points, giving fleets actionable insights that improve safety, efficiency, and decision-making across operations.

AI can also help those installing electric vehicle charge points. CrowdCharge has a new simulator tool allowing project managers to see a ‘digital twin’ of their low carbon technologies, with the ability to add new elements and see the impact on installation costs. The simulator can then be used to optimise existing and planned infrastructure development, before connecting new energy assets. It can also evaluate many different complex variables, including multiple sites, reducing the need for grid upgrades for locations, and simulating the impact of vehicle to grid (V2G) charging. 

What are the risks?

Despite its promise, AI adoption does come with challenges. Data security remains a top concern, with 59 per cent of respondents to the Webfleet research citing it as a barrier. 
AI systems run using vast amounts of data, which if victim of a cyber attack, could expose sensitive information about the fleet, drivers, and operations. The use of cameras and sensors for monitoring also raises concerns about surveillance and the potential misuse of personal data

This underscores the importance of implementing robust cybersecurity measures alongside any new technology adoption.

Over-reliance on AI could also lead to a decline in human judgment, potentially compromising safety. It’s crucial therefore for employees to make final decisions. 

Self-driving vehicles

AI plays a crucial role in autonomous vehicles 
by enabling them to perceive their surroundings, make decisions, and navigate safely without human intervention. AI algorithms process data from various sensors to understand the environment and conduct driving manoeuvres.

It is hoped that self-driving vehicles are safer 
by having faster reaction times than humans, 
and by being trained on large numbers of 
driving scenarios, including learning from 
real-world incidents. 
Self-driving vehicles can also improve transport, giving people more flexibility to get around, as well as boosting public transport options in rural areas to boost connectivity for local communities and independence for those unable to drive.

The Department for Transport has 
recently announced that self-driving vehicle pilots have been fast tracked so they start in England from spring 2026. This means companies will be able to pilot small scale taxi- and bus-like services without a safety driver for the first time – which could be available to members of the public to book via an app – before a potential wider rollout when the full Automated Vehicles (AV) Act becomes law from the second half of 2027.

The Automated Vehicles Act will require self-driving vehicles to achieve a level of safety at least as high as competent and careful human drivers, and they will undergo rigorous safety tests before use.

As the UK begins to phase out petrol and 
diesel vehicles, fleets must evolve. Whether through route optimisation, predictive maintenance, or EV transition planning, AI
is set to play a central role in fleet 
management.