Rising repair costs, supply chain disruptions, and changing customer expectations are forcing insurers and repairers to rethink how they operate. AI and machine learning are at the centre of this shift, helping businesses work smarter, make faster decisions, and improve efficiency across claims handling, risk management, and vehicle maintenance.
In my role as Solera’s North Europe VP, I see firsthand how AI is reshaping the industry, improving interactions between businesses, customers, and suppliers.
For years, claims processing has been complicated by the need for wholly manual assessments and the consequent creation of fragmented data, leading to delays and higher costs. AI is changing that, enabling insurers and repairers to assess damage faster, determine the best repair approach, and streamline decision-making.
Solutions from companies worth their salt combine visual intelligence and repair science to generate real-time damage assessments. This means insurers can approve claims more quickly while ensuring accuracy, reducing disputes, and avoiding unnecessary write-offs.
For repairers, AI-driven estimates help plan workloads more effectively, cutting turnaround times and improving service levels. When repair decisions happen quickly and reliably, unnecessary stockpiling is reduced, and the right parts are available when needed.
This alignment between insurers, repairers, and suppliers is making the claims process smoother and more predictable.
AI-powered analytics are helping insurers and fleet operators shift from reactive to proactive decision-making. By analysing vehicle condition, driving behaviour, and repair history, AI helps prevent breakdowns and reduce accident risks.
Telematics, AI-powered video, and advanced analytics are increasingly being used across the industry to monitor driver behaviour and vehicle health in real time. Early identification of risks enables fleets to take preventative action, reducing costs and improving safety.
At the same time, predictive maintenance is helping to optimise servicing schedules by basing them on actual vehicle usage rather than fixed intervals. This approach extends vehicle lifespan and reduces the likelihood of unexpected breakdowns.
For insurers, AI is refining risk assessment models. Instead of relying on broad demographic factors, real-time driving data and vehicle condition now influence pricing.
This shift enables usage-based insurance models that offer fairer premiums while encouraging safer driving habits.