Given that a high number of professionals intend to implement AI but only a smaller percentage currently use it, what barriers or challenges do you think prevent widespread adoption?
The fleet industry faces several significant barriers to AI adoption, including challenges of integration and trust. Many fleet operations still rely on traditional telematics and basic GPS systems, making the transition to AI-driven solutions a substantial investment in both technology and training – often a daunting prospect for resource-constrained organisations. Additionally, concerns around AI’s accuracy and precision have slowed adoption, with some fleet managers hesitant to fully trust it for real-time safety management, particularly in high-stakes environments when system reliability is critical. Addressing these concerns requires demonstrating AI’s clear, measurable benefits such as reducing accidents, improving driver behaviour, and enhancing operational efficiency.
What specific AI features or capabilities do you think could be most beneficial for enhancing fleet safety?
According to various reports, more than 70% of road accidents are the result of human error. AI’s real-time data analysis capabilities help mitigate against these mistakes to enhance fleet safety. For example, AI-driven video analysis and telematics can identify unsafe driving behaviours such as speeding, distracted driving, or tailgating and provide real-time alerts to drivers and fleet managers. By factoring in environmental and contextual data, AI systems can more accurately assess risk and offer timely interventions. Moreover, AI-powered systems can generate detailed, context-sensitive driver scores, offering fleet managers a holistic view of driver performance. This allows for targeted coaching and recognition of safe behaviours, fostering a positive safety culture and reducing accidents.
Why do you think supply chain professionals currently use Predictive Analytics Models to forecast potential safety risks?
Supply chain and logistics professionals are gradually leaning towards Predictive Analytics Models because they offer proactive insights that help prevent safety issues before they occur. By analysing historical and real-time data, these models can detect potential risks, such as dangerous driving behaviours or adverse weather conditions, and suggest preemptive actions. This allows fleet managers to take corrective measures before accidents happen, reducing both operational disruptions and liability risks. Predictive models empower fleet managers to make data-driven decisions that improve overall fleet safety and efficiency.
What might be the next steps for supply chain companies to fully leverage AI for proactive safety management?
Given that many supply chain companies are still in the early stages of exploring AI’s capabilities, the next step is to test the integration of AI systems with existing fleet management platforms to assess the benefits of seamless data flow and the real-time insights AI technology provides. Companies should also invest in training their teams to interpret AI-generated insights and make informed decisions. AI solutions that combine video telematics, predictive analytics, and driver behaviour scoring should be adopted to improve both immediate safety and long-term performance. Furthermore, companies must focus on data privacy, ensuring compliance with regional laws while enabling the full potential of AI technology.
How do you expect the role of AI in fleet safety to evolve over the next few years based on the survey results?
We expect AI’s role in fleet safety to become even more integral as technology continues to evolve. In the coming years, we anticipate a shift from reactive safety measures to predictive and preventative solutions. With advanced AI capabilities, fleet managers will be able to intervene before incidents occur, further reducing accident rates. As AI systems become more sophisticated, their ability to analyse diverse data sources – such as driver behaviour, road conditions, and traffic patterns – will lead to more accurate and actionable insights. The use of AI will also expand to include better route optimisation, predictive maintenance, and comprehensive risk management strategies, making AI a central tool in shaping the future of fleet safety.
Durgadutt Nedungadi is the Senior Vice President for India and International Business at Netradyne Technology. With over three decades of deep global experience across portfolio of offerings spanning hardware, software and services, Durga has worked with global as well as emerging organizations. Currently, he heads Europe, U.K., APAC and MEA business for Netradyne Technology, an industry leader in fleet safety solutions that harnesses the power of computer vision and edge computing to revolutionize the transportation ecosystem. Formerly, he has worked with HP and Wipro in different business roles managing P&Ls, working with organisations globally advising on their digital transformation strategies.
