Companies with transportation fleets have increasingly used data analytics with some form of AI in various aspects of their operations, according to survey results released by Fleet Advantage.
A majority of respondents (61.9%) said they are in the "partial adoption" phase of AI for their Class 8 fleet operations, while 38.1% said they are in "limited experimentation” phase. Zero respondents cited full integration.
Additionally, 57.1% said they are using AI for data processing, but it does not make decisions autonomously, and 24% said they are not using any form of AI.
“AI is no longer a concept these organizations are curious about, it is now a strategic data technology resource that can drastically drive measurable outcomes when responsibly used with accurate, proven data,” says Hadley Benton, EVP of business development for Fleet Advantage. “This survey illustrates how and where organizations with transportation fleets are finding benefits with AI, but it also shows where these businesses still need proper guidance from their asset management partners. The growing interest in use of AI depicts an exciting future for the industry, especially in the areas of procurement, fuel and utilization strategy, route optimization, maintenance and remarketing.”
Key takeaways:
- The most common AI programs used among respondents include predictive analytics (57.1%) and machine learning (28.6%).
- A significant majority (71.4%) of companies use a hybrid AI model that combines open-source and a proprietary platform. Surprisingly, 28.7% are using open-source AI.
- When it comes to applying AI insights, respondents pointed to route optimization (42.9%) and maintenance scheduling (33.3%) as the primary areas where AI supports decision-making.
- Only 23.8% are “slightly” confident in AI-generated insights.
- Roughly 28.6% of respondents said they are occasionally using AI for resale value forecasting, whereas nearly half (47.6%) said they are planning on doing so even though they haven’t started just yet.
- Fleet modernization planning (42.9%) and predictive maintenance (28.6%) are additional AI capabilities that respondents would like to see implemented.
- Maintenance scheduling (61.90%), route optimization (52.4%), and fleet modernization strategies/planning (57.1%) are the top areas where respondents are considering utilizing Agentic AI for autonomous decision-making.
- Data integration issues (38.1%) and inaccurate data (23.9%) were among the most common challenges faced in implementing AI for fleet and asset management. Lack of expertise (19.1%) and high costs (14.3%) also pose substantial obstacles.
- Budget constraints (19.1%), lack of skilled personnel (19.1%), and reliability/accuracy of data (23.8%) were mentioned as significant obstacles to expanding AI use in fleet operations. The majority (28.6%) cited limited technology infrastructure as their biggest obstacle.
