Moderate Dependence of Heavy-Duty Truck Fleets on Artificial Intelligence Data: Analysis by Fleet Advantage
Fleet management companies are increasingly integrating data analytics and artificial intelligence (AI) into various aspects of their transportation operations, according to a survey conducted by Fleet Advantage.
The survey revealed that a majority of respondents (61.9%) are in the phase of partial AI adoption for their Class 8 fleet operations, while 38.1% are in the limited experimentation phase. Notably, no respondents reported full integration.
Additionally, 57.1% of respondents use AI for data processing, but these systems do not make autonomous decisions. On the other hand, 24% of respondents admitted to not utilizing any form of AI.
Hadley Benton, Executive Vice President of Business Development for Fleet Advantage, expressed that AI is no longer just a curiosity for organizations; it has become a strategic resource for data that can deliver measurable outcomes when used responsibly with accurate data.
The survey findings suggest that these businesses are discovering benefits from AI in crucial areas such as procurement, fuel and utilization strategy, route optimization, maintenance, and remarketing. However, there is still a need for guidance from asset management partners, as evidenced by the growing interest in AI usage.
Key findings of the survey include:
- Predictive analytics (57.1%) and machine learning (28.6%) are the most common AI programs used among respondents.
- A significant majority (71.4%) of companies employ a hybrid AI model that combines open-source and a proprietary platform. Interestingly, 28.7% are using open-source AI.
- Respondents identified route optimization (42.9%) and maintenance scheduling (33.3%) as the primary areas where AI supports decision-making.
- Only 23.8% of respondents express a slight degree of confidence in AI-generated insights.
- Nearly half (47.6%) of respondents are planning on using AI for resale value forecasting, even though they have not started yet.
- Fleet modernization planning (42.9%) and predictive maintenance (28.6%) are additional AI capabilities that respondents would like to see implemented.
- Maintenance scheduling (61.9%), 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.
Challenges faced in implementing AI for fleet and asset management include data integration issues (38.1%), inaccurate data (23.9%), lack of expertise (19.1%), high costs (14.3%), and limited technology infrastructure (28.6%). Budget constraints, a lack of skilled personnel, and the reliability/accuracy of data were cited as significant obstacles to expanding AI use in fleet operations by 19.1%, 19.1%, and 23.8% of respondents, respectively.
The survey results shed light on the current trends and challenges in the use of AI for fleet management, reflecting an industry that is increasingly acknowledging the potential of AI for efficiency, safety, and cost savings, yet faces real-world challenges in implementing these technologies.
- The survey unveiled that the majority of respondents are integrating AI into their business operations, particularly in data processing (57.1%) and fleet management, but only a few have fully integrated the technology.
- The use of AI is not just a passing trend for organizations; it is a strategic resource, as highlighted by Hadley Benton, offering measurable outcomes in areas like procurement, route optimization, and maintenance when used responsibly with accurate data.
- Industry respondents are exploring AI capabilities in various domains, such as resale value forecasting, predictive maintenance, and fleet modernization planning, despite encountering challenges like data integration issues, inaccurate data, and limited technology infrastructure.
