Private fleets are rapidly embracing generative AI, but poor data quality and integration issues are preventing many from unlocking its full operational value, according to a new survey from Fleet Advantage.
The company released findings from its latest Use of AI in Fleets survey during the National Private Truck Council’s 2026 annual conference. The survey, conducted in April, gathered responses from more than 2,500 transportation and private fleet executives.
The survey found 87.1% of respondents are now using generative AI large language models for back-office functions, driver feedback, and extracting insights from internal documents such as maintenance manuals and compliance guides. Predictive analytics and machine learning trailed well behind at 38.7% and 35.5%, respectively.
Operational use cases also increased sharply year over year. AI adoption for route optimization climbed from 42.9% in 2025 to 71.0% in 2026, while maintenance scheduling rose from 33.3% to 64.5%. Fuel type analysis jumped from zero adoption last year to 61.3% this year.
Driver safety applications are also gaining momentum. The survey found 61.3% of fleets are using AI tools to monitor driver behavior and support coaching programs. However, 6.5% reported having no formal driver safety monitoring program at all.
At the same time, fleets reported worsening challenges around data management. Concerns about data integration rose from 38.1% last year to 71.0% in 2026, while concerns over inaccurate data increased from 23.8% to 64.5%. A lack of AI expertise also grew as a concern, climbing from 19.0% to 45.2%.
The survey also suggested enthusiasm around agentic AI has cooled as fleets move from experimentation to real-world deployment. The percentage of respondents not using agentic AI nearly doubled to 38.7%, while active usage slipped slightly to 16.1%. Interest in applying agentic AI to procurement and asset lifecycle management also fell sharply compared to last year’s survey.
Only 9.7% of respondents said they have a formal framework in place to measure AI return on investment. More than half said they track some metrics informally, while 19.4% rely on anecdotal assessments.
The survey also identified gaps in total cost of ownership (TCO) modeling for Class 8 equipment. About one-third of respondents still handle TCO modeling manually, while 29.0% do not perform TCO modeling at all. AI-driven TCO modeling adoption averaged just 12.1%.
Telematics integration remains another weak point. More than half of respondents said they collect telematics and ELD data but have not integrated it with AI tools, while just 9.7% are using AI models to generate real-time insights from that data.
“The data tells a story we see playing out across the industry every day,” said Mac Hudson, senior off-lease manager at Fleet Advantage. “Private fleets are embracing AI faster than anyone anticipated, particularly GenAI, but enthusiasm alone does not create results.”
Hudson added that fleets investing in data quality, telematics integration, and structured measurement frameworks will be best positioned to turn AI from “a back-office convenience” into “a true operational advantage.”
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