A new study from Fleet Advantage surveying more than 2,500 transportation and private fleet executives reveals that 87.1 percent of respondents are now using generative AI large language models in some capacity — yet the industry faces a widening gap between technology adoption and measurable operational results. The survey, conducted in April 2026, finds that despite broad AI uptake, most fleets are struggling to convert that investment into daily efficiency gains. Data quality is the central bottleneck. Roughly 71 percent of fleets report running into walls caused by data integration challenges, with 64.5 percent flagging inaccurate input data as a specific barrier. When AI systems are fed inconsistent or siloed datasets — maintenance records in one system, fuel data in another, driver behavior logs somewhere else — the outputs fail to generate reliable recommendations that operations teams trust and act on. The research also identifies a notable blind spot in total cost of ownership modeling, with many fleets underestimating the full cost of keeping aging units in service versus scheduled replacement. AI tools designed to flag replacement thresholds require clean historical cost data to function effectively, a dependency that many fleets have yet to fully address. Carriers making the most progress are pairing AI deployment with structured internal education programs to build team fluency around data inputs and outputs. Purpose-built fleet management video training is one way large fleets are closing the gap between AI tool availability and real-world operational use. Source: FleetOwner.com — 2026 Fleet AI Study: Data Integration, TCO Blind Spot, Slow Operational AI (fleetowner.com)