IN Brief:
- Microlise found that 70% of transport and logistics decision-makers expect AI to transform transport management in 2026.
- The survey covered 250 industry decision-makers, with 14% disagreeing and 16% undecided.
- Route optimisation, maintenance, compliance, and utilisation remain central to the next phase of deployment.
Microlise has published new research suggesting that AI is moving into a more practical phase across fleet and transport operations, with seven in ten decision-makers now expecting 2026 to mark the year the technology begins to transform transport management.
The finding comes from a survey of 250 transport and logistics decision-makers. Seventy per cent said 2026 would represent a step change for AI adoption in the sector, while 14% disagreed and 16% said they were unsure. The result marks a notable shift from Microlise’s 2025 industry report, in which only 36% believed AI was being used to its fullest potential in logistics.
The gap between those two readings is revealing. Twelve months ago, much of the market was still treating AI as an interesting layer on top of existing fleet systems. Now the conversation is moving closer to operating reality: route planning that cuts empty miles, predictive maintenance that reduces unscheduled downtime, intelligent load building, driver behaviour insight, and compliance support that can work continuously rather than in weekly review cycles. In other words, the discussion is becoming less about possibility and more about application.
Microlise is set to put that theme on stage at its Transport Conference in Manchester on 12 May, where chief technology officer Dean Garvey-North will address how operators can extract measurable value from AI tools. That emphasis on measurable value is important. The transport sector has little patience for software that introduces more dashboards than decisions. Cost pressure, utilisation demands, and tighter customer expectations mean operators increasingly want systems that can change outcomes on fuel, service reliability, vehicle availability, and administrative overhead.
There is a wider industry context behind the shift. Fleets are now carrying richer streams of data from telematics, cameras, route systems, driver apps, temperature monitoring, and maintenance platforms. For years, one of the biggest barriers to value was not data scarcity but fragmentation. AI becomes more useful when that data is connected well enough to support action rather than reporting. That does not remove the hard work. Operators still need data discipline, sensible exception management, and enough process consistency for the software to do something reliable with the information it receives.
That is why 2026 looks less like a dramatic AI takeover and more like an operational test. The fleets that get most from the technology will not necessarily be the ones with the biggest systems budget. They will be the ones that can integrate planning, compliance, maintenance, and driver management tightly enough for intelligent tools to improve decisions in the flow of work. The market appears increasingly ready for that shift. The next question is which operators can turn confidence into execution.
