By Fleet Management Weekly Staff
May 6, 2026
Lori Olson
Editor’s Note: At the recent NAFA I&E conference, Lori Olson, Geotab’s Signature Advisor for Data-Driven Decision Making, presented “Human After All: Navigating Disruption with a Human Touch,” detailing how today’s leaders are addressing evolving vehicle designs, integrating in-cab technology, leveraging AI, and preparing for an autonomous future—all while keeping the human element front and center. Here is a summary of Lori’s insights on the strategic application of data and AI in fleet operations, along with the opportunities and challenges for fleet management over the next decade.
Artificial Intelligence (AI) is rapidly becoming one of the most valuable tools for fleet operations managers. From predictive maintenance and route optimization to automated reporting and EV charging strategies, AI can process data at speeds no human team can match. But as many fleets are learning, better technology does not automatically lead to better outcomes. The real opportunity lies in combining AI’s efficiency with human judgment, empathy, and experience.
That message emerged clearly in a recent presentation by fleet professionals discussing how organizations can “navigate disruption with a human touch.” Their takeaway was direct: AI should handle the heavy lifting, while people focus on decisions, relationships, and exceptions. For fleet managers under pressure to improve uptime, control costs, and support drivers, that balance may be the most important leadership skill of the next decade.
Where AI Delivers Immediate Fleet Value
Most fleets already use forms of automation, whether they realize it or not. Telematics systems can trigger maintenance alerts before breakdowns occur. Routing platforms can assign the right technician to the right job faster than a dispatcher working manually. EV charging systems can optimize energy usage and even send power back to the grid during peak demand.
These are not futuristic ideas. They are now available and can save fleets substantial time and money. AI can also eliminate hours of manual spreadsheet work, simplify compliance reporting, and help managers identify trends buried in thousands of data points.
For operations managers, the practical value is simple: let machines process information so your people can spend more time solving problems.
Where Humans Still Matter Most
While AI excels at patterns and speed, it struggles with context. A dashboard may show a technician’s performance slipping, but it cannot recognize that the employee is dealing with a personal issue, burnout, or a training gap. A customer account may appear healthy on paper, while an experienced manager senses dissatisfaction building.
Those moments require human leadership. They require conversation, trust, intuition, and accountability.
Fleet operations have always depended on institutional knowledge—the dispatcher who knows which driver handles difficult routes best, the manager who senses morale issues before turnover rises, or the service leader who spots risk before metrics catch up. AI should enhance that knowledge, not replace it.
Don’t Let the Bot Run Too Far
One caution raised in the presentation was the danger of over-automation. Systems that create friction rather than service can quickly damage employee and customer confidence.
Many operations managers have already experienced it: automated phone trees, chatbots with no escalation path, or systems that technically function but fail to solve real problems.
The lesson for fleets is clear. Automation should improve access, not block it. If AI handles scheduling, service requests, maintenance approvals, or internal support, there must always be a clear path to a knowledgeable human when judgment or urgency is required.
Retooling Your Team for the Future
As AI takes over repetitive tasks, managers must redefine how team members create value. If routing becomes automated, dispatchers can shift toward exception management, customer communication, and productivity improvement. If reporting becomes automatic, analysts can spend more time interpreting trends and recommending action.
That transition requires leadership. Employees need training, reassurance, and a roadmap. If new tools are introduced without explanation, many workers will assume the technology is there to replace them.
Smart fleet leaders should communicate the opposite message: AI removes low-value work, allowing people to contribute at a higher level.
A Winning Model: Human + Machine
One example shared in the discussion came from competitive chess. When average players used mid-level computers effectively—and knew when to trust or override them—they outperformed elite players and elite machines working alone.
Fleet management will follow the same path.
The best-performing operations teams will not be those that rely only on instinct, nor those that blindly trust algorithms. They will be the fleets that combine technology with experienced people who know when to intervene, question outputs, and apply judgment.
What Fleet Managers Should Do Now
- Automate repetitive tasks first – reporting, reminders, scheduling, basic routing.
- Protect human decision points – escalations, customer issues, employee coaching, safety judgments.
- Train teams continuously – teach employees how to use AI as a tool, not fear it as a threat.
- Measure outcomes, not novelty – adopt technology that improves uptime, cost control, and service.
- Keep people visible – fleets still run on relationships, accountability, and trust.
Final Word
AI will continue to reshape fleet operations faster than many expected. But technology alone will not create resilient, high-performing fleets. The winners will be managers who use automation to free people for more valuable work—and who remember that the most important operating system in any fleet is still the human one.
