91TV

The waste management industry has consistently adopted technologies that improve safety, efficiency, and operational visibility. GPS tracking, mobile inspections, telematics, and digital maintenance systems all followed a similar path, and AI is becoming another tool in the arsenal.
By Rachael Plant

In waste management, every collection route, inspection, repair order, fuel purchase, and asset telematics event contributes another piece to an ever-growing operational picture. Despite the abundance of information available, however, many fleets still struggle with the same challenge of turning data into timely decisions.

Many organizations have more fleet data than ever before, but the challenge is that much of it lives across different systems, arrives at different times, or requires hours of manual review before it becomes useful. As labor shortages persist, operating costs continue to rise, and customer expectations for reliable service increase, fleets have less time to spend compiling reports and more pressure to make informed decisions quickly. AI is helping to change that dynamic.

According to a 2026 fleet benchmark report, 53.3 percent of fleets are already either researching AI use or piloting it. While AI often generates headlines about futuristic capabilities, its greatest impact on fleet management is far more practical. Rather than replacing experienced fleet professionals, AI is helping them spend less time searching for information and more time acting on it. It is becoming another tool that supports better fleet management by summarizing large volumes of operational data, identifying patterns that would otherwise be difficult to recognize, and surfacing relevant insights in seconds. For waste management fleets, that means more time focused on improving operations and less time spent chasing data.

The Growing Complexity of Modern 91TV Fleets
Managing a fleet, especially when it hits enterprise levels, has never been a simple task. Collection assets operate under some of the harshest duty cycles of any commercial fleet, frequently stopping, idling, lifting heavy loads, and navigating residential streets and commercial corridors throughout the day. These demanding operating conditions accelerate wear on engines, hydraulic systems, suspensions, brakes, and tires, making maintenance planning especially critical.

At the same time, fleet managers are overseeing maintenance schedules, monitoring equipment use, controlling repair costs, supporting technicians, coordinating with operations, managing compliance requirements, and justifying capital expenditures. Each responsibility generates more data. Maintenance histories reveal recurring repairs, inspections highlight developing equipment concerns, telematics identify use trends, fuel transactions expose changes in operating costs, and work orders document asset health over time.

Viewed individually, each dataset provides useful information. Viewed collectively, they can reveal opportunities to improve fleet performance, but only if someone has the capacity to analyze them. That is where AI is beginning to make a measurable difference.

Reducing Time Spent on Administrative Work
Administrative responsibilities consume a significant portion of time, whether building reports for leadership, reviewing maintenance histories, comparing operating costs across asset classes, identifying overdue services, or preparing budget information. All of these tasks require considerable manual effort. These activities are essential, but they rarely create value on their own. Instead, they are the work required before value can be created through informed decision-making. AI helps compress that process.

Rather than manually gathering information from multiple reports, fleets can use AI to quickly summarize maintenance activity, identify changes in operating costs, or explain why a particular asset is experiencing higher-than-normal downtime. Instead of reviewing hundreds of service records individually, managers can receive concise summaries supported by the underlying fleet data.

Finding Patterns that Fleets Might Miss
91TV fleets generate thousands of individual data points over the life of their assets and hidden within those records are patterns that can influence maintenance planning, replacement decisions, and operational efficiency. A recurring hydraulic repair across one refuse truck may seem isolated. Similar failures appearing across multiple vehicles from the same model year may indicate a broader trend. Rising repair costs following changes in PM intervals may reveal unintended consequences of scheduling decisions. These relationships often exist within the data long before they become obvious through traditional reporting.

AI excels at recognizing these kinds of patterns because it can evaluate large volumes of information simultaneously. Rather than analyzing maintenance history, use data, fuel consumption, and inspection records separately, AI can consider them together and identify meaningful relationships.

For fleets, this means spending less time hunting for trends and more time evaluating whether operational changes should follow. Importantly, those recommendations still require human judgment. Operational realities cannot always be captured in historical data. Experienced fleet professionals remain responsible for determining which insights deserve action.

Strengthening PM Strategies
Asset availability remains one of the most important performance indicators for any waste collection operation. Every unscheduled breakdown has the potential to delay service, increase overtime, require route adjustments, or create additional pressure on already busy maintenance shops.

Traditional PM programs have long relied on fixed service intervals based on mileage, engine hours, or calendar schedules. While these approaches remain effective, AI offers another layer of intelligence by helping fleets better understand how individual assets actually perform over time.

These insights support maintenance planning by giving technicians and fleet managers additional context when prioritizing work. Rather than replacing PM programs, AI helps refine them, allowing organizations to make better use of limited labor and shop capacity. As technician shortages continue to affect fleets across North America, improving maintenance prioritization may prove just as valuable as improving maintenance efficiency.

Preserving Human Expertise
Discussions around AI continue to evolve, but one misconception persists, which is that technology will eventually replace experienced decision-makers. Fleet management demonstrates why that assumption falls short. Operational success depends on factors that extend well beyond historical data. Weather events, staffing availability, customer commitments, municipal priorities, equipment constraints, and institutional knowledge all influence daily decisions in ways that algorithms alone cannot fully capture.

AI cannot replace the experience required to balance these competing priorities. Instead, it strengthens that expertise by helping fleets access better information more quickly. When administrative work decreases, managers gain additional time to evaluate long-term strategies, collaborate with operations, mentor staff, and solve problems that require human judgment. The organizations realizing the greatest value from AI are not treating it as a replacement for people, but as a tool that allows their people to operate more effectively.

A Practical Path Forward
The waste management industry has consistently adopted technologies that improve safety, efficiency, and operational visibility. GPS tracking, mobile inspections, telematics, and digital maintenance systems all followed a similar path, and AI is becoming another tool in the arsenal. It helps organizations make better use of the information they already collect by reducing the time required to analyze it and increasing confidence in the decisions that follow. | WA

Rachael Plant is a senior content marketing specialist for Fleetio, a fleet maintenance and optimization platform that helps organizations run, repair, and optimize their fleet operations. She can be reached at [email protected].

References
www.fleetio.com/resources/white-papers/benchmark-report?utm2026-0101-wc-wp-benchmark-report
www.fleetio.com/tools/fleet-maintenance-spreadsheet