For large fleets with substantial resources, investing in new technologies, staff training, and detailed lifecycle cost analysis can be challenging but still possible. Smaller municipal fleets, on the other hand, are often left behind.

Many operate with outdated systems, incomplete cost tracking, and little visibility into the true financial and operational impact of their vehicle decisions. The result is millions of taxpayer dollars lost to inefficiencies every year, resources that could otherwise go toward frontline services or much-needed fleet upgrades.

However, artificial intelligence is changing that equation. By replacing guesswork and spreadsheets with AI-driven total cost of ownership analytics, even the smallest fleets can achieve the same level of decision-making power as their larger counterparts. 

An industry survey from earlier this year found that about 40.8% of fleet professionals are already using generative AI tools to some extent, while another 39% are not yet using them but are considering adoption. 

Nearly one-third said they expect to adopt them within the next year, and among current users, almost one-third reported efficiency improvements of 8–10%, while about 10% saw gains of 15% or more. Even fleets still in the planning stage anticipate measurable benefits, with 29% expecting improvements of 10–12% once implemented.

The Problem With Legacy TCO Methods

For decades, municipal fleet leaders have relied on manual spreadsheets, isolated procurement systems, and gut instinct to track and predict costs. These legacy approaches suffer from three major flaws. The first is incomplete data. Many fleets don’t have an integrated way to connect maintenance records, fuel expenses, warranty data, and downtime tracking. 

Without a holistic view, decisions about when to replace, repair, or electrify vehicles are made in the dark. Additional survey data shows that this lack of connectivity also contributes to barriers in AI adoption, with fleet professionals citing integration complexity, data security concerns, and accuracy issues - each reported by nearly 20% - as major obstacles to wider use.

The second flaw is that these systems are reactive rather than predictive. Legacy TCO methods often identify costs after they’ve already occurred. For example, a vehicle may be kept in service far past its optimal replacement point, driving up repair costs and downtime.

Finally, traditional TCO tools rely heavily on one-size-fits-all models. National averages and generic benchmarks are useful, but they rarely reflect the unique realities of smaller fleets operating in specific geographies with diverse duty cycles. 

The consequence is that smaller fleets frequently overspend, underutilize assets, or miss opportunities to electrify responsibly. Worse, when budgets tighten, these inefficiencies compound.

Why Smaller Fleets Are Especially Vulnerable

Large city or county fleets can spread risks across thousands of vehicles and often have specialized analysts or software to optimize procurement. Smaller fleets, sometimes only a few dozen vehicles, don’t have that luxury. A handful of poorly timed breakdowns or replacement decisions can derail an entire year’s budget.

Additionally, new environmental and service mandates add pressure. Citizens expect cleaner vehicles, more uptime, and faster response times, while state and federal policies are accelerating deadlines for electrification. For small fleets with limited staffing and outdated tools, keeping pace can feel impossible. 

The irony is that smaller fleets stand to benefit the most from smarter TCO management. Every dollar saved has an outsized impact, and every avoided breakdown can mean uninterrupted essential services for residents.

How AI-Powered TCO Might Level the Playing Field

Artificial intelligence flips the script by giving smaller fleets the ability to see around corners. Instead of working from lagging indicators, AI platforms continuously ingest and analyze real-time data across all vehicle and asset categories. 

AI can model when a vehicle is likely to incur major maintenance, allowing managers to schedule replacements before costs spike or service interruptions occur. Unlike generic models, AI adapts to each fleet’s duty cycles, geography, and usage patterns, providing recommendations tailored to actual conditions.

For fleets exploring electrification, AI can compare the lifecycle costs of gas, diesel, hybrid, and electric vehicles in specific use cases, factoring in charging infrastructure, incentives, and local utility rates. 

The technology also optimizes budgets by pinpointing the right time to retire or retain vehicles, ensuring fleets don’t replace them too early or too late. Ultimately, AI-driven TCO management empowers small fleets to make evidence-based decisions with the same rigor as large organizations, without requiring a team of analysts.

Steps Small Fleets Can Take Today

Adopting AI may sound daunting, but smaller fleets don’t need to overhaul everything at once. The first step is simply to centralize data. By collecting existing cost information, such as fuel, maintenance, downtime, and warranties, into a single system, even if imperfect, managers create a foundation for analysis. 

From there, they can start with a pilot program, applying AI-driven TCO analysis to a subset of vehicles such as police cruisers or sanitation trucks.

Many state and federal programs now provide funding to support electrification planning tools and pilot projects, so fleets should take advantage of these grants and incentives to offset upfront costs. Just as important, managers should engage staff early. 

Mechanics, operators, and supervisors hold critical institutional knowledge, and involving them in data collection and analysis ensures that AI recommendations align with operational realities. This human involvement is key, as survey findings show that while around 40% of professionals feel somewhat comfortable using generative AI tools, only about 10% feel highly comfortable delegating tasks to them.

Most prefer partial delegation with oversight, highlighting the need for transparency and trust-building during implementation. Once proof-of-value is demonstrated, fleets can expand analytics fleetwide, scaling incrementally to incorporate predictive maintenance, charging optimization, and other advanced features over time.