But they are also expensive assets, costly to purchase, install, and maintain, and like all types of equipment, they degrade over time. One recent study indicated meters undercounted actual water flow by up to 10%. Small errors (e.g., 2–3%) can translate into millions of dollars lost annually for large utilities.
The challenge is that meter accuracy loss is often silent. As meters age, many begin to under-register flow, meaning utilities may be delivering water they are not fully billing for. That missed billable volume can quietly erode revenue, increase apparent losses, and create difficult customer conversations when corrections are eventually made. For utilities, the question is not whether meter accuracy matters. It is how to identify where the greatest risk exists and where limited resources should be focused first.
👉 Understand better how AI for meter accuracy work in this webinar with our experts.
Watch the on-demand webinar to see how utilities can protect revenue and reduce apparent losses.
The challenge of finding the right meters to test
Utilities can’t test or replace every meter. Capital budgets are limited, and field resources are stretched thin. Traditionally, replacement decisions have been driven by factors such as meter age, customer complaints, regional replacement schedules, and usage patterns. While reasonable on the surface, these approaches are blunt instruments. A meter may be old and still performing well. Another may appear normal but be quietly under-registering. Without a more precise way to prioritize, utilities risk spending time and budget on meters that are not the highest priority while leaving revenue at risk elsewhere in the system.
The core challenge is prioritization: which meters are most likely measuring less water than is actually being delivered, and how much revenue is at risk?
“Meter programs do not need more guesswork. They need a smarter way to see which meters are putting revenue at risk.” — Jim Fitchett, Co-founder, VODA.ai
Where AI changes the game
Artificial intelligence brings a fundamentally different approach to this problem. Rather than relying on broad rules or reactive triggers, AI analyzes the rich data already stored in meter-reading systems and utility records, usage history, meter age, size, model, and more. From this data, AI can define a detailed picture of each meter’s likely performance and risk profile.
The result is a ranked list of meters most at risk of accuracy issues, along with estimates of the water volume and billing loss associated with each one. Instead of guessing where to focus, utility managers get a clear, data-driven roadmap for investigation and testing.
Smarter spending, stronger revenue protection
The impact goes beyond just finding bad meters. By directing limited capital toward the meters most likely to be underperforming, utilities can:
- Recover lost revenue by identifying and correcting inaccurate meters sooner
- Reduce unnecessary spending by avoiding the replacement of meters that are still working well
- Prioritize field work by focusing testing and investigation on the meters most likely to create financial impact.
- Reduce customer disputes by addressing accuracy issues before they lead to large billing corrections.
- Support investment decisions with clearer estimates of Volume at Risk and Revenue at Risk.
For many utilities, this is not about replacing their current meter program. It is about making that program smarter, more defensible, and more financially focused.
A practical tool for a real problem
AI won’t replace the expertise of utility managers or the hands-on work of field crews. But it gives them something they’ve never had before: the ability to see across an entire meter population, identify where the greatest risks lie, and act with confidence.
In an industry where every gallon counts, meter accuracy is not just an operational issue. It is a revenue protection issue. By using AI to prioritize the right meters for investigation, testing, and replacement, utilities can protect revenue, reduce apparent losses, and make more confident decisions with limited resources.
👉 Learn more about VODA.ai Meters from our experts.
Watch the on-demand webinar to see how utilities can protect revenue and reduce apparent losses.
This article is part of our AI for Utilities series – where we break down how AI and machine learning can transform water asset management. From risk prediction to proactive prevention, we cut through the hype to share what really works.
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