Helping Water & Wastewater Professionals Turn AI Into Better Decisions
Executive Brief
Non-Revenue Water remains one of the greatest operational and financial challenges facing water utilities worldwide.
Most utilities already collect enormous amounts of operational data from production, district flows, pressure monitoring, AMI and AMR systems, customer billing, work orders, GIS, and field inspections. Yet many organizations still rely on manual analysis to decide where to investigate potential water losses.
Artificial intelligence is changing that, rather than replacing experienced utility professionals, AI helps utilities connect information that already exists, identify patterns more quickly, and prioritize investigations where the greatest operational and financial benefits are likely to be realized.
The question is no longer whether utilities have enough data, but is whether they are using that data to make faster, better business decisions.
Executive Insight
The most successful Non-Revenue Water programs will not necessarily be those with the largest budgets. They will be the ones that consistently answer three questions better than everyone else:
Where should we investigate first?
Which meters represent the greatest revenue risk?
Where will today's field resources deliver the greatest return?
AI is becoming another decision-support tool that helps answer those questions.
Why This Matters
Every unnecessary truck roll...
Every unnecessary meter replacement...
Every missed apparent loss...
Every delayed leak investigation...
Has a direct financial impact on the utility.
Artificial intelligence cannot eliminate those challenges.
It can help utilities prioritize them.
That means:
Better use of field crews
Faster identification of potential revenue losses
More effective meter replacement programs
Improved operational planning
Better customer service through faster issue resolution
Ultimately, AI helps utilities move from reactive investigations to proactive decision-making.
Business Opportunity
Think of AI as an operations analyst that works alongside your team. Instead of reviewing thousands of customer accounts, meter readings, and work orders manually, AI can identify the small percentage of locations most likely to require attention. For example, an AI model might identify an account because it combines several indicators:
Multiple estimated meter reads
Declining consumption
An aging meter
Previous service history
Pressure anomalies within the surrounding district
None of these indicators proves water loss, together, however, they suggest a higher-priority investigation. That allows utility managers to allocate field resources where they are most likely to improve operational performance and revenue recovery.
Executive Takeaway
Artificial intelligence should not be viewed as another technology project, it should be viewed as a business improvement initiative. Its greatest value is not producing more reports, its greatest value is helping utility leaders make better operational decisions with the information they already have.
Boardroom Question
If your utility could improve its ability to prioritize investigations by just 10%, what would that mean for:
Water recovered?
Revenue protected?
Field productivity?
Customer satisfaction?
That is the business case for AI in Non-Revenue Water.