The Next Era of Digital Water: Why Intelligence Matters

I believe the next era of digital water will be defined not by how much data we collect, but by how intelligently we use it to inform treatment and other operational decisions that produce better outcomes.

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For decades, the water industry has been investing in systems that collect data, store data, monitor operations, and track performance. Even with these systems, operators still manually connect that information, interpret what it means, and determine the best course of action. It has also been a monumental lift to integrate disparate systems across the utilities tech stack.

That’s all changed. Technology, and AI in particular, has advanced to the point where we can finally close those gaps. By combining large datasets, treatment chemistry, predictive modeling, and AI, we can help operators make better decisions, improve performance, and boost treatment outcomes. At Usalco, we call this new category of technology Treatment Intelligence. It’s why we’re investing in Decision Blue.

To be clear, treatment intelligence does not negate the years of training and human-in-the-loop decisions needed to implement it; rather, these modern tools give operators access to data and intelligence they simply haven’t had at their fingertips until today.

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We’ve Digitized Water. But Have We Made It Smarter?

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The water industry has made tremendous progress in digital transformation. But digitization and intelligence are not the same thing.

Over my time in the water industry, I’ve worked alongside thousands of water systems, gaining firsthand insight into the challenges they face and the opportunities technology creates to improve operations and overcome new obstacles.

I’ve seen and developed software systems that transform paper records, spreadsheets, and disconnected systems into increasingly sophisticated digital frameworks that help utilities manage compliance, monitor performance, and organize critical operational information.

That progress has been significant. But there’s more we can do with the technology that is available to us today that wasn’t a decade or even a handful of years ago.

Making information accessible is an essential first step. Turning that information into better decisions is where the real opportunity lies.

Across the water industry, utilities have invested in LOTS of systems: SCADA systems, laboratory information management systems, metering, GIS, sensors, asset management software, and other information management tools, etc.

These investments serve important purposes. They allow operators to digitize data, document performance, manage infrastructure, etc.

But much of this technology was designed to answer a particular set of pointed questions, across a particular part of the operation:

• What is happening in my plant?
• What’s happening in my distribution system?
• What happened with my lab samples?
• What’s happening with my meters?

Those questions matter. But they’re only part of what an operator needs to know.

The more valuable questions are often:

Why is this happening? What is likely to happen next? What should I do differently? And how can I achieve a better outcome?

Answering those questions requires more than data collection or another dashboard.

It requires intelligence.

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Three Industry Forces Are Accelerating the Need for Treatment Intelligence

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I believe three converging forces are creating an inflection point for better water treatment intelligence: increasing complexity, workforce transformation, and advances in AI.

Operational complexity is increasing.

Operators are being asked to manage more variables outside of their control, tighter performance expectations, and growing cost pressures.

Changing source water conditions, emerging contaminants, regulatory requirements, and aging infrastructure create an increasingly complex operating environment, even for the most seasoned operators.

At the same time, utilities must diligently manage chemical consumption, energy use, labor, and capital expenditures.

The traditional approach of reviewing historical performance, manually adjusting treatment processes, and responding to changing conditions will remain important. But operators need better tools to anticipate problems before they occur.

The more complex treatment becomes, the more valuable intelligent decision support becomes.

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Institutional knowledge is becoming harder to replace.

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Water treatment has always depended on experienced operators.

Ask an experienced operator why they adjusted a coagulant dose, changed a treatment strategy, or responded differently to a particular water quality event, and the answer frequently reflects years of accumulated experience.

That expertise is extraordinarily valuable.

But as experienced professionals retire and utilities struggle to recruit and train the next generation, the industry confronts a significant knowledge-transfer challenge.

We need to capture and scale treatment expertise rather than letting it exist only in individual operators’ minds.

Treatment Intelligence can help institutionalize that knowledge by combining established treatment models, operating history, and professional advice into tools that support both experienced professionals and newer operators.

The objective isn’t to replace operator expertise; no amount of AI or technology will do that. Rather, it’s to make that expertise more accessible, consistent, and scalable.

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AI is creating new possibilities for decision support.

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For the first time, we have progressively accessible technologies that can examine complex relationships across large volumes of data.

AI and machine learning can identify patterns, evaluate historical performance, recognize anomalies, and support predictions that would be difficult to produce manually.

But AI alone isn’t the solution.

Water treatment is a highly specialized discipline. A general-purpose AI model doesn’t inherently understand the chemistry of coagulation, the biological dynamics of wastewater treatment, or a treatment facility’s operational constraints.

Effective Treatment Intelligence requires a foundation of trusted data, validated scientific models, and domain expertise.

AI becomes powerful when it’s applied to that foundation.

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A Final Note on Human-Led, Intelligence-Enabled Decision Making

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The future of water treatment isn’t autonomous facilities operating without people, nor is it decisions being made without institutional knowledge and an experienced knowledge base.

It’s operators equipped with better intelligence.

Water treatment decisions have real consequences for public health, environmental protection, operating costs, and infrastructure performance.

That means trust, transparency, and human management must be fundamental to how this technology develops.

AI-generated recommendations must be grounded in reliable information and validated treatment science. Operators need to understand why a recommendation is being made, what assumptions support it, and where uncertainty exists.

Security and data governance are equally important. Integrating operational information must never be at the expense of protecting critical infrastructure. These aren’t secondary considerations. They’re essential requirements for earning operator trust.