From Data Chaos to AI Clarity: A Water
Utility's 3-Step Implementation Guide
Learn how water utilities can successfully implement AI solutions without losing control. This practical guide covers the essential 3-step process, Prep, Analysis, and Implementation, with real-world examples of predictive maintenance, overflow prevention, and treatment optimization. Discover how to leverage existing systems like SCADA and GIS data to do more with less while maintaining operational security.
More and more utilities are working to improve their data and AI capabilities. This is mostly driven by a need from utilities to do more with less. They have older systems, less funding, and fewer workers than ever before. This is not focused on replacing workers but rather giving them the tools and resources for today’s challenges. With each new technology innovation there is also a risk of failure because of rushed implementation.
Thankfully, institutional IT companies such as Nvidia, Microsoft, Amazon Web Services, and OpenAI are making it safe and easy for water utilities to implement new AI systems.
The 3-Step Framework for AI Success
Once you already know what your drivers are and have detailed out how to achieve those through the use of AI then the implementation can be straightforward. The general process for implementing AI solutions can be simplified down to a 3-step process: Prep, Analysis, Implementation.
Step 1 – Prep: Building Your Data Foundation
Prepare all your existing data for use by the AI system. Typically, this means bringing your GIS, SCADA, and other data systems together. For example, if you want to use AI to help find water quality issues before they get out of control then the following data would be needed:
GIS – Location of water lines, valves, connectivity, and diameters.
SCADA – Water produced, the quality, and timing
Work Orders – Historical water quality issues and reports, also any flushing activity.
Hydraulic Model – Typically in EPAnet format to estimate the flows and pressures around the system
Meter Data – Used to get water demand in near real time
If all your data from the above sources check the boxes of accurate, relational, and relevant then it will make connecting the data together trustworthy. There are many examples of utilities that were negligent in one of the above areas and when it came time for action, they were left to manually figure it out instead of leveraging AI.
This greatly depends on the exact type of AI you need to implement. Machine Learning models are different than Large Language Models (LLMS). The LLMs that are rising in popularity such as OpenAI need the data structured in a certain way. This is the “vectorizing” of the data. This means that instead of a well-ordered rows and columns like you would expect to see in an excel spreadsheet or a relational database this more closely mirrors the human mind in organizing and recalling information.
A simplified visual is above to show how similar words, concepts, and ideas get grouped together. This means that if you have a water distribution issue then it’s closely associated with anything to do with pressure, water quality, pipelines, etc. but it likely won’t make many connections to other things in the utility such as pump maintenance or conference visits.
Step 2 – Analysis: Turning Data into Intelligence
This is the actual results of the AI typically used to save time, money, or improve safety, or maybe even all three. There are many types of AI analysis that we covered in an earlier blog post, it may be worth a revisit.
Predictive Models – AI predictive models are decision-making tools that help identify patterns, trends, and relationships in their data.
Machine Learning – Machine learning is the ability of machines to learn and improve from experience without being explicitly programmed to do so.
Generative AI or Large Language Models – This approach uses deep learning techniques to generate natural language text that is similar to human writing.
Check out EPA Smart Sewer Systems or let me know and I’ll get you in touch with some utilities who already have this in progress.
Step 3 – Implementation: Making AI Work for Your Team
This is what each project is aiming for, the results and the useful part. The analysis and results must be brought back into the everyday usage of the utility. The results and outputs only matter if actually used.
Common integrations examples include:
Predict pipeline breaks – based on previous failures and your pipeline materials, age, pressures, and other factors you can predict with 90%+ confidence which pipelines will fail. This is typically run once a year and used for capital project planning. Integrations include GIS and work order management.
Predict sanitary sewer overflows – Based on historical overflows plus current weather predictions, this gives you up to 80% confidence in what sewer manhole will overflow, when, and how much. Then you can mobilize ahead of time to reduce the impact. Or given enough time in advance even clean out the lines and bring in bypass pumping. This type of analysis is typically running every few hours and as frequently as every 15 minutes during rain events. Integrations include GIS, Hydraulic Models, Manhole Sensors, and Weather Data.
Predict treatment effluent – Based on historical influents, treatment conditions, and current operations you can predict with about 90% confidence what the discharge quality will be based on what you have coming in. This allows the operations team to ramp up (or down) chemical dozing, air blowers, side stream processes, and more. This type of analysis is typically running 24/7 and updating every 5 minutes or so. Integrations include SCADA and Weather Data.
Respond to Public Inquiries – Based on utility records and standard procedures this is typically an online chatbot to help customers find out how to pay their bill, current water conditions, or when to go for specific help. More and more utilities are implementing this into their websites and mobile applications so that customers can get their information quicker. Integrations include Billing, Customer Accounts, and Web Services.
Your Action Plan: Getting Started Today
Assess Your AI Readiness – Talk to your IT department about current capabilities. If you’re using Microsoft SharePoint and databases, you’re already positioned to leverage OpenAI integrations. Start with low-hanging fruit: convert training manuals and regulatory documents into searchable chatbots that provide instant answers to staff questions.
Secure Your SCADA Integration – The common roadblock isn’t capability, it’s security. Physical and virtual one-way data diodes safely extract SCADA information from operational technology networks and feed it into analytical databases without creating vulnerabilities.
Form a Cross-Functional AI Working Group – AI projects typically benefit multiple departments, making cost-sharing logical. Bring together operations, planning, engineering, and IT stakeholders to identify high-impact opportunities and coordinate implementation.
Leverage Existing Vendor Relationships – Your current software providers and equipment vendors have likely added AI capabilities since your initial purchase. Reach out to explore new features. Infrasync remains technology-agnostic, but we’ve witnessed impressive innovations from both startups and established providers like Microsoft.
The water utility sector is embracing technology-driven solutions to address modern operational challenges. The question isn’t whether to implement AI, it’s how quickly you can do it safely and effectively.
Don’t let data chaos hold you back. With the right preparation, analysis tools, and implementation strategy, your utility can harness AI to do more with less while maintaining the reliability your community depends on.
Ready to explore AI for your utility? Infrasync helps water and wastewater utilities implement practical, secure AI solutions that deliver measurable results.
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