AI in water utilities is moving beyond experimental applications, with smaller operators beginning to use the technology to manage regulatory reporting, infrastructure planning and everyday operational demands.
At Newtown Artesian Water Company in Pennsylvania, Director of Operations Richard Pierone is using artificial intelligence to bring previously outsourced work back in-house, reduce administrative pressure and support longer-term investment decisions.
The utility serves communities in Bucks County through a network supplied by five groundwater wells and two purchased-water connections. Like many smaller water providers, it must meet complex regulatory requirements while managing limited resources.
In interviews with H2O Global News, Pierone and Rick Oppedisano, CEO of Delta Bravo AI, discussed the practical benefits of artificial intelligence, the challenges of adoption and why human oversight remains essential.
The growing regulatory burden on smaller utilities
Richard Pierone, Director of Operations at Newtown Artesian Water Company
For Pierone, the challenge is not simply meeting regulatory requirements. It is balancing compliance with daily operations, infrastructure maintenance, emergency response and the financial pressures associated with external specialist support.
“Keeping up with ever-evolving regulations and ensuring compliance while maintaining normal day-to-day operations is an ongoing challenge.”
Richard Pierone, Director of Operations, Newtown Artesian Water Company
Before introducing AI, Newtown Artesian relied on external engineering consultants for several regulatory reporting activities. Although the utility possessed much of the necessary operational knowledge, preparing the documentation internally was often too time-consuming.
Pierone initially experimented with general-purpose AI before learning about Aquaspec, Delta Bravo AI’s water-sector platform, through a National Association of Water Companies meeting.
The utility subsequently began applying the technology to regulatory documentation, operational procedures and asset management.
How AI in water utilities can reduce external consultancy costs
One of the most immediate changes has been Newtown Artesian’s ability to prepare regulatory reports internally.
Pierone says the company has used AI to help create and submit its Consumer Confidence Report, Risk and Resilience Assessment and Long-Term Infrastructure Improvement Plan without outsourcing the work.
“The biggest impact has been reducing our reliance on our engineering firm for reports that we are fully capable of producing ourselves but previously did not have the time to take on.”
Richard Pierone
According to the utility’s initial account, a Consumer Confidence Report that previously required approximately $6,000 in external engineering support can now be prepared internally, with Pierone reviewing the resulting document.
The company also reported completing a Risk and Resilience Assessment in approximately three to four hours using existing plans, regulatory templates and operational information.
These figures illustrate the utility’s reported experience rather than independently verified savings or results that can necessarily be replicated elsewhere.
Pierone plans to extend the approach to additional regulatory and financial filings, including its Annual Asset Optimisation Plan and quarterly adjustment submissions.
Turning lengthy technical manuals into practical guidance
Beyond compliance, the utility has explored how AI can make existing technical information more accessible to operational staff.
One example involved a 278-page manual for a granular activated carbon treatment system associated with the utility’s PFAS response.
The original document was condensed into separate eight-page operational and maintenance guides, giving staff a shorter reference for everyday tasks.
“Realistically, the likelihood of someone reading a 278-page document and retaining the relevant information is low, let alone being able to locate the information they need quickly.”
Richard Pierone
Importantly, the original manual remains available. Pierone explains that AI can identify the sections from which information has been drawn, allowing operators to refer back to the source material.
This approach offers a potential way to improve access to technical knowledge, although condensed guidance must still be checked against the original manufacturer’s instructions and applicable operating procedures.
Using data to prioritise water infrastructure investment
Another application involves the assessment of ageing water mains and future replacement priorities.
Newtown Artesian has been combining information about historical leaks, pipe age, replacement costs and other influencing factors to support its Long-Term Infrastructure Improvement Plan.
According to Pierone, the resulting assessment has helped the utility identify higher-risk mains while considering which assets may require replacement further into the future.
“What makes this assessment particularly valuable is that we can run it in-house at any time.”
Richard Pierone
If further main breaks occur or upcoming roadworks change investment priorities, the assessment can be updated using new information.
This creates opportunities for more responsive asset planning, although engineering judgement remains necessary when translating analytical findings into infrastructure decisions.
Why smaller utilities face barriers to AI adoption
Despite these applications, AI adoption is not straightforward for water utilities operating with small teams and constrained budgets.
Rick Oppedisano, CEO of Delta Bravo AI, identifies staffing capacity, organisational culture and financial resources as three significant barriers.
“The people who would benefit most from AI are often too busy to adopt it.”
Rick Oppedisano, CEO, Delta Bravo AI
Oppedisano argues that smaller utilities frequently lack the resources to evaluate new technology, conduct pilot projects or provide additional staff training.
He also acknowledges that caution within the water industry is understandable, given the consequences that incorrect decisions could have for public health.
For technology providers, demonstrating practical value and reliability may therefore be as important as developing new capabilities.
Human oversight remains essential for water utility AI
Both interviewees emphasise that AI-generated information requires professional scrutiny, particularly when it relates to regulatory compliance, engineering or public health.
Pierone says he personally reviews documents before submission and checks that the information used by the system is accurate and current.
He also draws a clear distinction between administrative and analytical support and activities that still require specialist engineering expertise.
“Project design and the creation of engineering plans is an area where we continue to rely on our engineering firm.”
Richard Pierone
Oppedisano similarly argues that general-purpose AI may not have sufficient knowledge of a utility’s permits, infrastructure or regulatory obligations to produce dependable recommendations without appropriate context.
He says Aquaspec is designed to use curated regulatory information, industry standards and utility-specific records, with source references accompanying its analysis.
These are provider-described safeguards, and their effectiveness remains dependent on the quality of the information, implementation and review processes.
“In regulated industries, humans will always bear the liability for consequential decisions.”
Rick Oppedisano
What comes next for AI in water utilities?
Looking ahead, both interviewees see potential for AI to support smaller utilities in areas such as operational continuity, infrastructure investment, knowledge transfer and regulatory administration.
Oppedisano believes AI could help utilities preserve institutional knowledge as experienced employees retire, while improving their ability to prepare capital plans and pursue infrastructure funding.
For Pierone, the most practical advice is to begin with a manageable task where the utility already has the expertise but lacks sufficient time.
“I would recommend starting with a small task that you feel confident handling yourself, or a project where time, rather than capability, was the limiting factor.”
Richard Pierone
Newtown Artesian’s experience suggests that the near-term value of AI may lie less in replacing technical expertise and more in helping existing teams apply their knowledge across a wider range of responsibilities.
Whether these benefits can be reproduced consistently across the sector will depend on data quality, regulatory safeguards, staff training and the continued involvement of qualified professionals.
For further coverage of digital innovation and operational developments across the water industry, visit the H2O Global News Business section.
Frequently asked questions
Smaller water utilities are exploring AI for regulatory reporting, operational documentation, asset management and infrastructure planning. Newtown Artesian Water Company reports using AI to bring several previously outsourced tasks in-house.
AI may reduce administrative time and external consultancy requirements. However, financial benefits depend on the utility’s existing processes, implementation costs and the reliability of AI-generated outputs.
AI can support analysis, reporting and planning, but qualified professionals remain responsible for engineering design, technical verification and consequential operational decisions.
Utilities should assess data quality, regulatory requirements, information security, staff training, human oversight and whether the technology solves a clearly defined operational problem.
