The Augmented Operator: Navigating The Intersection Of AI And The Water Sector Workforce

By Ashwin Dhanasekar, Brown and Caldwell; Melissa Meeker, The Water Tower; and Gigi Karmous-Edwards, KEC

Introduction: The Collision Of Two Waves

The water sector is facing a convergence of crises. On one side, an estimated 30–50% of the utility workforce is projected to retire within the next decade, taking with them irreplaceable institutional knowledge.¹ On the other, artificial intelligence (AI) is no longer future technology; it is being deployed today for real-time leak detection, energy optimization, and predictive maintenance.² These two forces are colliding at precisely the moment utilities can least afford disruption.

The core thesis is straightforward: AI may be the only tool capable of bridging this labor gap, but only if utilities adopt a Human-in-the-Loop (HITL) strategy that treats workforce training as equally important as software procurement.

The Workforce Friction Points

Trust gap. Seasoned operators who have managed facilities for decades are understandably skeptical of algorithms that contradict decades of “gut feel.” This “black box” problem, where AI provides outputs without a legible rationale, erodes confidence and entrenches resistance.³ For AI to earn a seat at the control room table, its recommendations must be transparent, explainable, and validated against lived operational experience.

Skills mismatch. The operator’s role is shifting from “doing,” manual sampling and hands-on inspections, to “reviewing,” interpreting AI-driven dashboards, managing digital twins, and validating automated alerts. This is an evolution, not a diminishment. But it demands investment in digital literacy, not just software licenses.

Legacy data and legacy mindsets. Fragmented data silos mirror fragmented organizational cultures. Aging SCADA systems, disconnected asset management platforms, and siloed departmental datasets create fertile ground for AI failure.⁴ Without data governance, even the most sophisticated model will produce unreliable outputs, further cementing operator skepticism.

AI As A Workforce Catalyst

Used thoughtfully, AI does not replace operators; it amplifies them. Three applications stand out.

Capturing institutional knowledge. Large Language Models (LLMs) offer a compelling mechanism for encoding the “unwritten rules” held by senior operators before they retire. By systematically documenting heuristics, alarm interpretations, and troubleshooting logic into structured knowledge bases, utilities can preserve expertise that no operations manual ever captured. An example is a GPT known as “WWJD - Chlorine”. This specialized expert assistant for rural and small water system operators was modeled after a seasoned rural water engineer, Jerry, and delivers clear, experienced-based advice on chlorine dosing, residual monitoring, seasonal impacts, disinfection byproduct (DBP) control, equipment troubleshooting, and regulatory compliance. Through an in-depth interview with Jerry, regulatory and research documentation, field-ready SOPs, and case studies, the GPT is substantiated through authoritative sources.⁵

Lowering the barrier to entry. AI-assisted inspection tools, such as automated CCTV analysis for pipeline condition assessment, allow junior staff to perform at a higher level faster.² This makes the water sector more attractive to a technology-savvy generation and compresses the learning curve that has historically discouraged early-career engineers. The City of Houston has developed WaterGPT, an on-premise LLM, to house regulatory frameworks, planning documents and operational datasets. This secure, conversational platform provides utility staff with rapid access to complex regulatory and engineering knowledge and supports data-driven analyses for capital planning, risk management, and operation efficiency.⁶

Democratizing data. The rise of no-code and low-code AI platforms is enabling “citizen developers” within utilities — operators and engineers who build and refine predictive models without data science degrees.⁷ This shifts AI ownership from a siloed IT function to the frontline workforce where domain expertise lives. The Town of Victoria created a no-code OpenAI (enterprise) GPT called OCR Log Test Data Analyzer to replace monthly retyping of handwritten water quality logs. Staff are able to upload pictures or scans of the daily forms into the GPT. OCR extracts and aggregates the data, exporting it to structured Excel sheets. Staff are able to ask questions related to trends and comparisons. The new process saves time, results in fewer errors and duplications, and allows for basic analytics in real time.⁸

The Innovation Hub Model

One of the most promising approaches to bridging the training gap is the collaborative innovation hub: a dedicated environment where utility staff experiment with emerging technologies without operational risk. Hands-on exposure to agentic AI tools, digital twin simulations, and real-time sensor analytics allows workers to build confidence iteratively, learning through failure in a safe setting. These centers provide the “training runway” that most utilities cannot build alone.

The model works best as a partnership. Engineering consultants bring technical rigor and implementation experience; non-profit and academic partners supply research infrastructure and pedagogical frameworks. Together, they can translate cutting-edge AI research into actionable, field-ready skills, a capability neither party can deliver independently.

A quiet, yet critical, shift is happening in how we manage our water resources. Organizations like The Water Tower are laying the groundwork for AI Innovation Hubs, thoughtfully designing support systems for utilities nationwide. To meet this transition, the focus must start with education. Colorado State University’s new course, Applying AI in Environmental Engineering Practice, offered by their Civil & Environmental Engineering Department, is built on this exact premise: stripping away the buzzwords to help the industry steadily evolve, equipping professionals with the practical, hands-on skills required to navigate an AI-driven future.

Actionable Roadmap For Utility Leaders

Before purchasing the next AI platform, invest in three foundational priorities:

Invest in digital literacy first. Every operator should understand what AI can and cannot do, how training data shapes model outputs, and the ethical implications of algorithmic decision-making.³ This is not optional; it is the prerequisite for responsible deployment.

Incentivize cross-training. Formally reward operators who develop digital competency through compensation structures, certification programs, or advancement pathways. Skills like managing digital twin models or interpreting AI-assisted CCTV inspections should be recognized as professional milestones.

Establish transparent governance. Define clearly how AI outputs inform operational decisions, who holds accountability, and how errors are reviewed. HITL governance frameworks ensure automation enhances human judgment rather than displacing it.³ The human must remain in the loop, not as a formality but as a structural safeguard for public health infrastructure.

Conclusion: A Resilient, Human-Centric Future

The goal is not an autonomous water plant. It is an empowered workforce using AI to ensure water equity, resilience, and safety for the communities they serve. The current AI boom is an extraordinary opportunity to reframe the water sector, not as an aging industry struggling to fill vacancies, but as a high-tech, mission-driven career path for the engineers, data analysts, and problem-solvers of the next generation. That rebranding starts not with a press release, but with a training budget.

References

1. Davenport, M. A., et al. (2018). Knowledge transfer challenges in public water utilities: Workforce succession planning in the context of aging infrastructure. Journal of Water Resources Planning and Management, 144(6), 04018022.
2. Xenochristou, M., Hutton, C., Hofman, J., & Kapelan, Z. (2020). Water demand forecasting accuracy and influencing factors at different spatial scales using a gradient boosting machine. Water Resources Research, 56(8), e2019WR026304.
3. Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4), 105–120.
4. Mounce, S. R., Mounce, R. B., & Boxall, J. B. (2011). Novelty detection for time series data analysis in water distribution systems using support vector machines. Journal of Hydroinformatics, 13(4), 672–686.
5. Developed by the City of Carlton (GA) and The Water Tower as part of Water Research Foundation Project 5321: GenAI as a Catalyst for Water Sector Transformation, 2025
6. Zanfei, A., Menapace, A., Brentan, B. M., Luvizotto, E., & Righetti, M. (2022). Novel approach for burst detection in water distribution systems based on graph neural network. Sustainable Cities and Society, 86, 104090.
7. Developed by the City of Houston (TX) as part of Water Research Foundation Project 5321: GenAI as a Catalyst for Water Sector Transformation, 2025
8. Developed by the Town of Victoria (VA) as part of Water Research Foundation Project 5321: GenAI as a Catalyst for Water Sector Transformation, 2025

About The Authors

Ashwin Dhanasekar is the Operations Leader for Digital Solutions at Brown & Caldwell, based out of Denver, Colorado. He also leads BC’s AI and Data Science Team, helping the water sector organically and effectively adopt AI. Ashwin is also an Adjunct Professor at Colorado State University, helping students apply AI in practice. He has over 15 years of experience in the water sector, working in various roles throughout the sector, helping clients with their digital transformation.

Melissa Meeker is CEO of The Water Tower, a nonprofit water innovation center focused on workforce development and R&D. Prior to this role, she held the position of Executive Director for the WateReuse Association, Water Environment & Reuse Foundation, and South Florida Water Management District, and served as the Deputy Secretary of the FL Department of Environmental Protection.

Over the course of more than 25 years, Gigi Karmous-Edwards has worked in various domains of digital technologies, spanning the Data Communications industry, Academia, and most recently, dedicating the last 13 years to the Water Sector.  Gigi is the technical lead and Co-PI of a GenAI WRF #5321 (GenAI for the Global Water Sector) project. Gigi is the founder and former chair of the SWAN Digital Twin H2O Work Group, leads AI market insights at BlueTech Research as a Technology Advisor Group (TAG) member, and served on the Advisory Boards of Veralto and Qatium. As the CEO of Karmous-Edwards Consulting, she advises global utilities and technology companies on digital transformation and GenAI. B.S. in Chemical Eng and M.S. Electrical Eng.