Artificial intelligence is reshaping the water sector faster than most utilities can keep pace with, but according to John Ikeda, Chief Mission Officer at the Water Environment Federation (WEF), the bigger problem isn’t the technology itself. It’s that the AI community and the water community barely talk to each other. On this episode of the Exec Exchange, Ikeda explained how WEF is trying to close that gap through the Water AI Nexus, and what utility leaders need to understand before they get left behind.

Building a Bridge Between Two Industries

Launched last year at WEFTEC, the Water AI Nexus brings together WEF, the University of Pennsylvania’s Water Center, Amazon, and a growing group of leading utilities. The mix is deliberate: an association perspective, an academic perspective, a utility perspective, and the perspective of the hyperscalers actually building AI infrastructure. The center’s advisory council has since expanded to include more organizations from both sides.

Insight Reports as the Anchor

Rather than functioning as a talking shop, the center is structured around published insight reports that set out shared principles for how AI and water intersect. The first addressed sustainable water use by data centers, arguing that hydrology, climate, and grid energy mix need to be built into data center design from the outset, not bolted on afterward. The second looked at how utilities can use AI to make workers more efficient rather than replace them. A third report, examining the real tradeoffs between direct water use and indirect water use tied to energy consumption in data center cooling, is currently in development.

The Water Versus Energy Tradeoff

One of the more counterintuitive points Ikeda raised: a data center that reports zero water use for cooling isn’t automatically the more sustainable option. If that facility relies on energy-intensive alternatives and the local grid isn’t fully renewable, its indirect water footprint could be significantly higher. Part of the center’s next report is establishing a common, credible way to measure and compare these tradeoffs across hyperscalers and regulators.

AI as a Tool for Operators, Not a Replacement

Ikeda was direct about where AI fits into the utility workforce. Physical infrastructure work, digging up pipes and fixing hidden faults, isn’t close to being automated, and public and regulatory pushback on a fully automated utility is likely regardless. The more realistic opportunity is using AI to turn reactive maintenance into planned maintenance and to give operators real-time decision support, sometimes described as an operator co-pilot. In a sector already struggling to attract enough people into operator roles, framing AI as a labor-cutting tool is, in Ikeda’s view, the wrong approach entirely.

A Free Course for the Whole Sector

To help utility staff build baseline AI literacy, WEF launched AI 101 for Water Professionals, a free two-hour online course covering the fundamentals of AI, from predictive modeling to large language models to agentic AI, alongside practical guidance on writing effective queries and understanding the ethics and guardrails involved. It’s open to anyone, anywhere, through the Water AI Nexus resource hub.

The throughline of Ikeda’s message is that no one, including WEF, knows exactly how AI or data center cooling technology will evolve over the next few years. What the center is betting on instead is process: bringing water professionals and AI developers into the same room to co-create solutions before the technology outpaces the sector’s ability to shape it.