There’s a version of the water industry right now where AI is everywhere.
It’s optimizing treatment. Predicting failures. Running entire systems in the background while operators sip coffee and watch dashboards glow green.
That version exists, though mostly in conference presentations.
Then there’s the version most of you are living in.
Where AI might be:
- helping summarize inspection reports
- speeding up CCTV review
- flagging anomalies that still require a second (or third) look
- or quietly sitting in a pilot phase that’s been “almost ready” for about nine months
And, in at least a few cases, probably writing meeting notes that no one reads anyway.
We’re not knocking it. Far from it.
There are real, meaningful use cases emerging:
- faster condition assessment in collection systems
- better prioritization of capital projects
- early warning signals for equipment issues
- support for a workforce that’s getting thinner by the year
But there’s also a gap—one worth talking about—between what AI is supposed to do and what it’s actually doing on the ground.
That’s the story we’re interested in.
Where did AI actually save you time? Where did it fall short? What are you using it for that you didn’t expect? And what still requires a human who knows the system better than any model ever will?
Because if there’s one thing we’ve learned covering this industry, it’s that technology doesn’t entirely replace experience; it reshapes where that experience matters most.
So consider this an open call.
Tell us how you’re using AI in your plant, your collection system, your modeling work, or your day-to-day operations. The useful stories. The frustrating ones. The “it worked better than we thought” moments.
Get in touch with Content Director Eric Sandy at [email protected].
And if the most honest answer is, “we’re still figuring it out,” you’re probably in very good company.
— Water Daily
