Waterloop Podcast: AI Moves From Answers To Action

SewerAI's Seth Nickinson, Principal Product Manager, joined Travis Loop on the waterloop podcast to talk about where AI fits in the work of running a collection system. The episode, titled "AI Moves From Answers To Action," traces how utilities are moving from collecting data, to asking it questions, to acting on the answers, and where SewerAI's new Command Center fits in.

From Collecting Data to Asking It Questions

For years, the push in the water sector was to capture more data, then to organize it. Most utilities SewerAI works with have done both: inspections are coded, stored in the cloud, and tied to GIS. SewerAI's own history follows the same path, starting with AutoCode, which uses AI to make inspection data more accurate.

The next step is making sense of it all. Traditional analytics tools offered formulas and fixed reports, so getting an answer meant knowing exactly how to ask. Seth described AI as freeing up the questions people can ask of their data. A collection system supervisor can ask in plain language and get an answer from their own records, including spatial analysis that once waited in a GIS specialist's queue.

From Answers to Action

An answer matters when it leads to a decision, and that's the step Command Center was built for. SewerAI launched it in Pioneer this year as a conversational layer over inspection, GIS, and asset data.

Command Center is basically a layer that sits on top of our existing products to help people move into the planning cycle and into the reporting and into the what next.

In practice, that means analysis, dashboards, and plans built from a question, plus outputs shaped for whoever needs them. NASSCO standardized defect coding and data exchange, but every utility owner and engineering firm still wants deliverables packaged its own way, whether the report is headed to a regulator, a client, or leadership. Command Center produces the output in the format each audience expects, so teams spend their time on the decision instead of the formatting.

Action That Adapts in Real Time

Seth sees the next phase bringing action closer to the moment. A superintendent asks for a daily update over morning coffee and gets a snapshot on their phone of whether crews are on track to finish the week's work.

Further out, he expects more of the work to happen in the field, with AI running on crawlers, sensors, and local hardware. That brings the data, and the response, closer to real time. A team might have a 30-day plan for operations and maintenance, then a rainstorm, a cold snap, or a broken piece of equipment changes the week. AI can help that plan adjust as conditions change.

The long-term goal is an assistant that helps utilities, and the firms that serve them, run the whole system in real time, the way complex industrial sites already do.

What Makes It Trustworthy

Acting on AI's answers takes trust, and Seth sees utilities approaching it with reasonable caution. SewerAI builds its tools around each customer's own data and the language of the industry, with a defined set of tasks to perform, which keeps results reliable and repeatable. Security follows the same discipline as the rest of the platform. Every customer's data stays segregated and hosted in North America, and customer data is not used to train the underlying models.

That trust matters more as the workforce turns over. A record the next operator can question in plain language carries forward what veteran staff know about the system.

Listen to the Episode

Catch the full episode on YouTube, Spotify, or Apple Podcasts. waterloop is also available on YouTube Music, Amazon Music, and Audible.

waterloop is a nonprofit news outlet dedicated to exploring solutions, sustainability, and equity in water, covering the people, technologies, and policies shaping the future of water management.