Making informed decisions about where AI belongs

Introducing artificial intelligence across an organization starts with the problems it needs to solve, rather than the technology itself. The water utility needed to identify which opportunities could provide meaningful value and whether its information, systems and workforce were ready to support them. Any future solution would also need to maintain the reliability, security and accountability expected of a public utility.

Relevant information sits across plant control systems, laboratory applications, maintenance records and engineering libraries. Other sources include financial platforms and SharePoint. At the same time, the utility is planning major capital improvements and considering a replacement enterprise resource planning system.

Possible applications include retrieving institutional knowledge, supporting maintenance decisions and improving capital expenditure forecasting. The utility is also exploring routine administrative workflows. Each opportunity places different demands on data quality, system integration, governance and human oversight.

The central challenge is separating practical opportunities from ideas that may be appealing but premature. The water utility needs a clear basis for deciding what to prioritize, what preparation is needed and where AI may not be the right answer.

We are conducting an enterprise-wide AI feasibility assessment that considers current technology, data maturity, organizational readiness and business needs.

Through interviews with operational and enterprise teams, we are building a clearer picture of existing systems, information and day-to-day challenges. We are using these findings to identify potential use cases and assess each one against value, technical feasibility, data readiness and risk.

The assessment considers governance, legal and regulatory obligations, cybersecurity and responsible adoption. For each opportunity, we examine the practical changes needed to introduce it successfully. In some cases, analytics, automation or process improvements may offer a simpler and more appropriate response than AI.

The intended outputs are a prioritized set of use cases, supporting investment considerations and a phased roadmap that connects near-term opportunities with necessary foundational improvements. Ongoing engagement with executives and functional teams keeps the work aligned with the water utility’s priorities, including infrastructure reliability, capital delivery and preserving institutional knowledge.

The assessment is in progress. Its intended impact is to give the water utility a practical decision framework  where AI could add value, where it may not be appropriate and what needs to be in place before implementation. This framework supports more deliberate innovation while protecting the utility’s essential public-service mission.