VODAai featured prominently this week as it emphasized the role of artificial intelligence and machine learning in helping water utilities manage regulatory pressures, water loss, and long‑term infrastructure planning. The company highlighted on‑demand and recorded sessions with California Data Collaborative, Orange County Water District, and other experts showcasing real‑world AI use cases.
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Across these discussions, VODAai positioned its platform as a decision‑support layer for operators seeking data‑driven tools for monitoring, potable reuse, compliance, and capital planning. The focus on practical applications in a regulated, mission‑critical sector underscores potential demand for analytics solutions tied to recurring software or service revenues.
The company also spotlighted its AI‑driven risk prediction tools ahead of a presentation by Cory Sides at the AZ Water Association 99th Annual Conference & Exhibition. These tools are designed to help utilities prioritize pipe renewal and water loss interventions by identifying which mains require immediate attention and which assets can be deferred.
By centering on risk‑based asset management, VODAai is targeting a core challenge for capital‑constrained utilities that need to reduce non‑revenue water and optimize replacement budgets. Active participation in the AZ Water conference further signals ongoing engagement with industry stakeholders and potential customers in the water infrastructure analytics space.
In addition to product and thought‑leadership activity, VODAai expanded its commercial organization with the hire of Hershey Barber as an Account Executive. Barber brings SaaS and strategic account experience from roles at Leadr and ZipRecruiter, supporting the company’s efforts to scale its go‑to‑market motion.
Collectively, the week’s developments highlight VODAai’s push to deepen its presence in AI‑enabled water utility decision support while building sales capacity, which may strengthen its positioning as utilities accelerate digital transformation and seek data‑driven infrastructure decisions.