Required Education: High School Diploma or EquivalentRequired Experience, Knowledge & Skills: Strong understanding of operations, asset management, maintenance, inspections, work management, or utility field processes Experience working with IT, data, analytics, or digital transformation teams Ability to translate business problems into technical requirements without losing the operational intent Strong requirements gathering, process mapping, stakeholder engagement, and prioritization skills Experience with enterprise systems such as SAP, APM, Snowflake, Power BI, PI, or similar platforms Understanding of data quality, source-of-truth concepts, data lineage, and business data validation Familiarity with AI, machine learning, predictive analytics, chatbots, automation, or advanced analytics use cases Ability to lead pilots and proof of concepts from idea through implementation Strong communication skills with both technical and non-technical audiences Ability to manage competing priorities and keep teams aligned around business valuePreferred Experience, Knowledge & Skills: Experience in electric utility, substation operations, transmission and distribution, asset management, or field operations Experience supporting AI use cases related to asset health, alarms, inspections, maintenance optimization, work planning, or operational risk Knowledge of Snowflake, Foundry, Cortex AI, Power BI, SAP, Bentley APM, PI historian, or similar platforms Experience working with vendors on technology pilots or enterprise software implementations Experience preparing executive presentations, funding requests, roadmaps, and implementation plansRole SummaryThe AI Solutions Product Owner will lead the development and delivery of AI use cases that support Substation Operations, Asset Management, and related business functions. This role will serve as the key bridge between Operations, IT, data teams, analytics teams, vendors, and leadershipThe Product Owner will be responsible for identifying high-value AI opportunities, gathering business requirements, defining data and model needs, validating data accuracy, and ensuring AI solutions are practical, trusted, and usable by the business.