AI Product Lead

Work Schedule: Monday - Friday, 8 a.m. – 5 p.m.

Positions Supervised: n/a

The AI Product Lead drives how AI delivers value across the organization. You will discover high-impact opportunities, maintain a clear and prioritized backlog, and empower teams to adopt AI with confidence. This role owns the AI intake, discovery, and enablement operating model; the AI/ML Engineering Manager owns the final roadmap and delivery priorities. Your mission is enablement: equipping people across every department with the tools, examples, and coaching they need to solve problems independently— raising AI literacy and accelerating results organization-wide.

You will identify patterns across teams, consolidate recurring needs into reusable solutions, and apply rigorous judgment to ensure the engineering team invests in initiatives that demand specialized development, integration, or production-grade reliability.

For initiatives that warrant engineering, you will define scope, success criteria, and stakeholder expectations, then establish a structured handoff with clear milestones and decision points for the engineering team. This model enables engineering to focus on high-impact technical challenges while empowering the organization with speed, AI literacy, and measurable outcomes.

(The following duties and responsibilities are all essential job functions, as defined by the ADA, except for those that begin with the word "may.")

• Foster trusted relationships with teams across every department; equip them with the tools, training, and support to use AI confidently and independently.
• Create and maintain org-visible example project spaces with curated prompts, guardrails and edge-case handling, and example workflow automations with sample files that teams can copy and adapt.
• Partner with the AI/ML Engineering Manager to deliver office hours, workshops, and coaching; may lead training sessions as needed.
• Identify when teams need additional support or when a use case warrants escalation to engineering for custom development.
• Proactively address adoption barriers (e.g., fear of job displacement, skepticism, workflow disruption) through clear communication, success stories, and tailored change management strategies.

• Meet with departments and sub-teams to surface high-value AI opportunities, clarify goals, and translate requests into structured problem briefs with measurable outcomes, constraints, and explicit non-goals.
• Operate a single intake funnel with a consistent scoring rubric; maintain a transparent, stack-ranked backlog in Azure Dev Ops.
• Distinguish between Field/Controls Engineering needs (e.g., Predictive Maintenance, knowledge-enabled field assistants) and Corporate/Internal needs (broad adoption vs. niche cohort solutions); apply appropriate success criteria and capacity guardrails.
• Build and maintain a stakeholder map (IT, Legal, Compliance, HR, Finance, Operations) to ensure alignment on governance, data access, and cross-functional dependencies, coordinating with the Data Governance & Delivery Manager and the Principal Architects (Data Platforms and Applications Engineering); this role does not set governance policy.

• Clarify what stakeholders expect at each phase (Discovery → Prototype → Pilot → Scale) and document requirements, presenting evidence and recommendations, with the AI/ML Engineering Manager having final /no-go authority
• Ensure every backlog item has a complete problem brief, success metrics, and defined non-goals before engineering work begins.
• Regularly review and recommend reprioritization of the backlog based on business value, urgency, and…