Engage stakeholders to understand needs and prioritize AI/automation features by value, risk, and feasibility.
Communicate requirements, progress, value, and AI assumptions/limitations at the right level of detail.
Partner with users and SMEs to define actionable requirements and testable acceptance criteria, including quality, safety, and compliance expectations.
Report status, blockers, and risks (data, model, compliance) to the Product Manager and escalate as needed.
Partner with the Product Manager to maintain product vision and strategy for AI-enabled capabilities (agentic AI, workflow automation, decision support).
Support roadmap planning aligned to strategy, release plans, and measurable outcomes (time saved, quality, cost, compliance).
Promote transparent delivery and decision-making, including how AI performance is evaluated and monitored.
Build domain and solution context (data sources, constraints, dependencies, risks, goals) to ensure deliverables meet user needs.
Own and prioritize the backlog; break AI work into clear epics/stories, including data readiness, evaluation, and rollout.
Maintain backlog visibility and clarity with a definition of done that includes testing, metrics, and required documentation (e.g., model/release notes).
Track development progress and readiness; coordinate demos, acceptance, releases, and post-release AI monitoring.
Strong stakeholder influence skills, ability to influence adoption without direct authority.
Lead or participate in agile ceremonies and help balance discovery, experimentation, and delivery.
Clarify priorities, outcomes, and tradeoffs; coordinate with delivery teams, data science, security, and operations.
Drive adoption of AI-enabled practices across delivery teams through communication, training coordination, and feedback loops, in addition to feature delivery.
Help monitor investment and ROI; support cost/benefit analysis for AI and automation (tools, licensing, infrastructure, operations).
Requirements
Product Owner/Manager (or similar) experience in agile; delivery of technology products with cross-functional teams.
Strong communication, leadership, and stakeholder management; strong analytical/problem-solving; able to explain AI tradeoffs and limitations to technical and non-technical audiences.
Working knowledge of modern software delivery practices (branching strategies, CI/CD, code review, sprint cadences, and delivery metrics) and the ability to apply them to drive consistent, measurable delivery outcomes across development teams.
Familiarity with agentic AI patterns (multi-step reasoning, tool-calling, orchestration) and the risk controls needed to govern autonomous actions in development workflows.
Hands-on experience with AI-assisted development tools (Copilot, Cursor, AI-driven testing, or prompt-based automation) and the ability to write testable acceptance criteria for AI features, including non-deterministic behavior, guardrails, and fallback conditions.