Senior Director, Internal AI Product Owner

Overview The AI Product Owner owns the product definition, delivery rhythm, and value realization for internal AI capabilities. This role bridges business stakeholders, AI engineering, architecture, data/platform teams, security, legal, change management, and operations to turn high-value workflows into production-ready AI products.  The role is intentionally both product-led and technically fluent. The AI Product Owner does not need to be the primary AI engineer, but must understand enough about LLMs, agentic workflows, retrieval, data readiness, evaluations, guardrails, observability, and cost controls to shape strong requirements, challenge weak assumptions, and make delivery tradeoffs visible.  This is also a hands-on builder role. The AI Product Owner should be able to use tools such as Codex, Claude / Claude Code, and comparable AI development environments to prototype workflows, inspect code and data shapes, test prompts and agent behaviors, validate technical assumptions, and create lightweight working examples that help business and engineering teams move faster. Responsibilities Product Strategy and Roadmap   Own the product vision, roadmap, release plan, and prioritized backlog for assigned AI products, copilots, agents, automation patterns, and internal AI capabilities.  Convert business problems into AI product opportunities with clear users, jobs to be done, value hypotheses, constraints, risks, and measurable outcomes.  Partner with business owners to separate viable production use cases from experiments, demos, and low-value automation requests.  Maintain a clear product source of truth covering active use cases, owners, status, blockers, next decisions, value potential, launch readiness, and governance posture.  Drive prioritization using business value, user pain, feasibility, data readiness, security risk, operational complexity, and strategic alignment.  AI-First Requirements and Solution Shaping   Translate business workflows into product requirements for AI assistants, copilots, agents, retrieval-augmented generation, document understanding, generative analytics, workflow automation, and human-in-the-loop experiences.  Write PRDs, user stories, acceptance criteria, workflow maps, model behavior requirements, evaluation criteria, launch requirements, and operational runbooks.  Define where AI should assist, automate, escalate, or defer to a human, including confidence thresholds, exception paths, approval flows, and audit requirements.  Partner with AI engineering and architecture to shape requirements for prompts, tools/functions, data sources, retrieval design, structured outputs, model routing, integrations, and telemetry.  Ensure requirements cover data access, privacy, tenant boundaries, retention, security controls, compliance constraints, and operational support needs.  Hands-On AI Building and Technical Prototyping   Use Codex, Claude / Claude Code, and comparable AI-enabled development tools to build lightweight prototypes, prompt flows, agent workflows, evaluation examples, workflow automations, and proof-of-concept experiences.  Get hands-on with product discovery and feasibility testing by inspecting code, APIs, logs, data schemas, sample records, model outputs, retrieval results, and telemetry where appropriate.  Create working artifacts that clarify requirements, accelerate engineering alignment, and help stakeholders see what a proposed AI capability would actually do.  Test model behavior, prompt/tool interactions, edge cases, failure modes, guardrail needs, and human-in-the-loop escalation patterns before work is scaled into production.  Know the boundary between prototype and production: use hands-on building to reduce ambiguity and risk, while partnering with engineering for secure, maintainable, supported implementation.  Delivery Execution   Serve as day-to-day product owner for agile delivery teams building internal AI products and shared AI capabilities.  Manage backlog grooming, sprint planning inputs, dependency tracking, acceptance review, release readiness, stakeholder demos, and post-launch iteration.  Coordinate across AI engineers, architects, data engineers, platform teams, application teams, UX/design, Legal, Security, GRC, support, and functional business owners.  Keep delivery decisions explicit: scope, tradeoffs, risks, dependencies, assumptions, unresolved questions, and decision owners.  Help teams choose the right build, buy, partner, or platform path based on speed, maintainability, governance, cost, and strategic leverage.  Translate continuously between business and technical teams so product priorities, technical constraints, user impact, risk, and executive decisions stay aligned.  Evaluation, Quality, and Responsible AI   Define product success metrics for each AI capability, including accuracy, relevance, task completion, cycle-time reduction, user satisfaction, adoption, cost, latency, and business impact.  Establish evaluation sets, test scenarios, red-team cases, human review workflows, and launch gates appropriate to the risk level of each use case.  Partner with engineering to monitor hallucination risk, prompt/tool failures, retrieval quality, model drift, latency, token usage, cost, user feedback, and incident patterns.  Ensure AI products include guardrails, fallback behavior, explainability where needed, safe escalation paths, logging, auditability, and support handoffs.  Drive responsible AI practices in partnership with Legal, Security, Privacy, Risk, Architecture, and business stakeholders.  Adoption, Enablement, and Value Realization   Own launch planning, stakeholder readiness, training, communications, adoption tracking, and user feedback loops for assigned AI products.  Partner with AI COE enablement efforts to help teams use AI responsibly and effectively, including playbooks, examples, office hours, and repeatable patterns.  Tra