AI Product Owner - Vice President

About the role

The AI Product Owner will lead the strategy and delivery of AI-enabled digital servicing experiences across Morgan Stanley and E*TRADE client platforms. Key responsibilities include owning OKRs, partnering with UX, managing dependencies, and ensuring AI systems improve client experience, operational efficiency, and risk posture.

Responsibilities

• Define product vision and OKRs for AI-enabled servicing (e.g., containment/deflection, time-to-resolution, CSAT/NPS impact, cost-to-serve reduction, risk events reduction).
• Own a metrics-first operating cadence: set baselines, targets, instrumentation requirements, and post-launch optimization loops.
• Lead end-to-end product development: problem framing → discovery → delivery → launch → optimization, using usage data, client feedback, and competitive intelligence as inputs.
• Translate ambiguous needs into AI-suitable scope and testable acceptance criteria, including explicit "do-not-automate" boundaries and human oversight needs.
• Build and maintain a prioritized backlog in Jira and manage roadmap sequencing across multiple platforms and legacy services.
• Drive alignment across many dependency teams (technology, service, UX, Legal, Risk, Compliance, Data, Operations).
• UX ownership (experience and conversation design): co-own IA, content strategy, and interaction design with UX. Ensure experiences meet usability standards: clarity, recovery from failure, safe fallback behavior, and accessible design. Establish and enforce design patterns across unified platforms to reduce inconsistency and cognitive load.
• AI evaluation, quality, and release readiness: Define AI quality measures (task success rate, hallucination/error rates, escalation accuracy, safety policy adherence). Own evaluation strategy: offline test sets, human review workflows, pilot/A-B plans, and regression checks. Ensure release readiness includes monitoring, rollback, incident playbooks, and measurable guardrails.
• Embedded risk management for AI + digital servicing: Identify risks that impact roadmap delivery and client outcomes, expanding to include AI-specific risks: privacy leakage, unsafe responses, bias, explainability expectations, and misuse. Partner with Legal, Risk, Compliance, and Fraud to define required controls (approvals, logs, disclosures, audit trails) and integrate into the Definition of Done.
• Data product ownership for AI readiness: Drive requirements for data access, quality, labeling/ground truth, taxonomy, and lifecycle management needed to support virtual agents and servicing automation. Ensure analytics/events are implemented to measure OKRs and model performance in production. Hands-on acceleration: Create and maintain dashboards for OKRs/KPIs, experimentation results, and operational health (containment, escalations, top intents, failure modes, drift indicators). Use approved tooling to generate code snippets, API examples, test scripts, prompt/policy configurations, and lightweight prototypes to accelerate engineering throughput (with appropriate review and SDLC controls).
• Stakeholder leadership & business reviews: Orchestrate business reviews, exec updates, and working forums (planning, materials, execution, follow-ups) as in the original role, adding AI program reporting: risk posture, evaluation results, and production health.

Requirements

• 9.5 Years of transferable experience across work and higher education.
• Master of Business Administration (MBA) or Bachelor’s degree (BS/BA) with ample work experience.
• 5+ years building digital products/platforms, including backlog management, roadmap planning, and metrics ownership.
• Experience owning digital containment KPIs (e.g., containment/deflection, escalation precision, task success rate) and operating a post-release optimization loop.
• Experience defining and running AI evaluation (offline 'golden set', regression testing, human review rubric) and production monitoring/incident response.
• Ability to define AI product requirements: guardrails, human-in-the-loop points, evaluation metrics, and monitoring.
• Ability to understand technical architecture and code, converse in detail with engineering about APIs, logs, and system diagrams; able to work effectively in legacy architectures and across multiple dependency teams.
• Uses approved AI/dev tooling to produce reviewable code artifacts (scripts, prototypes, test cases, prompt/policy configs) to accelerate delivery; engineering owns production implementation, review, and SDLC compliance.
• Demonstrated capability with UX and engineering to deliver high-quality, client-friendly experiences — including ownership of end-to-end flows, content, and interaction patterns.
• Strong written/verbal communication, critical thinking, organization, and ability to drive cross-functional alignment.

Qualifications

• Experience partnering with Legal/Risk/Compliance on customer-facing digital experiences, and capable of embedding controls into product delivery for AI-enabled features.
• Wealth management / brokerage / banking domain familiarity preferred.
• Customer servicing process knowledge (intent taxonomy, call drivers, servicing flows) preferred.

Skills

• Experience defining and running AI evaluation (offline 'golden set', regression testing, human review rubric) and production monitoring/incident response.
• Ability to understand technical architecture and code, converse in detail with engineering about APIs, logs, and system diagrams; able to work effectively in legacy architectures and across multiple dependency teams.
• Uses approved AI/dev tooling to produce reviewable code artifacts (scripts, prototypes, test cases, prompt/policy configs) to accelerate delivery; engineering owns production implementation, review, and SDLC compliance.
• Demonstrated capability with UX and engineering to deliver high-quality, client-friendly experiences — including ownership of end-to-end flows, content, and interaction patterns.
• Strong written/verbal communication, critical thinking, organization, and ability to drive cross-functional alignment.

Benefits

At Morgan Stanley, we offer a comprehensive benefits package designed to support our employees and their families at every point along their work-life journey. This includes:

• Health insurance options
• Retirement savings plans
• Flexible spending accounts
• Employee assistance programs
• Parental leave
• Wellness programs
• Professional development opportunities

Pay

Expected base pay rates for the role will be between $110,000 and $190,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.

Schedule

Morgan Stanley offers flexible schedules to accommodate the diverse needs of our employees. The exact schedule for this role will be discussed during the hiring process.