Product Owner, FA Desktop AI Experience, Executive Director

Morgan Stanley•Harrison, NY

1d•$150,000 - $250,000

About The Position

We are seeking an experienced Executive Director to serve as Product Owner for the FA Desktop AI Experience within the Field Experience & Platforms (FEP) organization. This role is focused on the development, integration, and adoption of AI capabilities (Generative, Agentic, etc.) across Financial Advisor (FA) and Virtual Client platforms, including partnering with broader groups within and outside of FEP as needed. This position is pivotal in advancing the advisor desktop ecosystem to drive measurable productivity gains, workflow efficiency, and enhanced client engagement. In this role, you will drive strategy, delivery, and scaling of AI-powered capabilities embedded directly into advisor platforms. Additionally, you will serve as a horizontal AI enablement leader—partnering with SMEs and peer platform owners to guide the consistent, scalable, and responsible implementation of AI solutions across the FA ecosystem. You will play a critical role in ensuring AI capabilities are intuitive, compliant, reusable, and aligned to enterprise standards, while delivering tangible business impact.

Requirements

Over 10 years of experience in product management, platform leadership, or technology strategy within financial services or similar industry.
Proven track record of owning and delivering complex, cross-functional platform capabilities that drive measurable business outcomes.
Knowledge of AI capabilities, particularly Generative AI, AI agents, and enterprise AI deployment patterns.
Experience embedding AI capabilities into production workflows within large-scale platforms.
Strong understanding of Wealth Management and Financial Advisor workflows preferred.
Familiarity with CRM ecosystems (e.g., Salesforce) and integrated desktop environments.
Knowledge of Responsible AI, model risk management, data governance, and regulatory control frameworks.
Demonstrated ability to influence peer product owners, SMEs, and development teams without direct authority.
Strong experience with Agile SDLC practices, backlog governance, and enterprise prioritization.
Exceptional communication skills, with the ability to translate complex technical concepts into clear business value for diverse audiences.
Proven ability to operate effectively within a highly matrixed, global organization across time zones.

Responsibilities

Partner to define and execute the AI product vision, strategy, and roadmap for FA Desktop and Virtual Client platforms, aligning delivery to measurable business outcomes.
Collaborate with platform owners, business stakeholders, and SMEs to identify and prioritize high-impact AI use cases that enhance advisor productivity and client servicing.
Own and manage a prioritized pipeline of AI-enabled platform enhancements, balancing innovation, regulatory requirements, technical dependencies, and platform stability.
Lead the end-to-end product lifecycle for AI capabilities—from ideation and requirements through development, testing, launch, adoption, and iterative refinement.
Serve as a trusted AI advisor to peer platform owners, guiding teams on WM AI tools, reusable components, solution patterns, and governance processes.
Advocate for scalable, reusable AI solutions that can be leveraged across CRM/Salesforce, Call Center integrations, enterprise AI initiatives, and adjacent advisor platforms.
Facilitate alignment on major design decisions, including MVP scope, workflow integration, architecture considerations, and build vs. buy evaluations.
Partner closely with Technology, UX, Business/Product, WM AI, and Legal/Compliance/Control (LCD) to ensure responsible and compliant AI deployment.
Drive adoption strategies including pilot design, training enablement, engagement frameworks, and feedback loops to ensure sustained utilization.
Establish KPIs tied to productivity, efficiency, engagement, and ROI; monitor usage and performance metrics to guide continuous improvement and scaling opportunities.
Address key AI deployment challenges—data quality, governance, model risk, architecture, and responsible AI—through the lens of advisor impact and enterprise standards.