AVP, AI Product Manager

LPL Financial•Charlotte, NC

2d•$109,270 - $182,117•Hybrid

About The Position

Where Ambition Meets Innovation Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals. Job Overview: The Assistant Vice President, Advisor AI Solutions will lead product management for a portfolio of advisor-facing AI experiences within the AI Business Solutions (ABS) team — the product management and business impact arm of the Chief Data & Artificial Intelligence Organization. Reporting to the VP of AI Business Solutions, this role owns the end-to-end product development lifecycle for key advisor-facing AI capabilities, from opportunity framing and solution validation through pilot delivery, commercialization, and scaled adoption. This AVP will define and deliver advisor chat and agentic AI experiences, evaluate and orchestrate third-party AI products, and ensure AI capabilities drive measurable business impact aligned to enterprise OKRs. The role operates in a four-in-a-box model with Technology, Business, and Operations & Risk co-designing product definitions — including data contracts, evaluation frameworks, and governance documentation — for engineering to implement at scale. This hybrid role is required to sit out of our Fort Mill, SC San Diego, CA, or NYC hub office at least 3 days per week onsite.

Requirements

Bachelor’s degree in Business, Computer Science, Information Systems, or related field (Master’s preferred).
3+ years of product management experience.
Experience building and deploying AI/ML or Generative AI products into production environments preferred.
3+ years experience defining KPIs and measuring business impact of AI initiatives.
3+ years experience partnering with Engineering and Data Science in regulated environments.
Product Management Excellence Ability to define clear roadmaps tied to OKRs and deliver measurable outcomes.
Experience managing full product lifecycle in enterprise environments.
AI Product Fluency Hands-on familiarity with: Copilot and chat-based experiences Agentic systems Monitoring and evaluation frameworks
Stakeholder Communication Ability to translate technical concepts into business value.
Strong written and executive communication skills.
Matrixed Leadership Ability to influence without authority and deliver results in four-in-a-box operating model.

Nice To Haves

Wealth management or advisor platform experience.
Prior experience deploying AI within FINRA/SEC-regulated environments.
Strong financial acumen and ability to build business cases and drive / track ROI.
Experience driving field adoption and change management at scale.

Responsibilities

Advisor Chat & Agentic Experiences Define product vision and roadmap for key advisor-facing AI capabilities which may span prospecting, planning, investment management, client service or practice management.
Translate advisor workflow friction into AI-powered solutions that reduce manual effort, improve advisor experience, and enhance client outcomes.
Deliver production-grade AI capabilities with defined success metrics and adoption plans.
Pilot Design, Validation & Enterprise Scaling Lead disciplined experimentation from hypothesis through pilot validation.
Establish measurable success criteria (time savings, quality lift, revenue impact) before scaling.
Develop commercialization plans for successful pilots and manage phased rollout to enterprise adoption.
Third-Party AI Evaluation & Vendor Orchestration Apply documented build-vs-buy-vs-rent frameworks to evaluate internal vs. external AI capabilities.
Conduct market, competitive, and capability assessments of potential vendor partners.
Partner with Technology and Risk to integrate third-party AI solutions under enterprise guardrails.
Partnership with Technology Co-develop product definitions including: Product requirements and user stories Data contracts and signal definitions Model evaluation criteria Ensure clean handoff for scalable engineering implementation.
Governance & Responsible AI Ensure all AI products meet compliance, legal, risk and regulatory requirements before deployment.
Adoption & Value Realization Own rollout strategy, training integration, and advisor enablement materials.
Partner with field leadership to drive sustained adoption.
Track realized benefits against forecasted business case.
Instrumentation & ROI Maintain benefits ledger and KPI dashboards.
Measure effect sizes (uplift), manual work reduction, and revenue/NPS impact.
Establish feedback loops to continuously optimize AI performance.