VP II, Advisor AI Solutions

What if you could shape the future of LPL Financial's advisor experience through AI-powered products, contributing directly to the organization's mission to transform insights into a system of action that improves advisor productivity and client outcomes.

Job Overview

The Vice President II, Advisor Solutions will lead product management for advisor-facing AI experiences within the AI Business Solutions (ABS) team; the product management and business impact arm of the CDAO organization. This role owns the full lifecycle of advisor-facing AI products, from opportunity identification and solution validation through pilot delivery and enterprise adoption.

Reporting to the SVP of AI Business Solutions, this leader will define advisor chat and agentic experiences, evaluate and orchestrate third-party AI products, and drive the commercialization of AI capabilities that deliver measurable business impact. The role operates in a four-in-a-box model with Technology, Business, and Operations and Risk co-designing product definitions, including data contracts, evaluation evidence, and governance documentation, for engineering to implement at scale and strategic prioritization to accelerate business outcomes.

This leader will partner closely with product leaders (Home Office Solutions, Data Science Solutions, Business Reporting & Analytics) and cross-functional teams, including Technology, Operations and Risk, and the Business to ensure every AI product is strategically aligned, responsibly governed, and delivers tangible advisor value.

This hybrid role is required to sit out of our Fort Mill SC or NYC hub office at least 3 days a week onsite.

• Advisor Chat & Agent Experiences:
  Define product vision and roadmap for advisor-facing AI capabilities spanning prospecting, financial planning, investment management, client servicing, and practice operations. Translate advisor pain points into AI-powered solutions that reduce manual effort and improve client outcomes.
• Third-Party AI Evaluation & Orchestration:
  Assess build-vs-buy-vs-rent decisions for AI capabilities; evaluate third-party AI products for fit-for-purpose, cost, and speed; manage vendor relationships and integration requirements where external solutions deliver faster or more economical outcomes.
• Pilot Design & Enterprise Scaling:
  Lead disciplined experimentation from hypothesis through pilot validation; develop commercialization plans for successful pilots; manage phased rollout to enterprise adoption with clear success criteria at each stage.
• Advisor Journey Mapping:
  Document advisor workflows and identify automation opportunities; prioritize use cases based on time savings, revenue impact, and advisor satisfaction; maintain a pipeline of validated opportunities for product development.
• Integral Partnership with Technology:
  Co-design product definitions with engineering partners, including product requirements, data contracts, model artifacts, KPIs, and SLOs. Ensure clear handoff for Technology to implement and scale.
• Governance & Compliance:
  Prepare RAIC (Responsible AI Council) documentation and evidence packs; ensure all AI products meet governance, privacy, and regulatory requirements before deployment.
• Adoption & Value Realization:
  Own rollout strategy, training integration, and feedback loops; partner with field leadership to drive adoption; track and report business impact to executive leadership.
• Instrumentation & ROI:
  Maintain benefits ledger, KPI dashboards, adoption telemetry; report effect sizes (uplift), manual work reduction, and revenue/NPS lift.
• Intelligence Layer charter:
  Define signals, data contracts, and evaluation telemetry to engineer network economies that align with the Strategic Data Foundation and systems of record in partnership with Data Science solutions to help optimize an advisor’s day.
• Value stream ownership:
  Prioritize CRITICAL advisor journeys, such as Regular Reviews & Ongoing Advice, Onboarding, Discovery, Rebalancing, and Compliance, and deliver measurable cycle time and quality improvements.
• Trust & Governance:
  Implement risk-tier routing (T1–T4), LLM judges, and an immutable audit trail; achieve 100% RAIC…