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.

Responsibilities:

• 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 compliance pre-deployment in partnership with Data Science Solutions.  
• Instrumentation & ROI: Maintain benefits ledger, KPI dashboards, adoption telemetry; report effect sizes (uplift), manual work reduction, and revenue/NPS lift.  
• Vendor orchestration: Apply build-vs-buy-vs-rent with documented fit-for-purpose and ROI; integrate third-party capabilities under governance guardrails 

Objectives & Key Results (OKRs) 

Objective 1: Deliver Advisor-Facing AI Products with Measurable Impact 

• KR1: Deploy two or more advisor-facing AI capabilities from pilot to enterprise rollout in Year 1. 
• KR2: Achieve 20%+ reduction in advisor time spent on targeted workflows. 
• KR3: Deliver measurable NPS lift of 5+ points among participating advisor cohorts. 

Objective 2: Build and Govern Third-Party AI Ecosystem 

• KR1: Complete fit-for-purpose assessments for all third-party AI products under consideration. 
• KR2: Onboard one or more third-party AI solutions with demonstrated ROI advantage over internal build. 
• KR3: Achieve 100% RAIC compliance for all deployed AI products (internal and third-party). 

Objective 3: Drive Adoption and Revenue Impact 

• KR1: Achieve 70%+ adoption rate among target advisor cohorts within six months of rollout. 
• KR2: Document revenue lift per use case with validated business case for each deployed capability. 
• KR3: Establish feedback loops surfacing advisor friction to product backlog within 48 hours. 

Objective 4: Enable Cross-Functional Alignment 

• KR1: Maintain four-in-a-box operating rhythm with Technology, Business, and Operations and Risk partners, with zero critical handoff delays. 
• KR2: Conduct semi-quarterly planning sessions with partner product teams (Home Office, Data Science, Analytics). 
• KR3: Achieve 90%+ stakeholder satisfaction from field leadership and compliance partners. 

Requirements:

• A bachelor's or master's degree in Business, Computer Science, Information Systems, or a related field. 
• At least 10 or more years of experience in product management, with a proven track record of delivering AI or automation products with measurable business impact. 
• Proven experience with AI/ML product development, Co-Pilot experiences, or intelligent automation. 
• Minimum 3 years of experience managing third-party vendor ecosystems, partnerships, or build-vs-buy evaluations at enterprise scale. 
• Experience success taking products from pilot through enterprise adoption, including commercialization and value measurement. 

Core Competencies:

Product Management: Proven ability to define and execute product roadmaps from concept through enterprise adoption, with accountability for business outcomes. 

Agentic platform experience: Planner/Execution/Evaluator agents, gateways, judges, audit trail in regulated environments. 

Build vs. Buy vs. Rent Decision-Making: Experience evaluating internal development against third-party solutions, with clear frameworks for cost, speed, and fit-for-purpose assessment. 

Experimentation & Validation: Track record designing pilots, measuring outcomes, and making evidence-based decisions on scaling or pivoting. 

Technology Partnership: Ability to develop strong partnerships with engineering, co-owning product definitions while respecting delivery boundaries. 

Commercialization: Experience developing business cases, revenue models, and adoption strategies for enterprise products. 

AI Governance Fluency: Familiarity with responsible AI principles, model risk management, and regulatory requirements for AI in financial services. 

Stakeholder Communication: Clear and effective communication skills across technical, business, and field audiences. 

Matrixed Leadership: Ability to influence without authority and drive outcomes through partnership across organizational boundaries. 

Preferences 

• Financial services or wealth management background with understanding of advisor workflows. 
• Prior experience shipping AI in FINRA/SEC contexts (model risk, privacy, audit readiness) and operating a RAIC‑style governance process 
• Familiarity with responsible AI governance frameworks and model risk management. 
• Background working with field organizations on adoption and change management. 
• Experience operating in matrixed environments with four-in-a-box or similar partnership models with Technology, Business, and Operations and Risk.  
• Strong financial acumen with the ability to build business cases and track ROI for product investments. 
• Exceptional communication and stakeholder management skills across technical, business, and field audiences.