AVP, Home Office AI Product Owner

LPL Financial•San Diego, CA

3d•$109,270 - $182,117•Hybrid

About The Position

What if you could build a career where ambition meets innovation? At LPL Financial, we empower professionals to shape their success while helping clients pursue their financial goals with confidence. What if you could have access to cutting-edge resources, a collaborative environment, and the freedom to make an impact? If you're ready to take the next step, discover what’s possible with LPL Financial. Job Overview: The AVP, Home Office AI Product Owner, is accountable for transforming how operational and supervision work flows through LPL’s home office by applying AI‑enabled, human‑reviewed automation at scale. The role focuses on the highest‑volume, highest‑friction workflows that impact advisors and their clients, with a mandate to reduce cost to serve, cycle time, and operational risk in a regulated environment. This role owns outcomes, not just delivery. Success is reflected directly in the advisor and associate experience: accounts open faster, information is captured once, requirements are clear upfront, and work moves cleanly through the organization with minimal rework. Home office teams spend less time chasing status, correcting submissions, or reprocessing cases — and more time delivering consistent, high‑quality service. Reporting to the VP, Home Office AI Solutions, this role operates as a senior product leader in a complex matrix organization. You will partner closely with operations, supervision, engineering, data, risk, legal, and compliance leaders to define strategy, secure alignment, and deliver AI‑powered workflow solutions that are practical, governable, and measurable. This includes designing and deploying agentic AI patterns that assist humans while preserving accountability, review, and auditability.

Requirements

Bachelor’s degree in a related field or equivalent practical experience.
5+ years of experience in product ownership or product management delivering workflow tools, automation, or operational platforms.
5+ years experience shipping changes that reduced manual touches, cycle time, rework, or operational risk.
5+ years experience partnering with engineering teams in an agile environment, including backlog management and iterative delivery.
5+ years strong written and verbal communication skills that translate operational needs into buildable requirements.
Customer discovery: Comfortable running interviews and shadowing users, then turning messy feedback into clear product decisions.
Operational product judgment: Can pinpoint where unclear requirements and inconsistent evidence create predictable rework.
UX sensibility: Writes and reviews flows with the user in mind, including how decisions, errors, and AI outputs are presented.
Technical fluency: Comfortable working with engineering and data teams on integrations, form logic, audit trails, and reviewer workflows.
AI product literacy: Knows where AI helps in production workflows and how to design for review, overrides, and safe fallbacks.
Metrics discipline: Uses data to prioritize and to verify impact.
Stakeholder leadership: Influences without direct authority and drives alignment across operations, supervision, engineering, and data partners on decisions and sequencing.
Executive‑level communication: Able to clearly articulate strategy, tradeoffs, risk, and outcomes to senior leaders.

Nice To Haves

Experience in wealth management, brokerage, banking operations, or another regulated services business.
Familiarity with account opening, supervision review, document workflows, or e-signature experiences.
Experience implementing human-reviewed AI in production.
Familiarity with operational instrumentation and KPIs such as touches per case, return rates, queue time, reopen rates, and escalation rates.

Responsibilities

Portfolio Ownership & Strategic Prioritization: Own the end‑to‑end portfolio of AI‑enabled home office workflows, setting strategy and priorities based on volume, cycle time, rework drivers, operational risk, and measurable business impact. Partner with operations and supervision leaders to maintain a focused, outcome-driven roadmap that delivers meaningful improvements each quarter.
Customer discovery and workflow mapping: Shadow frontline work, review real cases, and map where submissions stall, return, or bounce across queues. Turn findings into specific decisions and a buildable plan.
Clear requirements at intake: Define and enforce intake standards that clearly specify required data, acceptable evidence, decision rules, and known edge cases, ensuring submissions are complete, reviewable, and automation‑ready from the start. Eliminate ambiguity that drives returns, escalations, and unnecessary manual review.
Human-centered workflow design: Design human‑centered, AI‑assisted workflows that improve the experience for advisors, investors, and home office associates while preserving accountability, explainability, and regulatory defensibility. Apply agentic AI patterns to orchestrate multi‑step tasks, surface next‑best actions, and coordinate handoffs with appropriate human oversight.
UI and interaction design partnership: Design or co-design screens and forms that make AI outputs usable and reviewable. Examples include confidence cues, reason codes, suggested next steps, and a clear override path.
Supervision and governance partnership: Collaborate with supervision, risk, legal, and compliance partners to translate policy and regulatory expectations into buildable workflow logic, evidence capture, and governance controls. Ensure all AI‑assisted solutions meet audit, supervision, and regulatory requirements by design.
Human-reviewed AI assistance: Apply AI to speed up repeatable steps with clear ownership. Examples include drafting case summaries for associate approval, extracting key fields from documents for review, and classifying requests for routing.
Measurement, Outcomes (KPI) & Continuous Improvement: Track touches per case, return rates, queue time, reopen rates, and escalation rates. Use data and user feedback to continuously iterate, tune AI behavior, and determine what to scale or retire.
Rollout and adoption: Pilot in live queues, gather feedback from associates and advisors, and drive adoption by partnering with operations leadership to embed new workflows as the default way work gets done.