Principal Product Manager, CX AI & Automation

F5•San Jose, CA

About The Position

We are seeking a Principal Product Manager, AI to serve as the agentic catalyst for our customer experience organization. This is not a role that administers a backlog. It is a strategic bridge between the business and the technology organization, accountable for re-architecting how the enterprise operates when generative and agentic AI are treated as first-class participants in the work. The role establishes a single, technically grounded owner for CX AI delivery. Diffuse ownership today produces fragmented pilots, duplicated spend, and shadow AI adopted without evaluation, security review, or measurement. The incumbent consolidates that ownership, sets the standards by which CX AI solutions are built and approved, and becomes the accountable decision-maker for what gets built, bought, and stopped. The role also protects delivery velocity: scaling agentic workflows requires dedicated product leadership that carries the discovery, definition, and prototyping load itself, engaging core engineering deliberately rather than depleting bandwidth committed to foundational platform work. Scope spans the full customer lifecycle, from first touch through renewal and advocacy. Go-to-market motions represent a substantial portion of that scope, alongside post-sale service, self-service and digital channels, and the internal workflows that determine whether the experience is consistent or fragmented. Success requires an uncommon blend of AI product management, hands-on software development, and domain expertise across customer-facing operations.

Requirements

Demonstrated ability to re-architect business operations rather than optimize existing process, with a record of changing how work is done.
Deep understanding of the customer lifecycle and its operating models, including demand generation, pipeline management, quote-to-cash, onboarding, support and case resolution, renewals, and partner channels.
Command of operational and experience measurement alike, spanning cycle time, cost to serve, and resolution rates alongside satisfaction, effort, and retention indicators.
Strong product judgment, distinguishing substantive problems from stated requests, defining measurable outcomes, and declining work with clear rationale.
Sufficient engineering depth to assess feasibility, challenge estimates, and prototype independently.
Sound reasoning about agentic AI capabilities and limits, data dependencies, integration complexity, evaluation methodology, and cost drivers.
Governance instinct balanced with pragmatism, setting standards teams adopt willingly rather than route around.
Outstanding written communication, producing strategy documents and business cases upon which executives and delivery teams act.
Executive presence across commercial, operational, and technical audiences, with proven effectiveness as an interpreter between them.
Strong influence without formal authority across functions and geographies.
Bachelor's or Master's degree in a business, technology, or analytical discipline, or an equivalent combination of education and experience.
10+ years of combined experience spanning AI or technology product management, software development, and customer-facing business functions or the systems supporting them.
Hands-on software or application development experience, current enough to prototype and evaluate technical approaches independently.
4+ years in or supporting customer experience or go-to-market functions, including marketing, sales, revenue operations, deal desk, support, customer success, or partner operations.
Demonstrated delivery of production generative AI or agentic solutions at enterprise scale, with measurable improvement in experience quality, cost, cycle time, or revenue.
Practical command of contemporary AI patterns, including large language models, agents and multi-agent orchestration, retrieval-augmented generation, conversational AI, and intelligent document processing.
Experience establishing governance, standards, or review processes for a technology domain, with evidence of adoption by teams that did not report to you.
Working knowledge of the CX systems landscape, including CRM, CPQ, marketing automation, case and service management, knowledge and self-service platforms, and underlying data platforms.

Nice To Haves

Hands-on experience with AI-assisted development tools, agent frameworks, or low-code automation platforms.
Experience delivering automation across the Salesforce and Oracle stacks, spanning new product introduction (NPI), configure-price-quote (CPQ), and partner self-service built on a partner relationship management (PRM) platform.
Familiarity with AI evaluation and observability practices, including how solution quality is measured over time.
Experience with conversational AI or agent-assist deployments in a service or support context.
Experience in a startup or early-stage environment spanning discovery, definition, and rollout.

Responsibilities

Own the AI product strategy and roadmap for CX, aligned to measurable business outcomes rather than a catalog of pilots.
Map the current-state customer journey and the workflows behind it, including systems of record, cross-functional handoffs, exception paths, and undocumented practices.
Identify where agentic AI restructures the work rather than accelerating it, covering decisions suitable for delegation, steps that can be eliminated, customer friction that can be removed, and activities that should remain human.
Define target-state journeys built around agents, retrieval, and language models, specifying where human oversight is retained and why.
Establish quantified baselines and value cases across operational and experience measures ahead of any build commitment.
Translate ambiguous business problems into rigorous product definitions covering outcomes, success criteria, scope, and explicit exclusions.
Act as the single accountable owner for CX AI delivery decisions, consolidating fragmented initiatives under a common strategy and standard.
Establish and operate the intake and approval path for CX AI solutions, replacing unmanaged shadow AI with a governed alternative teams prefer to use.
Define the bar a solution must clear before deployment, including evaluation coverage, quality thresholds, human oversight, customer transparency, and rollback conditions.
Own build-versus-buy and vendor decisions for CX AI capability, weighing capability, cost, risk, and time to value.
Partner with Architecture, Data Engineering, Security, Risk, and Legal and Compliance to satisfy data privacy, security, and responsible AI requirements by design.
Maintain visibility into AI spend and unit economics across the portfolio, and discontinue initiatives that do not earn continued investment.
Drive the agentic roadmap as a dedicated parallel track, engaging core engineering capacity deliberately and preserving foundational delivery commitments.
Prototype independently to prove concepts and de-risk approaches ahead of formal engineering investment.
Author the requirements and technical specifications teams deliver against, at a precision that avoids rework.
Own outcomes beyond deployment, driving adoption across customer-facing teams and refining solutions based on production behavior.
Report on adoption, operational efficiency, experience quality, and realized return on investment, including where value was not achieved.
Codify successes into reusable patterns and reference journeys other functions can adopt without bespoke work.
Establish the roadmap for maturing AI proficiency across CX internal roles, including AI adoption within the software development lifecycle, so that roles are elevated rather than simply made faster.
Advance AI fluency across CX teams and present strategy, trade-offs, and recommendations to executive leadership.