Manager, CX Knowledge & Enablement

Harvey

1d•$144,000 - $216,000

About The Position

Harvey is seeking a Manager of CX Knowledge & Enablement to build and lead the infrastructure and team that support customer experience as the product evolves. The function is new, and while some foundation exists, the role will define its remit and roadmap. Reporting to the CX Operations Manager, this person will own knowledge infrastructure for customer-facing and AI-assisted work, as well as competency, onboarding, training, and quality assurance programs. The goal is to help CX specialists build confidence, handle complex customer issues, and meet performance targets through practical learning and support tailored to their needs. The manager will lead a team responsible for execution while remaining hands-on during the build phase—designing the knowledge architecture, establishing initial standards, and proving the operating model. They will develop enablement for an AI-assisted support organization, building a reliable knowledge layer for both people and AI systems and scaling the team's impact as the organization grows.

Requirements

8+ years of experience across knowledge management, support enablement, education, trust and safety, or a related function, including experience building or leading a team and owning a system rather than a program.
Demonstrated experience architecting knowledge for service at scale, including building a knowledge system for a large user and AI-agent population and explaining its architecture decisions and outcomes.
A clear point of view on how enablement changes when agents handle first-line support and specialists focus on complex cases.
Track record of owning a knowledge consolidation or platform migration that retired fragmented surfaces and moved an organization onto one system.
Experience launching, managing, and improving knowledge or training platforms, with measurable operational improvement.
Data-driven approach to impact measurement, including defining success metrics and instrumenting ramp, quality, and resolution outcomes.
Strong instructional design range and judgment in selecting learning formats and adapting content for different audiences, tenure levels, and learning preferences.
Experience working in fast release cycles and rapidly adapting content as products and processes evolve.
Deep familiarity with AI-assisted support workflows and the needs of agents and specialists handling complex technical customer issues.
Experience building content for AI systems as well as people, or a clear point of view on what changes when both use the same knowledge base.
Strong cross-functional partnership skills across Product, Engineering, AI, and GTM, including driving adoption without directly owning the technology.
Excellent communication skills and the presence to establish organizational credibility for the team's work.

Nice To Haves

Experience supporting a legal, regulated, or otherwise high-stakes customer base.

Responsibilities

Own the end-to-end knowledge infrastructure strategy, including architecture, consolidation from fragmented surfaces, governance, and platform decisions across training, product best practices, troubleshooting, and product support.
Design knowledge so that it is structured, retrievable, and reliable for both human and AI consumers.
Define support competency standards, including readiness from onboarding through mastery, how it is measured, and how it is maintained as the product changes.
Own the quality system as a diagnostic layer, routing knowledge problems such as incorrect, missing, or hard-to-find content back into the platform.
Build a release-to-readiness loop with Product and Product Ops to prepare the organization for product releases before support volume rises.
Design learning strategies that select appropriate formats—including instructor-led sessions, self-paced modules, documentation, video, peer learning, and in-workflow guidance—for audiences, content, and urgency.
Build and lead the team that maintains the system, hiring ahead of need, setting a high bar, and scaling team capability.
Measure outcomes such as specialist ramp time, performance-target attainment, readiness for new releases, resolution quality, and self-service and agent-handled deflection; combine quantitative data and specialist feedback, develop action plans, and report effectiveness to leadership.
Use AI to generate and maintain content, identify staleness and knowledge gaps from ticket signals, and automate enablement work where appropriate.
Partner with the AI Support team, Engineering, Product, and GTM Enablement so the knowledge layer supports dependent surfaces, and own agent-facing content.