Manager, CX Knowledge & Enablement

Harvey•New York, NY

1d•$144,000 - $216,000

About The Position

Harvey is seeking a Manager of CX Knowledge & Enablement to build and lead a new function supporting its customer experience organization. Reporting to the CX Operations Manager, this person will manage the Enablement team and own the knowledge infrastructure, competency programs, and quality assurance programs used by customer-facing teams and AI-assisted workflows. The role defines the function’s scope and roadmap, and requires hands-on work establishing its architecture, standards, and operating model during a build phase. The goal is to help CX specialists build confidence, handle complex customer issues, and consistently meet performance targets through practical learning and tailored support. The manager will build a knowledge system that is structured, retrievable, and reliable for both people and AI consumers; establish onboarding, ongoing training, competency, and quality programs; and ensure product changes translate into organizational readiness. The role also leads a team, measures downstream operational outcomes, applies AI to knowledge and enablement work, and partners across Product, Product Ops, Engineering, AI Support, and GTM Enablement.

Requirements

8+ years 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 point of view on how enablement changes when agents handle first-line support and specialists handle more complex cases, including how competency standards and training should evolve.
Track record of owning a consolidation or platform migration that retired fragmented surfaces and moved an organization to one system.
Experience launching, managing, and improving knowledge or training platforms, with measurable operational improvements.
Data-driven approach to measuring impact, including defining success metrics, instrumenting ramp, quality, and resolution outcomes, and using results to guide investment.
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 requirements for agents and specialists to handle complex technical customer issues confidently.
Experience building content for AI systems and people, or a clear point of view on how shared knowledge bases must change for both audiences.
Strong cross-functional instincts and ability to partner with Product, Engineering, AI, and GTM to build and drive adoption without directly owning the technology.
Excellent communication skills and the presence to establish credibility for the team’s work across the organization.

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 enablement, training, product best practices, troubleshooting, and product support.
Structure knowledge so it is retrievable and reliable for human and AI consumers.
Define support competency standards, including readiness from onboarding through mastery, measurement, and ongoing readiness as the product changes.
Own the quality system as a diagnostic layer and route knowledge issues—such as incorrect, missing, or hard-to-find content—back into the knowledge platform.
Build a release-to-readiness process with Product and Product Ops to convert upcoming releases into organizational readiness before ticket volume increases.
Design learning strategies and select appropriate formats, including instructor-led sessions, self-paced modules, documentation, short-form video, peer learning, and in-workflow guidance.
Build and lead the team responsible for maintaining 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-serve and agent-handled deflection; combine metrics with structured specialist feedback, develop action plans, and report effectiveness to leadership.
Use AI to generate and maintain content, identify staleness and gaps from ticket signals, and automate enablement work where appropriate.
Partner with AI Support, Engineering, Product, and GTM Enablement so the knowledge layer serves dependent surfaces, and own agent-facing content.