Manager, CX Knowledge & Enablement

Harvey•San Francisco, CA

1d•$144,000 - $216,000

About The Position

Harvey is seeking a Manager of CX Knowledge & Enablement to build and lead a new enablement function and own the knowledge infrastructure that supports customer-facing and AI-assisted work. Reporting to the CX Operations Manager, this role will define the function’s scope and roadmap, establish its knowledge architecture and standards, and lead competency, onboarding, training, and quality programs. The manager will remain hands-on during the build phase by designing the system, drafting initial standards, and validating the operating model before delegating ongoing execution to the team. The role is focused on helping CX specialists build confidence, handle complex customer issues, and meet performance targets through practical learning and support. It will connect product releases to organizational readiness, maintain knowledge that is structured and dependable for both people and AI systems, and use quality findings and operational data to improve the knowledge system. The manager will partner with Product, Product Ops, Product Engineering, AI Support, and GTM Enablement, and use AI to create and maintain content, identify gaps and stale information, and automate appropriate enablement work.

Requirements

At least 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.
Demonstrated ability to architect knowledge systems that can serve large populations of users and AI agents, and explain the architecture decisions and their benefits.
A clear point of view on how enablement should evolve when agents handle first-line support and specialists focus on complex cases.
Track record of owning a consolidation or platform migration that retired fragmented knowledge 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 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 needs of agents and specialists handling complex technical customer issues.
Experience creating content for AI systems as well as people, or a clear point of view on the differences 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 ability 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, migration from fragmented knowledge surfaces, governance, and platform decisions.
Ensure knowledge covering enablement and training, product best practices, troubleshooting, and product support is structured, 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 errors, gaps, and findability issues back into the knowledge platform.
Build a release-to-readiness process with Product and Product Ops that prepares the organization for releases ahead of resulting ticket volume.
Design learning strategies and choose suitable formats, including instructor-led sessions, self-paced modules, written documentation, short-form video, peer learning, and in-workflow guidance.
Build and lead the team responsible for maintaining the system; hire ahead of need, set a high bar, and develop team capabilities as the organization grows.
Measure outcomes such as specialist ramp time, performance-target attainment, readiness for new releases, resolution quality, and self-service and agent-handled resolution deflection.
Combine quantitative metrics with structured specialist feedback, develop targeted action plans, and report effectiveness to leadership.
Use AI to generate and maintain content, detect staleness and knowledge gaps from ticket signals, and automate suitable enablement tasks.
Partner with AI Support, Engineering, Product, and GTM Enablement to ensure the knowledge layer supports dependent surfaces, and own agent-facing content.