Knowledge Manager AI and Automation

Job Purpose

The Knowledge Manager will lead the organization’s transition to an AI-ready knowledge state by establishing strong knowledge governance, improving content quality and structure, and reducing fragmentation of information spread across multiple tools (e.g., SharePoint, ServiceNow, Confluence, Google Drive, Slack/Teams, ticketing systems, wikis). This role will define and drive a scalable knowledge operating model so content is findable, trustworthy, reusable, and ready for AI-powered search, assistants, and automation.

Key Activities

1) Knowledge Strategy & AI-Readiness

• Define and deliver a knowledge strategy aligned to business goals and AI enablement (search, copilots, RAG/LLM assistants, automation).
• Establish an “AI-ready content” framework: standards for structure, metadata, taxonomy, freshness, and ownership.
• Partner with data/AI teams to ensure knowledge content can be safely and effectively used in AI applications (e.g., retrieval quality, chunking readiness, access controls).

2) Content Governance & Operating Model

• Create governance for distributed content teams: ownership, approval workflows, review cadences, and lifecycle management.
• Set policies for what should be documented, where it should live, and how it should be maintained.

• Define and operationalize KPIs (e.g., findability, reuse, deflection, freshness, quality score, duplication rate).

3) Tool & Content Consolidation (Multi-Tool Environment)

• Assess current knowledge ecosystem and map content across repositories and workflows.
• Rationalize tools and define “source of truth” principles and publishing models (authoring vs. consumption layers).
• Lead initiatives to reduce duplication, consolidate critical knowledge, and improve discoverability across systems.

4) Taxonomy, Metadata, and Information Architecture

• Design and maintain enterprise taxonomy and controlled vocabulary aligned to products, processes, customers, and internal functions.
• Define required metadata and templates to support filtering, search relevance, and AI retrieval accuracy.
• Implement content standards: naming conventions, tagging rules, page structures, and content types (FAQs, SOPs, how-tos, troubleshooting, policies).

5) Knowledge Quality, Lifecycle, and Content Excellence

• Establish a content quality framework (accuracy, clarity, completeness, accessibility, compliance).
• Run regular audits to identify stale/duplicate/low-value content and drive remediation.
• Create templates, style guides, and playbooks for authors across the organization.

6) Change Management & Enablement

• Drive adoption of knowledge practices through training, communications, and stakeholder engagement.
• Build a community of practice for knowledge owners and contributors.
• Coach teams on writing for reuse and AI (task-based writing, modular content, consistent terminology).

7) Cross-Functional Collaboration

• Partner with IT, Security, Legal/Compliance, Product, Support/Customer Success, HR, and Operations to ensure knowledge is governed and usable.
• Align knowledge practices with enterprise search, identity/access management, and data privacy requirements.
• Support AI initiatives by providing curated, high-quality knowledge sources and feedback loops.

Experience

Required Skills & Experience

• 5–10+ years in knowledge management, content operations, information architecture, or enterprise content management.
• Experience building governance models and driving adoption across multiple teams/tools.
• Strong understanding of taxonomy, metadata, and content lifecycle management.
• Practical experience improving enterprise search and/or preparing content for AI retrieval (RAG), copilots, or automation.
• Excellent stakeholder management and change leadership skills.
• Strong writing/editing and content design skills (clarity, reuse, structured content).
• Experience with tools such as Confluence, SharePoint, ServiceNow Knowledge, Zendesk Guide, Salesforce Knowledge, Notion, Guru, or similar.
• Familiarity with search technologies (Microsoft Search, Elastic, Coveo, Google Cloud Search) and relevance tuning.
• Understanding of AI/LLM concepts (RAG, embeddings, chunking, prompt patterns, evaluation of answer quality).
• Experience in regulated environments with content compliance, retention, and access controls.