Knowledge Manager - AI and Automation

Knowledge Manager - AI and Automation

Greetings from AkzoNobel (Experts in the proud craft of making paints and coatings since 1792).

AkzoNobel has a passion for paint. We're experts in the proud craft of making paints and coatings, setting the standard in color and protection since 1792. Our world-class portfolio of brands - including Dulux, International, Sikkens and Interpon - is trusted by customers around the globe. Headquartered in the Netherlands, we are active in over 150 countries and employ around 34,500 talented people who are passionate about delivering the high-performance products and services our customers expect.

We are hiring for the role of Knowledge Manager - AI and Automation at Pune (Hybrid role), below is the job description as required.

Purpose of the job

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 responsibilities

• 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).
• 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).
• 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.
• 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).
• 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.
• 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).
• 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.

Job Requirements

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).

Preferred Qualifications

• 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.

Key skills/competency

• Knowledge Management
• AI Readiness
• Content Governance
• Information Architecture
• Taxonomy
• Metadata Management
• Enterprise Search
• Stakeholder Management
• Change Leadership
• Content Lifecycle