Enterprise Knowledge Management Specialist

About This Role

This role will establish, operationalize, and scale the Bank's Enterprise Knowledge Management Capability within the Data & AI function. The specialist will partner closely with various internal groups to define enterprise knowledge governance standards, operating models, taxonomy, controls, and AI-ready knowledge practices. This position acts as the framework architect, enterprise catalyst, and implementation partner, developing strategy, roadmaps, standards, and adoption approaches to bring the framework into practical execution.

Responsibilities

• Partner with Group Risk Management (GRM) to define and implement the Bank's enterprise knowledge management strategy, roadmap, and operating model
• Position knowledge as a governed enterprise asset supporting productivity, operational resilience, compliance, and AI effectiveness
• Develop practical implementation plans to move from fragmented repositories to trusted, reusable, and AI-ready enterprise knowledge
• Establish business cases, value measures, and adoption plans for prioritized knowledge domains
• Partner with GRM and enterprise stakeholders to establish knowledge governance standards, policies, controls, and implementation playbooks
• Define roles and responsibilities across knowledge owners, stewards, custodians, users, and governance forums
• Design lifecycle management processes covering knowledge creation, review, approval, publication, consumption, refresh, retention, and retirement
• Develop review cadences, quality criteria, certification standards, and escalation routines for critical knowledge assets
• Lead phased implementation across priority business and support functions, starting with high-value domains
• Translate the framework into practical working routines, templates, control checkpoints, training, and adoption materials
• Coordinate cross-functional execution across Risk, Technology, Compliance, Operations, HR, and business domain teams
• Drive change management and behavioural adoption without replacing business ownership of content
• Develop enterprise taxonomy, metadata, and classification standards for knowledge assets
• Define standards for ownership, authoritative source identification, versioning, confidentiality, effective date, review date, and content status
• Improve discoverability and reusability of policy, process, control, risk, product, operational, and technical knowledge
• Establish information architecture principles for structured and unstructured knowledge repositories
• Define standards for AI-consumable and AI-trustworthy knowledge assets supporting Microsoft Copilot, Enterprise Search, RAG, Knowledge Graphs, and AI Agents
• Partner with Data, AI, and Technology teams to improve retrieval quality, grounding accuracy, source traceability, and user trust