Manager of AI-Enabled Knowledge Management

MAKE STRATEGY A REALITY | ACCELERATE YOUR GROWTH | CHOOSE YOUR PATH As the world's leading change and transformation consultancy, we're helping businesses move from strategy to reality by taking a pragmatic and practical approach to build solutions that last. We're seeking a Manager of AI-Enabled Knowledge Management in our Atlanta, GA office to help evolve how the firm captures, curates, governs, and activates knowledge in an AI-powered era.

This role will play a strategic and operational part in strengthening the firm's knowledge foundation-our competitive advantage- by improving content quality, discoverability, reuse, and integration into client delivery and business development workflows.

As part of the Knowledge & Strategy function within the Strategy & Growth team, the AI-Enabled Knowledge Management Manager will lead end-to-end knowledge lifecycle activities, from capture and curation to taxonomy, governance, and adoption. The role partners with IT / Data / AI owners to define KM requirements for RAG, semantic search, metadata, knowledge quality, source validation, and governance. The role will also incorporate applied and builder-level AI capabilities, helping design and implement AI-enabled knowledge workflows (e.g., copilots, metadata automation, semantic search) that improve speed, accuracy, and usability while maintaining strong governance and trust.

The role will partner closely with Practice leaders, account teams, Strategy & Growth, Marketing, Technology, and client-facing teams to ensure knowledge is high-quality, accessible, and embedded into how the firm sells and delivers work. The role will also cross-train with Research colleagues to support secondary research needs when required, though research is not a primary responsibility.

• A) Knowledge asset lifecycle & IP reuse Oversee knowledge capture processes for priority assets, including project deliverables, proposals, qualifications, methodologies, and reusable client assets. Partner with subject matter experts and account teams to ensure content accuracy, relevance, and alignment with business priorities. Accountable for AI knowledge architecture and AI quality controls, partnering with AI COE, IT, teams Define and maintain content curation standards, including quality, context, sensitivity, and reuse guidelines.
  
  Identify content gaps, duplication, and outdated materials; drive refresh, consolidation, or retirement actions. Facilitate knowledge-sharing sessions and enablement activities for client-facing teams.
• B) AI-enabled knowledge systems and workflow design Apply AI tools to improve knowledge workflows, including summarization, classification, metadata enrichment, and content discovery. Help design and scale AI-enabled KM capabilities (e.g., copilots, knowledge assistants, semantic search, metadata automation). Develop and maintain reusable prompt libraries, template, and workflow playbooks for KM use cases. KM product owner / roadmap partner for Hub / search / AI-enabled knowledge experiences Partner with Technology and business stakeholders to define requirements for AI-enabled knowledge systems.
  
  Evaluate AI-generated outputs for accuracy, relevance, and alignment with firm standards. Support experimentation and adoption of emerging AI KM capabilities.
• C) Taxonomy, metadata, and knowledge architecture Own and evolve taxonomy, metadata, tagging, and content structures to improve findability and reuse as enterprise search, AI, and semantic retrieval evolves. Translate business needs and user behavior into practical information architecture improvements. Establish and maintain metadata standards and controlled vocabularies. Enable AI-assisted tagging and metadata workflows with appropriate governance and validation. Use analytics and user feedback to continuously improve search relevance and knowledge experience.
• D) Governance, quality, and trust Maintain governance practices for knowledge quality, lifecycle management, and content ownership. Define and enforce standards for content validation, freshness, and reuse. Partner with Legal, Compliance, and Technology to ensure secure and responsible knowledge use. Drive and promote responsible AI practices, including source validation, transparency, human-in-the-loop review, content provenance, confidentiality/permissions, AI answer-quality feedback loops, AI-ready knowledge standards.
  
  Monitor and report on KM health metrics (quality, usage, adoption, gaps).
• E) Adoption and knowledge-sharing culture Drive adoption of KM tools, practices, and AI-enabled workflows through promotion, training, and stakeholder engagement. Develop communications, playbooks, and enablement materials to support knowledge reuse. Build strong relationships across teams to encourage contribution and continuous improvement. Translate KM and AI concepts into clear, business-relevant messaging.
• F) Insights integration & business value Support secondary research efforts (e.g., market scans,…