The Evolving Role of a Knowledge Manager in the Age of AI: Collaboration, Not Redundancy

May 14, 2025
Ekta Sachania

In the age of artificial intelligence, the role of a Knowledge Manager (KM) is undergoing a transformative evolution. AI has become an accelerator for the KM function, augmenting it with automation, summarization, and intelligent recommendations. But here’s the truth: AI cannot replace the Knowledge Manager. It can only empower one.

‍Lets see how this partnership is reshaping the KM landscape.

The modern KM function now deals with:

  • On-demand knowledge delivery
  • Prompted suggestions
  • Automated content summarization
  • Intelligent classification and tagging
  • Real-time refinement and recommendations

AI can support all of these—but only with the foundation a Knowledge manager sets.

Why AI Needs Knowledge Manager to Succeed

While Large Language Models (LLMs) can generate answers, categorize content, and even create first drafts of documentation, they are only as good as the knowledge they are grounded in.

AI needs curated, validated, and context-rich knowledge—something only a KM can ensure through governance and review workflows.

  • LLMs require inputs in accurate, organization and project-specific knowledge
  • Taxonomies and metadata schemas generated by AI need KM validation to ensure they’re readable and maintainable
  • AI-generated content must align with organizational standards, tone, and branding, which requires input from the KM team.

KM’s Critical Role in the AI Era

The Knowledge Manager’s responsibilities are evolving—not disappearing. Here’s how:

  1. Workflow Governance
    • KMs approve and manage end-to-end content workflows, integrating AI prompts into content lifecycle management.
    • They define review and publishing checkpoints to ensure compliance and quality.
  2. Content Curation & Validation
    • AI may draft a proposal, case study, or playbook, but KMs ensure accuracy, consistency, and strategic alignment to the project before it is published.
  3. Taxonomy and Information Architecture
    • AI can suggest structures, but the KM ensures these structures are intuitive, machine-readable, and sustainable.
  4. Knowledge Libraries and Communities Visualization
    • KMs design the knowledge experience and adoption—how it’s discovered, consumed, and acted on—while AI may support with insights and analytics.
  5. AI Controls
    • KMs are stewards of knowledge integrity. They uphold privacy, security, and confidentiality standards, ensuring responsible use of AI in knowledge workflows that align with organizational practices.

AI is not a replacement for the Knowledge Manager—it is an augmentation tool. It enhances speed, reduces repetitive work, and surfaces insights. But it lacks context, empathy, strategic foresight, and judgment—traits that define effective KM.

In this evolving landscape, the KM role becomes even more critical, not less. With AI as a partner, Knowledge Managers have the opportunity to lead a new era of intelligent, adaptive, and high-impact knowledge practices.

Ekta Sachania has over 15 years of experience in learning and talent development disciplines, including knowledge management, content management, and learning & collaboration with expertise in content harvesting, practice enablement, metrics analysis, site management, collaboration activities, communications strategy and market trends analysis. Demonstrated success in managing multiple stakeholder expectations across time zones and exhibiting good project management skills, by successfully developing and deploying projects for large audiences.  Ability to adapt and work in emerging areas with fast-shifting priorities.  Connect with Ekta at LinkedIn...

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