How Can AI Support Knowledge Management

Why It Matters

AI‑driven knowledge management mitigates the looming talent‑exit risk while delivering tangible productivity and decision‑support benefits, making it a strategic priority for digitally transforming enterprises.

Key Takeaways

• Only 8% capture retiring employees' knowledge consistently.
• AI automates interview summarization and structured document creation.
• Governance, taxonomy prevent outdated or inaccurate AI outputs.
• Novartis shows AI‑KM reduces duplication, speeds decisions.
• Upskilling and change management drive AI‑KM adoption success.

Pulse Analysis

The "Great Retirement" wave threatens to strip organizations of critical institutional knowledge, a problem amplified by rapid digital transformation. AI offers a scalable remedy by converting expert interviews into concise summaries, structuring raw transcripts into practice documents, and building searchable knowledge bases that surface insights across massive content pools. These capabilities compress the knowledge‑capture cycle, preserving both tacit insights and explicit data before they disappear with departing staff.

Yet technology alone cannot deliver reliable outcomes. Robust governance frameworks—clear accountability for content validation, lifecycle controls, and dedicated KM liaisons—are essential to prevent AI from propagating stale or erroneous information. A well‑designed taxonomy further enhances AI comprehension, enabling precise topic relationships and improving retrieval relevance. APQC’s research highlights that governance and content‑management challenges, not cultural resistance, are the top barriers to AI adoption, underscoring the need for disciplined oversight.

When integrated with mature KM practices, AI generates measurable business value. Novartis’ experience shows that centralizing trusted knowledge before layering generative‑AI reduces duplicate effort, accelerates access to reliable information, and strengthens onboarding and decision support. Realizing these gains requires parallel investments in change management, employee upskilling, and cross‑functional collaboration, along with metrics to track adoption, efficiency, and quality. Organizations that align AI‑KM initiatives with clear business objectives and governance will turn knowledge preservation into a competitive advantage.