AI Supports knowledge management by enabling organizations to capture, organize, and reuse knowledge more effectively. In practice, AI supports knowledge management through faster knowledge discovery, improved decision making, and scalable knowledge transfer that augments—rather than replaces—human expertise.
As organizations navigate the “Great Retirement” and rapid digital transformation, the question is no longer whether to use AI in KM, but how to implement it responsibly and effectively.
AI as an Enabler of Knowledge Capture and Transfer
APQC research on navigating great retirement with KM and AI shows that many organizations are at risk of losing critical institutional knowledge as experienced employees retire. Yet only 8% of organizations consistently capture knowledge from departing retirees, while 16% do not attempt to capture it at all. AI offers new ways to accelerate and enhance knowledge transfer efforts.
Organizations are already using AI to:
- Record and summarize expert interviews
- Convert transcripts into structured practice documents
- Train generative AI tools to retrieve knowledge from subject matter experts
- Create searchable repositories that can summarize and surface insights from large volumes of content
These approaches reduce the cycle time of knowledge capture and make it easier to preserve both tacit and explicit knowledge before it walks out the door.
Governance and Content Management Are Foundational
While AI introduces powerful capabilities, its effectiveness depends on strong governance and content management practices. Without structured oversight, AI systems can produce redundant, outdated, or inaccurate results.
APQC research also shows that the top barriers to ai adoption are rooted in governance and content management—including concerns about accuracy, data privacy, and compliance—rather than cultural resistance alone.
Effective governance includes:
- Clear accountability for content validation
- Lifecycle management to prevent outdated content from training AI systems
- Defined roles for subject matter experts and KM liaisons
- Oversight bodies such as steering committees or communities of practice
Taxonomy also plays a critical role. A well-designed taxonomy enables AI systems to understand relationships between topics, domains, and business functions—improving search, retrieval, and relevance.
Driving Business Impact Through AI-Enabled KM
Leading organizations demonstrate that AI can deliver measurable business value when integrated into a broader KM strategy. At Novartis, for example, the KM team centralized and curated trusted knowledge before layering generative AI capabilities on top.
Strong governance, collaboration with legal and compliance, and alignment with business priorities helped ensure reliability and adoption.
AI-supported KM can contribute to:
- Reduced duplicative work
- Faster access to trusted knowledge
- Improved onboarding and knowledge continuity
- Enhanced decision support
However, successful implementation requires more than technology. Organizations must invest in change management, employee upskilling, and cross-functional collaboration to ensure adoption.
Preparing the Workforce for AI-Driven KM
APQC research indicates that most organizations are planning to upskill employees and use change management strategies to support AI adoption. Strong change practices—such as leadership buy-in, structured communication, training, and measurement—are essential for sustaining momentum.
Measuring adoption, efficiency, quality, and engagement is also critical for validating the impact of AI-enabled knowledge transfer and securing continued leadership support.
AI can support knowledge management at scale, but it is most effective when paired with mature KM practices, strong governance, and a clear connection to business value.
Check out APQC’s extensive resources on knowledge management: