Chris Juhasz is the senior director of archives at History Factory. A certified archivist and knowledge manager, Chris has more than two decades of experience making archival content accessible and strategically useful for clients.
Your knowledge ecosystem may have a big weakness.
AI-driven technologies are transforming how organizations capture, share, and apply knowledge resources, but they aren’t always getting the full picture. AI platforms used for knowledge management (KM) are often built on datasets consisting only of recent best practices, lessons learned, and subject matter expertise. Documents, project records, and databases are included in AI-enabled repositories based on their age and immediate applicability. Typical knowledge management practices prioritize fresh know-how over older wisdom and “know-why.”
Not surprisingly, those datasets tend to lack historical breadth, continuity, and contextual relationships.
Answers Without Meaning
AI knowledge platforms fed with data that has this kind of recency bias can produce quick answers, but often cannot explain them clearly. The responses they generate can lack substance, nuance, and contextual awareness. This is because they’re missing key elements that make up the big picture:
- Long-term developmental trends, such as patterns and cause-and-effect relationships
- Connections between pieces of information
- The how and why of significant events
- Social and cultural context that makes past experiences meaningful and understandable
Mending the Gap
You can address AI context gaps using documentary material, such as structured narratives and information assets like official communications, publications, reports, interviews, and high-level meeting records, etc. Knowledge managers usually consider these types of content nonoperational or “perishable,” when in fact they can have enormous future potential for broadening perspective and clarifying insight.
Introducing documentary material into AI-powered KM systems can significantly enhance their ability to uncover and explain ideas and extract intelligence that your organization can use for learning and innovation.
An Integrated Knowledge Ecosystem
Integrating archival strategies into your organization’s KM life cycle ensures that the performance of your system is grounded in organizational memory. Merging history and archival practices into existing content acquisition and management processes can deepen and strengthen datasets. That means AI platforms can interpret and surface knowledge more accurately and create richer, more comprehensive content for learning and development, innovation, decision making, problem solving, and more.
Keep an eye open for our next installment, where we’ll discuss three practical ways organizations can combine archival and knowledge management capabilities to improve the continuity and interpretation of AI data.