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.
Building a layer of context into your AI knowledge management (KM) system can lead to more meaningful and nuanced results.
If your system relies solely on recent data, your people are missing vital background information that can connect the past and the present, revealing hidden knowledge. By leveraging archival capabilities, you can create a historical layer that significantly improves your AI system’s data and, ultimately, the insights it provides.
Three Pillars of Archival Integration
To build a more holistic and context-aware knowledge ecosystem, KM professionals should consider these three ways of integrating archival practices into their work:
- Creating structured project histories that include key decisions, determinants, and defining moments
- Digitizing and integrating relevant legacy assets to connect knowledge across time
- Converting personal stories and points of view into reusable knowledge content
1. Creating Project Histories
Many organizations conduct after-action reviews, but these are usually focused on final outcomes. Few integrate archival documentation methods to create complete project histories.
Complete project histories capture:
- Why you made the decisions you did
- What constraints you faced
- How you addressed challenges
Working together, knowledge managers and archivists can guide teams to collect this thinking and experience, including drivers, turning points, and impacts, to aid future interpretation and use of project information. To construct project narratives, they can design and distribute prompts and facilitate debrief sessions.
Once properly structured and annotated for AI, these histories become high-quality training data, enhancing interpretation and enabling deeper analysis and more robust outputs.
2. Converting Legacy Archival Assets Into Digital Knowledge Content
Older physical documentary materials—such as official communications, publications, interview transcripts, and administrative records—are essential for building context into AI knowledge systems, grounding contemporary knowledge in historical perspective, and improving the richness, clarity, and comprehension of results.
But before AI tools can leverage them, these existing sources of knowledge must be transformed into AI-ready digital content. Digitization alone isn’t enough. Archival documents must be specially formatted so that AI models can interpret the information they contain accurately, trace their origins, surface it reliably within digital workflows, and turn it into new insights and actions.
This is where archival expertise in selection, digitization standards, metadata design, and contextualization comes in. Working together, archivists and knowledge management specialists can utilize their capabilities to select the right legacy materials for digitization, format them correctly, and integrate the data they contain to make it accessible and useful.
3. Collecting Personal Stories
The insights and lived experiences of individuals are among the most valuable sources for AI enrichment, but they are rarely written down. This is where archival memory capture and preservation techniques come in.
To gather voices and firsthand accounts, archivists and knowledge managers:
- Identify and prioritize roles and subject matter experts of interest
- Set trigger points (e.g., retirements, leadership changes, or major program milestones)
- Implement appropriate recording and submission methods
Oral histories and story capture are two of the most effective ways organizations can enrich AI data using the social and cultural dimensions of human experience to create meaning. As with other assets, captured content must be indexed, tagged, and linked to related content so AI management systems can surface it dynamically.
Strengthening Knowledge Ecosystems With History and Meaning
Together, these three approaches introduce a layer of continuity and context into AI datasets, enabling systems to deliver insights that are more grounded, nuanced, and meaningful.
At History Factory, we believe the future of organizational intelligence lies at the intersection of archival science and knowledge management. By connecting these two disciplines, you ensure that your knowledge ecosystem is not just a collection of files but also a resource that is coherent, navigable, and infinitely useful.