Knowledge Capture: Beyond the Harvest

Rethinking Knowledge Capture in the Age of Constant Change

From ChatGPT via a prompt by the author.

With the growing expectation that AI will do everything for us, knowledge capture—new knowledge, knowledge learned in the moment by people—is getting shortchanged.

Learning, which is what knowledge capture is really about, is too often relegated to saving a document or uploading a file, or, with AI, uploading meeting notes or a recording. But that’s just recordkeeping. Knowledge—especially the useful kind—doesn’t live in SharePoint or Google Drive. It lives in people, in patterns, in relationships, and in the choices made under pressure. The real challenge lies in distilling that experience into something that can be reused and trusted by others—and, yes, as a source for AI.

Knowledge doesn’t live in SharePoint or Google Drive. It lives in people, in patterns, in relationships, and in the choices made under pressure

Knowledge isn’t just information—it’s contextual and often invisible. It shows up in how a senior engineer knows which part will fail before it does, or how a program manager anticipates what a stakeholder won’t say in a meeting. Tacit knowledge—what people know but haven’t written down—is the motherlode. Turning it into something usable is the real job.

Start With People

Technology doesn’t make people share. The notion that KM can be “solved” with a better platform misses the point. KM is social. It’s messy. It involves humans deciding to trust other humans with what they know—and organizations making space for that exchange.

Here’s what works:

• Interviews and Debriefs. If someone is walking out the door or wrapping up a big initiative, sit them down and ask what they wish they had known when they started. Capture methods, mental shortcuts, and workarounds. At some consulting firms, “Project Catalysts” do this as part of their playbook—structured debriefs aimed at extracting lessons and innovations worth retaining.

• Narratives and Stories. People understand experiences better than rules. A five-minute video about a successful product launch carries culture, nuance, and strategy in ways a checklist never will. Stories bind insight to emotion, which makes them stick.

• Lessons Learned and After-Action Reviews. These should be habits, not rituals. Done right, they generate raw material for continuous improvement. But they only work if they happen before, during, and after projects—not just at the end when everyone’s already disengaged.

• Communities of Practice. CoPs don’t just share knowledge—they nurture it. When people come together around a domain, whether it’s cybersecurity or onboarding, they invent shortcuts, share new angles, and build up a layer of collective intelligence. But they need support, not just a Slack channel. Stewards can help shape what emerges into usable assets.

Codify When It Matters

Turning experience into explicit knowledge takes more than writing things down. Codification only works when what’s written is understandable, contextual, and actually useful.

• Document the How, Not Just the What. Process documentation and good practices should describe decision points, not just steps. Good documentation gives people a way to think, not just something to follow. This includes SOPs, templates, methods, and branching logic informed by experience.

• Treat Content as a Strategic Asset. If knowledge assets are dumped into a CMS and left to rot, they lose value. Content needs curation. Relevance. Ownership. A knowledge base isn’t a landfill—it’s a library.

• Context is Non-Negotiable. Without context, content is just noise. Every captured item should include the creator's name, the date, why it matters, and instructions on how to use it. Good metadata, consistent tagging, and a meaningful taxonomy are prerequisites, not afterthoughts.

Tech Is a Tool, Not the Solution

You can’t buy your way into a knowledge-sharing culture. But used well, technology can reinforce it.

• AI and Knowledge Graphs. Tools like large language models and graph databases are starting to make connections across silos. They can suggest related content, highlight gaps, and even infer undocumented relationships—if the underlying knowledge base is solid.

• Search That Works. If people can’t find it, it doesn’t exist. Search has to be smart, fast, and tuned to user needs. Auto-tagging, relevance scoring, and feedback loops help. So does cleaning out outdated, low-value material.

• Collaborative Platforms. Social tools and collaborative workspaces create ambient opportunities to share. When people talk about what they’re doing as they do it, knowledge flows naturally. It’s not just about storing—it’s about engaging.

• Lifecycle-Driven CMS. Content management systems should support the entire content lifecycle, encompassing creation, approval, expiration, and retirement. Without this, knowledge assets age poorly, and that erodes trust.

Make It a Living System

KM is never “done.” Knowledge capture is an ongoing discipline, not a one-time effort. Assets must be reviewed, refreshed, and revalidated. New experiences must challenge captured knowledge. That requires governance, leadership support, and accountability.

The goal isn’t just documentation. It’s organizational sensemaking. Captured knowledge should enable teams to make better decisions, identify risks earlier, and innovate more confidently. It should reduce rework, smooth onboarding, and improve customer outcomes. It should be an integral part of how the organization operates, encompassing how people are evaluated and expectations are established, rather than a separate project.

If your organization still treats knowledge capture as clerical work or an IT problem, it’s time to reframe. Capture is strategic. And the most powerful form of capture might just be this: discussing what we know, while we still know it, and, yes, how to bring AI into that conversation.

What’s your biggest challenge when it comes to capturing knowledge? Let me know in the comments—or reach out. The dialogue itself is part of the answer.