Thank you for tuning in to week 215 of the Lindahl Letter publication. A new edition arrives every Friday. This week the topic under consideration for the Lindahl Letter is, “Process capture and the future of knowledge management.”

The history of knowledge management has been shaped by repeated attempts to store, retrieve, and reuse organizational insight. So much institutional knowledge gets lost and discarded as organizations change and people shift roles or exit. People within organizations learn through the every day practice of getting things done. It’s only recently that systems are augmenting and sometimes automating those processes. Early systems focused on document repositories, and later platforms emphasized collaboration, tagging, and collective intelligence. We now find ourselves in a period where knowledge management converges with automated workflows and computational assistants that can observe, extract, and generalize decision patterns. We are seeing a major change in the ability to observe and capture processes. Systems are able to capture and catalog what is happening. This creates an interesting inflection point where the system may store the knowledge, but the users of that knowledge are dependent on the system. That does not mean the process is understood in terms of the big why question. Scholars have noted that the operational layer of organizational memory is often lost because it resides in informal practices rather than formal documentation. The shift toward embedded and automated capture offers a remedy to that problem.

The rise of agentic AI and workflow-integrated assistants alters the knowledge landscape by making it possible to synthesize procedural knowledge in real time. Instead of relying on teams to manually update wikis or define operating procedures, modern systems can extract key steps from repeated actions, identify dependencies, and flag anomalies that deviate from observed patterns. This transforms knowledge management from a static library into a dynamic computational environment. What exactly happens to this store of knowledge over time is something to consider going forward. Supervising the repository will require deep knowledge of the systems which are now being maintained systematically. Maintaining and refining it will be the difference between sustained institutional knowledge or temporary model advantages that drop with the next update. Recent studies on digital trace data argue that high fidelity observational streams can significantly improve the accuracy of organizational models. When this data flows into agents capable of modeling tasks, predicting outcomes, and recommending actions, the role of knowledge management shifts from storage to orchestration.

Process capture also introduces new opportunities for long-horizon learning systems. This is the part I’m really interested in understanding. The orchestration layer has to have some background learning and storage that runs periodically. When workflows are automatically translated into structured representations, organizations can run simulations, perform optimization, and enable higher levels of task autonomy. These capabilities begin to resemble continuous improvement environments that merge human judgment with machine-refined operational insight. Researchers have observed that structured process models can improve downstream automation and decision support, particularly in complex enterprise settings where procedures evolve rapidly. This suggests that the next phase of knowledge management will involve systems that not only store information but also refine it through computational analysis and real world feedback. It’s in that refinement that the magic might happen in terms of real knowledge management.

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Links I’m sharing this week!

https://www.computerworld.com/article/4094557/the-world-is-split-between-ai-sloppers-and-stoppers.html

This video is a super interesting look at a number we don’t normally question on a daily basis. The delivery style is a bit bombastic, but the fact check on the argument is interesting. You know I enjoy numbers and was really curious how this was calculated.

That video referenced this widely shared analysis from Michael W. Green on Substack.

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