Conclusion

AI-native organizations require more than data persistence, repositories, knowledge graphs, dashboards, digital twins, or AI-generated summaries. They require governed organizational knowledge that remains legitimate, interpretable, projectable, revisable, and actionable across time. This paper develops the Organizational Knowledge Kernel as a theoretical construct for that problem.

The construct explains how organizational identity and intent anchor knowledge; how data and information become organizational knowledge through governed admission; how memory and current state remain distinct; how projections support situated understanding without fragmenting source knowledge; how authority and provenance constrain human-AI operation; and how decisions, execution, evidence, learning, and adaptation remain meaningfully connected. The theory is offered as a foundation for future empirical and design-oriented research, not as a claim of completed validation.