Philosophy
These principles, or call them realizations, guide Hadron's vision. They describe the reality of handling institutional knowledge, and don't consider solutions — yet. "Actors" are either humans or machines as they do work, using knowledge.
Expert knowledge from scientific and professional sources provides the basis for getting started. The vast majority of institutional knowledge, however, is built through trial and error while conducting business. When those first steps yield success, the knowledge is validated and refined.
Knowledge management must be guided by using measured outcomes from decision making to validate precisely the knowledge that led to decisions.
Knowledge management should be modeled after how the human brain manages memory. Knowledge that has not been relearned, updated or validated cannot be trusted. What was true last year might no longer be this year.
The gathering and handling of knowledge must be made an integral part of day-to-day business. If it is delegated and regarded as a discrete task, it fails.
Because actors have the front-row experience of using knowledge to make decisions, they must not only consume it, but also evaluate what was made available to them, and provide feedback. Ideally they play an active role in curating the knowledge.
It is impossible to predict what knowledge an actor requires to make good decisions. Actors must be given access to more knowledge than initially planned. Their use of knowledge delivers insight into what scope is needed by actors and their tasks.
Small and focused context drives faster and more precise decision making. More information can be distracting, and may lead to inferior outcomes.
Humans and AI fundamentally learn the same way, and apply the same general logic to use knowledge for decision making. Knowledge management should be guided primarily by scope and capabilities.