AI Captures Tacit Knowledge in Agentic Operations

Agentic operations, driven by process intelligence, offer a new approach to preventing valuable tacit knowledge from leaving organizations when employees depart. This method moves beyond static automation, which falters when tasks deviate from predefined templates. Instead, agentic operations map enterprise workflows as they actually function, not just as manuals describe them, creating a living, machine-readable knowledge graph.

The core challenge addressed is capturing human judgment, particularly in moments where work deviates from standard procedures, such as overrides, escalations, and exceptions. The process involves recovering the reasoning behind these expert decisions: what an expert noticed, what factors they weighed, and what contextual knowledge about customers, suppliers, or the time of year influenced their choices. This recovered reasoning is then tied to the specific context in which it was applied and tested against historical cases to validate its effectiveness.

Once validated, this reasoned pattern becomes part of the knowledge graph, enabling an agent to interpret situations and reason through them. The system can then determine when to make decisions, effectively encoding experience into a machine-readable format. This contrasts with traditional training and documentation, which often fail to capture the nuanced knowledge residing in individuals.