KTBot: Preserving Tribal Knowledge in Industrial Operations Through Conversational AI

Abstract

Critical industrial facilities rely on tacit knowledge accumulated by experienced operators over decades of hands-on work. This knowledge, often called tribal knowledge, encompasses diagnostic heuristics, exception-handling procedures, and contextual judgment that is rarely documented in standard operating procedures. As experienced operators retire, this knowledge is permanently lost, degrading operational reliability and safety. We present KTBot, a conversational AI system designed to capture, structure, and make queryable the tribal knowledge of industrial operators. KTBot uses a structured interview protocol to elicit knowledge through scenario-based conversations, stores the resulting knowledge in a graph-structured representation that preserves causal relationships and contextual conditions, and makes it accessible through a natural language query interface. We report on a deployment at a thermal power plant where KTBot captured knowledge from 12 operators with a combined 340 years of experience, covering 847 distinct operational scenarios. New engineers using KTBot resolved unfamiliar operational situations 2.4x faster than those relying on documentation alone, with a 67% reduction in escalations to senior operators. The paper discusses the design principles for knowledge elicitation from domain experts who are not technology-fluent, and the organizational dynamics of deploying knowledge preservation systems in unionized industrial environments.

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