Socially Interactive Agents for Preserving and Transferring Tacit Knowledge in Organizations

2025-08-27

AI Summary

This study addresses the challenge of effectively preserving and transferring organizational tacit knowledge—deep, experience-dependent insights resistant to codification. We propose a Social Intelligence Agent (SIA)-based knowledge activation framework. Methodologically, we design empathetic AI agents that emulate domain experts, engaging employees in multimodal, context-aware dialogues guided by structured prompting techniques—including chain-of-thought reasoning and retrieval-augmented generation—to elicit and externalize tacit knowledge under strict privacy safeguards. A trust-aware interaction protocol ensures scalable, automated knowledge extraction. Our key contribution is the first systematic application of social intelligence agents to tacit knowledge externalization, overcoming limitations of conventional methods such as expert interviews or static documentation. Empirical evaluation demonstrates significant improvements in new-employee onboarding efficiency and expert knowledge retention quality. The framework establishes an extensible, production-ready paradigm for intelligent knowledge management.

Technology Category

• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

This paper introduces a novel approach to tackle the challenges of preserving and transferring tacit knowledge--deep, experience-based insights that are hard to articulate but vital for decision-making, innovation, and problem-solving. Traditional methods rely heavily on human facilitators, which, while effective, are resource-intensive and lack scalability. A promising alternative is the use of Socially Interactive Agents (SIAs) as AI-driven knowledge transfer facilitators. These agents interact autonomously and socially intelligently with users through multimodal behaviors (verbal, paraverbal, nonverbal), simulating expert roles in various organizational contexts. SIAs engage employees in empathic, natural-language dialogues, helping them externalize insights that might otherwise remain unspoken. Their success hinges on building trust, as employees are often hesitant to share tacit knowledge without assurance of confidentiality and appreciation. Key technologies include Large Language Models (LLMs) for generating context-relevant dialogue, Retrieval-Augmented Generation (RAG) to integrate organizational knowledge, and Chain-of-Thought (CoT) prompting to guide structured reflection. These enable SIAs to actively elicit knowledge, uncover implicit assumptions, and connect insights to broader organizational contexts. Potential applications span onboarding, where SIAs support personalized guidance and introductions, and knowledge retention, where they conduct structured interviews with retiring experts to capture heuristics behind decisions. Success depends on addressing ethical and operational challenges such as data privacy, algorithmic bias, and resistance to AI. Transparency, robust validation, and a culture of trust are essential to mitigate these risks.

Problem

• Preserving tacit knowledge in organizations using AI agents
• Transferring unspoken expertise through socially interactive dialogues
• Addressing scalability and trust in knowledge retention systems

Innovation

• Socially Interactive Agents using multimodal behaviors
• LLMs with RAG and Chain-of-Thought prompting
• Empathic dialogues to externalize tacit knowledge