The Critical Problem of Organizational Knowledge Loss
Advanced economies face a silent knowledge transfer crisis. In Spain, the proportion of the population aged 65 and over reached 20.40% in December 2024, with projections indicating a decline in the ratio from 2.6 working-age individuals per retiree to just 1.6 by 2050. This demographic shift represents not only a fiscal challenge—pension spending will rise from 12.9% to 16.1% of GDP by 2050—but a massive hemorrhage of tacit knowledge accumulated over decades of professional experience.
Tacit knowledge, a concept grounded in Nonaka and Takeuchi’s SECI model, represents the implicit, contextual, and difficult-to-articulate know-how that senior professionals develop through years of practice. This knowledge—encompassing diagnostic judgments, problem-solving heuristics, and systemic understanding of complex processes—remains largely undocumented and unstructured, residing exclusively in the memory of experts nearing retirement.
The economic magnitude of the problem is considerable. McKinsey reports that in industrial sectors, a mid-sized company could avoid over $300 million in costs by addressing knowledge gaps generated by retirements.
AI-Based Conversational Elicitation Methodology
Traditional tacit knowledge capture through unstructured interviews presents inherent limitations: high time intensity, dependence on interviewer skill, expert memory bias, and the absence of automatic structuring mechanisms. Recent research on automated elicitation using LLMs demonstrates that AI-based conversational systems can overcome these limitations.
The methodology is built on three technical pillars:
1. Structured and Contextually Adaptive Prompt Design
Conversational AI systems employ dynamic decision trees that adapt questions based on the expert’s previous responses, exploring knowledge domains with systematic coverage while maintaining contextual flexibility.
2. Time-Bounded Sessions with High Cognitive Density
Sessions of 30–45 minutes optimize the cognitive fatigue curve versus information extraction. Platforms such as Sagelix implement 30-minute conversation protocols designed to keep the expert in a flow state, minimizing cognitive overload while maximizing the articulation of implicit knowledge.
3. Automated Structuring and Verified Anonymization
Post-conversation processing transforms transcripts into semantically structured datasets through NLP pipelines:
- Procedural knowledge extraction: Identification of decision sequences and resulting actions
- Knowledge graph construction: Representation of relationships between concepts, cases, and solutions
- Anonymization with semantic preservation: Removal of personal identifiers while maintaining knowledge integrity
- Coherence verification: Cross-validation through logical contradiction detection
Digital transformation through generative AI enables scaling this process to volumes unfeasible with traditional methods.
Dataset Quality Metrics: Comparative Analysis
Information Density and Contextual Depth
Datasets generated through conversational AI demonstrate higher semantic density per token compared to traditional technical documentation. The key lies in the fact that AI-guided dialogue systematically explores knowledge dimensions that would remain implicit in unstructured interviews.
Reproducibility and Epistemological Traceability
Unlike manual tacit knowledge codification, conversational systems maintain complete inference chains: from the original question to the expert’s response.
Generalization versus Domain Specificity
Expert datasets exhibit a superior balance between generalizable knowledge and contextual specificities through multi-level labeling:
- Technical domain (medical diagnosis, process engineering)
- Abstraction level (general principles vs. specific cases)
- Degree of consensus (standard practices vs. proprietary know-how)
- Temporal applicability (timeless knowledge vs. contextually dated)
This granularity is critical for training multi-agent systems that require differentiated knowledge based on operational context.
Future Implications: Toward Structured Knowledge Markets
Post-Retirement Expertise Monetization
Senior professionals can convert their knowledge into marketable digital assets. Sagelix implements marketplaces where organizations acquire expert datasets for AI training or decision support system development.
Democratizing Access to Scarce Expertise
Structuring tacit knowledge into verified datasets enables organizations without access to senior experts to incorporate know-how accumulated over decades, reducing information asymmetries.
Integration with Generative AI Systems
Tacit knowledge datasets represent high-quality fuel for fine-tuning LLMs in specialized domains. Integration with enterprise AI architectures will enable systems that internalize mental models and expert decision-making heuristics.
Conclusions
Tacit knowledge capture and structuring through conversational AI represents a paradigm shift in organizational knowledge management. The combination of demographic crisis, technological maturity of LLMs, and the economic imperative to preserve expertise creates a historic window of opportunity.
The remaining technical challenge lies in developing quality standards, ethical anonymization frameworks, and sustainable business models that incentivize expert participation. The transition from an economy where tacit knowledge is lost with every retirement to one where it becomes a reusable digital asset represents one of the most significant transformations in intellectual capital management.
