The bottom line: Enterprise AI can systematically capture the implicit experience knowledge of departing employees and make it usable for organizations in the long term.
Over the next 15 years, roughly one-third of the working population will leave companies through retirement and take their implicit experience knowledge with them. Enterprise AI systems can capture this knowledge in a structured manner and make it available to successors to ensure process stability.
Demographic trends are presenting companies with a substantial challenge: one-third of the working population will retire over the next 15 years. This not only creates vacant positions but also results in the loss of decades of accumulated implicit knowledge – those treasures of experience buried in established patterns of action, customer relationships, and solution approaches that are often never documented.
Enterprise AI solutions address this knowledge transfer bottleneck through structured capture and contextualization. Instead of manual documentation, AI systems can engage in dialogue with experienced employees, reconstruct decision chains, and extract domain-specific patterns of behavior. This is particularly relevant for customer service, technology support, and process optimization, where individual expertise directly shapes business operations.
For CTOs, this means: the architecture of such AI systems must cover both phases – first the knowledge acquisition from subject matter experts, then the structured provision to colleagues and successors. This requires vertical integration between AI models (such as language models like Claude), enterprise data sources, and governance frameworks to ensure quality and compliance.
Source: itwelt.at · Published 19 June 2026
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