Smart Expert Debriefing - Potentials and Challenges of AI for Acquiring und Documenting Implicit Knowledge of Leaving Experts
Keywords:
Implicit Knowledge, Knowledge Loss, Leaving Expert Debriefing, Knowledge Documentation, Traditional and Generative AIAbstract
Well over 50% of the knowledge critical to success in companies is only available in implicit form. In this respect, there are many risks of impending knowledge loss. For example, a large number of baby boomers will be retiring in the next few years. Initial studies show that this is associated with enormous, measurable monetary damage. This article examines how hybrid, scalable, AI-based solutions must be designed to assist in the acquisition and documentation of implicit knowledge.
The article explores three research questions: (1) Which traditional and generative AI approaches can automate sub-processes in an expert debriefing process based on a reference knowledge map and assist a process facilitator? (2) How should appreciative hybrid and blended moderation, knowledge acquisition and documentation be designed? (3) What other challenges exist and how can these be solved (e.g. acceptance, trust, AI literacy, data protection)?
Methodologically, a literature review is carried out, the author's more than 30 years of practical expert debriefing experience are taken into account, especially in the public sector, agile prototype development is presented and the intensive evaluation is designed.
The article shows that traditional and generative AI substantially reduces the manual effort involved in expert debriefing and thus promotes widespread use. Nevertheless, attention must be paid to a measured and targeted application of generative AI, particularly with regard to the special requirements of baby boomers and different expert types.
Future research lies in the continuous updating of generative AI approaches, multiplication in other application scenarios (e.g. onboarding) and evidence-based approaches.
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