A vision on the potential of AI in human expert input and labeling in military lessons learned
Year: 2026
Type: article
Abstract: Effective Knowledge Management (KM) is critical for ensuring that the right information reaches the right personnel at the right time. This process begins with the capture and storage of high quality information to avoid the common trap of ‘Garbage in = Garbage out.’ This paper examines the North Atlantic Treaty Organization’s (NATO) Lessons Learned Process, as a KM system that includes a change management process, emphasizing its role in enhancing interoperability and Command and Control within NATO military headquarters. The quality of the input and labeling of the observations and lessons captured, stored, and shared during the process contributes significantly to the likelihood that the Lessons Learned Process will result in tangible and lasting change. Current approaches to human-expert input and labeling do not ensure the necessary quality. This paper explores the potential of Artificial Intelligence (AI) techniques to augment or replace human involvement in these tasks. We present a forward-looking perspective on the integration of advanced AI and Machine Learning (ML) technologies to elevate the quality and effectiveness of the input and labeling of information during the Lessons Learned Process.
Source: OpenHSU
DOI: 10.24405/22132
Authors: de Boer, Maaike H. T., Eden, Andrew, Eaton, Jacqueline, Lange, Douglas S.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7124979645
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