Oct 2026· Advances in computational intelligence and robotics book series· 33 references
Knowledge Management and Sharing
Abstract
This chapter examines how algorithmic management reshapes organizational resource management while weakening the human knowledge structures that support long-term adaptation. It focuses especially on the tacit knowledge risks that emerge when organizations rely heavily on data-driven managerial systems for coordination, evaluation, allocation, and control. Drawing on literature on tacit knowledge, institutional memory, organizational learning, transactive memory systems, hybrid governance, and human-centric AI, the chapter explains why institutional memory becomes vulnerable under datafied management. It argues that organizations face a dual challenge: they must benefit from AI-enabled efficiency without allowing managerial automation to erode tacit knowledge, mentorship, contextual judgment, and knowledge-sharing routines. To address this challenge, the chapter introduces a Hybrid Knowledge Governance framework and offers practical recommendations for more human-centered AI adoption.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
