The emergence of Knowledge 5.0 represents a fundamental shift in how organizations combine Artificial Intelligence (AI) and human expertise in Strategic Decision-Making. Rooted in Industry 5.0, Knowledge 5.0 emphasizes human-centric collaboration, collective intelligence, and the co-creation of strategic knowledge through synergistic human–AI partnerships. This chapter examines five major trends shaping AI-enhanced Strategic Decision-Making within the Knowledge 5.0 paradigm: (1) the evolution towards collective human–machine intelligence, (2) human-centric AI governance for sustainable strategy, (3) explainable AI and transparent knowledge co-creation, (4) real-time adaptive decision-making through distributed cognition, and (5) the democratization of strategic expertise through AI-augmented knowledge systems. Drawing on research from Industry 5.0, knowledge management (KM), and strategic management, the chapter proposes an integrative Knowledge 5.0 Strategic Decision-Making Framework. The analysis demonstrates that effective strategy in the Knowledge 5.0 era depends not on replacing humans with technology, but on orchestrating human judgement, organizational knowledge, and AI capabilities to achieve resilient, sustainable, and value-driven outcomes.
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
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.