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
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026