Guest editorial: Artificial Intelligence (AI) for management decision-making processes. From measurement to strategy

Work on this special issue began in early 2023 when artificial intelligence (AI) was increasingly recognised as a transformative force but had yet to reach its current widespread adoption and influence. Our objective was to explore the promising yet relatively unexplored role of AI in managerial decision-making. This field is increasingly capturing the interest of management scholars, practitioners and organisations. The rapid diffusion of AI technologies, supported by enhanced computing capabilities, lower costs of technological tools and broader accessibility, creates novel opportunities and challenges in organisational decision-making (Dwivedi et al., 2021). Consequently, we aim to understand whether AI integration has begun reshaping managerial practices and the direction it may take in the future. AI'spotential to transform managerial decision-making processes highlights the need to expand the boundaries of management research. Integrated information systems have a significant influence on management practices and enhance organisational performance, requiring deeper integration with existing organisational processes and frameworks (Chapman and Kihn, 2009). Recent developments in machine learning and generative AI models further demonstrate AI'sability to augment human reasoning, creativity and foresight, rather than merely automate analytical processes (Farina et al., 2024; Patel and Lim, 2024). For instance, AI enables personalised financial planning and banking services by supporting both customers and managers with real-time decision assistance (Avelar and Jordão, 2025). AI algorithms also empower organisations to project demand accurately, enabling innovative marketing and distribution approaches (Talwar and Koury, 2017). Moreover, AI provides crucial data-driven insights within supply chains, healthcare, financial services and other sectors, significantly enhancing decision-making accuracy and efficiency (Loftus et al., 2020; Min, 2010; Secinaro et al., 2021). Despite these significant opportunities, management scholars often lag in interdisciplinary research that explores the implications of AI. While AI adoption has accelerated, a gap remains in understanding how organisations balance AI automation with managerial oversight and strategic decision-making. As suggested by Loureiro et al. (2021), there is a critical need for research addressing the impact of AI on internal and external organisational stakeholders, including ethical challenges and skills gaps. Recent studies also suggest that managerial readiness, trust and creativity play a decisive role in determining how AI is effectively integrated into organisational processes (Santoro et al., 2025). These insights underscore the need for management scholars to foster stronger interdisciplinary collaborations and integrate AI-related knowledge into their research and education. This special issue attracted numerous submissions, and 24 papers were accepted through rigorous review. These contributions include structured literature reviews offering insights into existing research and emerging AI topics in managerial contexts. Additional articles explore sector-specific AI applications across various industries, addressing innovation, ethical considerations and the critical skills required for future professionals. As AI continues to evolve, understanding its sector-specific implications remains essential, particularly as industries navigate the challenges of automation, regulatory compliance and workforce adaptation. In this editorial, we perform an in vivo analysis of the accepted articles to explore their thematic depth. We focus our analysis by addressing two research questions: RQ1. How does this special issue contribute to the research themes proposed in the original call for papers? RQ2. What emerging themes appear in the special issue that were not explicitly identified in the original call for papers? In subsequent sections, we present the main themes explicitly outlined in our call for papers. We then identify and discuss emergent themes identified from the selected articles. Finally, our discussion extends existing conceptual frameworks on AI adoption stages by suggesting new insights and proposing areas for future research.