Convenor
Convenor's affiliation
Simone Bevilacqua
University of Turin
Co-convenors
Mariano (Pitosh) Heyden, Sarosh Asad, Alberto Ferraris
Abstract
Artificial intelligence (AI) is profoundly transforming strategic leadership and decision-making across organizations. While prior research has predominantly examined the roles of CEOs and the top management team (TMT) in driving AI innovation, scholarly attention has remained siloed, focusing mainly on the upper echelons, with limited consideration to a multi-echelon perspective that encompasses cross-level interactions with middle managers. Positioned between top executives and frontline units, middle managers act as crucial bridging agents, translating strategic AI directives into daily operations.
This track invites contributions exploring how leadership configurations, in terms of demographic characteristics, values, prior work experience, and leadership styles, across hierarchical layers influence the adoption, integration, and strategic exploitation of diverse AI technologies, including generative AI, machine learning, robotics, and augmented reality. We welcome studies investigating both how top and middle managers jointly shape AI-enabled innovation and how AI reshapes strategic leadership roles, skills, competencies, and power dynamics. The topic directly speaks to the broader challenge of fostering creativity and resilience amid technological disruption, examining how leaders at different levels sustain adaptability and innovation in AI-intensive contexts. Conceptual, qualitative, quantitative, mixed-methods, and interdisciplinary submissions are encouraged to advance theoretical and practical understanding of strategic leadership in the AI era.
Description
At the frontier of technological advancement, artificial intelligence (AI) marks a profound rupture with wide-ranging implications for strategic decision-making and leadership across industries (Brown et al., 2024; Enholm et al., 2022). Rather than representing a single technology, AI encompasses a broad range of domains, including generative AI, machine learning, robotics, and augmented reality (Chowdhury et al., 2024; Volkmar et al., 2022). Given the disruptive capabilities of these technologies, they can generate sustained competitive advantage only when strategically embedded and leveraged within organizational processes by the top managers’ leadership (Doshi et al., 2025; Firk et al., 2022). Indeed, existing studies have predominantly centered on CEOs and TMTs, positioning them as critical actors and primary drivers of AI strategic innovation (Bevilacqua et al., 2025; Doshi et al., 2025; Jorzik et al., 2023).
However, while the strategic choices made at the upper echelons are undoubtedly important, they only tell part of the story of how AI reconfigures leadership in practice (Heyden et al., 2017; Heyden et al., 2018). Recent advances in the literature underscore the importance of adopting a multi-echelon perspective, which examines the relational interplay across organizational hierarchies, particularly between top and middle managers (Heyden et al., 2017; Georgakakis et al., 2022). Middle managers play a crucial role in translating AI-related strategic intent into operational reality, fostering experimentation, and shaping bottom-up learning processes (Heyden et al., 2018), yet, their role in AI transformation remains underexplored.
In line with the R&D Management Conference 2026 theme of “Creativity and resilience in an era of technological disruption,” the track emphasized how leaders at different levels foster creativity, adaptability, and strategic resilience in response to AI-driven change.
Therefore, this track aims to advance theoretical and empirical knowledge in three ways. First, it encourages studies that theorize and empirically examine leadership configurations across multiple hierarchical levels in relation to AI adoption, integration, and exploitation. Preference will be given to contributions that investigate the cross-level dynamics between upper and middle managers, although submissions exploring leadership configurations and relational dynamics between CEOs and TMT members, as well as within TMTs themselves, in the AI age, are also welcome. Second, this track seeks contributions that explore how AI reshapes strategic leadership roles, skills, and competencies at the top and middle management levels. Third, the track welcomes a wide range of methodological approaches, including conceptual papers, qualitative studies, quantitative analyses, mixed-methods designs, and experimental research.