Call for Papers

Call for short papers (pdf)

In this sub-theme, we invite scholars to explore how artificial intelligence (AI) systems –including both predictive models and generative AI (Gen AI) – and, more broadly, algorithmic technologies transform decision-making processes in organizations. As AI becomes increasingly embedded in organizational structures and processes, we face complex transformations in how decisions are made, by whom, and with what consequences. This has led scholars to call for the urgent development of theory, methods, and empirical studies that enable a better understanding of how AI reshapes “work and organizational realities” (Faraj et al., 2018, p. 67) through decision-making processes.
 
Organizational decision-making research has traditionally conceptualized management as a discrete and human-centered activity, focusing primarily on boundedly rational individuals navigating episodic decisions (Simon, 1997; March, 1962; Feldman & March, 1981). This body of work emphasizes the cognitive limits, social positioning, and political motivations of human actors, particularly managers and professionals (Turner & Makhija, 2012). While these frameworks remain influential, AI's characteristics are increasingly challenging some of the main assumptions. As organizations increasingly delegate authority to algorithms – from medical diagnostics to financial trading – the normative, epistemic, and material foundations of decision making shift in ways that are as yet poorly understood (von Krogh, 2018; Shrestha et al., 2018). For example, how can we capture and theorize the changes occurring to decision-making practices and organizations when AI systems operate autonomously, produce outputs that are difficult to interpret, and act with a speed that far exceeds human deliberation?
 
More recent research on AI in organizations has highlighted both the positive potential of algorithmic tools to facilitate value creation by automating structured work (Davenport, 2018) and reshaping organizational culture (Fountaine et al., 2019), as well as their “dark side”, including how they enable management control (Kellogg et al., 2020), establish inflexible rules (Lindebaum et al., 2019), and perpetuate power asymmetries (Curchod et al., 2020; Zuboff, 2022). Studies also reveal how AI systems create new forms of knowledge brokerage, where individuals with algorithmic expertise gain disproportionate influence in decision-making processes previously dominated by domain experts (Waardenburg et al., 2022). Additionally, generative AI tools are now co-producing text, images, and ideas that shape not just decisions but also the framing of problems, the narratives organizations tell, and the sensemaking processes of professionals (Mollick, 2024).
 
Recent research has also suggested that AI’s impact on organizational decision making extends beyond traditional dichotomies (e.g., human-machine, augmentation-augmentation). Various studies indicate that the relationship between humans and AI is better understood as a collective agency and interactive process that spans organizational contexts, actors, and temporal dimensions (Hillebrand et al., 2025; Murray et al., 2021; Raisch & Krakowski, 2021). For example, emerging empirical work reveals how AI applications in management simultaneously function across multiple managerial tasks, creating new interdependencies among actors and tasks (Anthony, 2021; Anthony et al., 2023; Pakarinen & Huising, 2023). Such interdependencies suggest that managing with AI requires embracing a more systemic view that considers various organizational actors’ interactions with AI over time and in context (Bailey & Barley 2001).
 
To address these core themes, we require a deeper analysis of how algorithms evolve and change as they move along their life cycle and across different practices (D’Adderio & Pollock, 2020), institutions (Christin, 2020) and organizations (Glaser et al., 2024). To do so we need, in turn, to relinquish the dominant conceptualizations of algorithms as self-contained computational tools with fixed properties and effects to capture the complex, relational, and dynamic nature of algorithms in organizational settings (Glaser et al., 2021).
 
In this sub-theme, we invite contributions that theorize and empirically investigate how AI’s distinctive properties reshape decision making within and across organizations. This includes understanding how human skill may be preserved in the age of intelligent machines, as well as whether and how organizations can maintain essential human capabilities while integrating AI into decision processes (Beane, 2024).Both theoretical and empirical work is welcome, particularly qualitative and interpretive work that foregrounds the temporal, relational, and material dynamics of human-AI interaction. Studies from diverse domains – including healthcare, law, finance, logistics, and public administration –are welcome, especially those that shed light on how professionals adapt, resist, or transform their work in response to algorithmic decision systems.
 
Key questions include:

• How are decisions about AI (including data sourcing, algorithm selection, and model development) made in organizations, and how do these decisions shape subsequent decision making with AI?

• How is decision making authority redistributed between human actors (managers, professionals, data scientists) and AI systems across different organizational contexts?

• How do organizational actors make sense of and adapt to AI-powered predictions and recommendations in their decision-making practices?

• How do generative AI tools influence meaning-making, creativity, and narrative construction in decision processes?

• How does the use of GenAI challenge existing boundaries between routine decision-making and creative, interpretive work?

• How does the introduction of AI into decision making processes reconfigure professional expertise, identities, and occupational boundaries?

• How do AI systems influence the socio-political dynamics of organizational decision making, including power relationships, accountability structures, and ethical considerations?

• What new ethical, epistemic, or legitimacy challenges arise from decisions informed or shaped by GenAI outputs?

• What implications does the redistribution of decision making have for organizational learning, innovation, and strategic adaptation?

• How do contextual factors (sector, regulatory environment, organizational culture) moderate the relationships between decisions about AI, with AI, and their implications?

• How do AI systems in decision making evolve and adapt over time as they encounter different organizational contexts, tasks, and human counterparts?

• What methodological approaches are most appropriate for studying the complexities of human-AI decision making in organizational settings?

• How can organizations design decision making processes that effectively integrate human judgment and AI capabilities while mitigating potential biases and ethical concerns?

• How do professionals navigate and manage tensions between competing imperatives (such as speed vs. accuracy, efficiency vs. thoroughness) in AI-mediated decision making?

• What distinctive sets of practices emerge as professionals integrate AI into high-stakes decision making processes, and how do these practices transform over time?

• How does AI’s distinctive materiality (visual outputs, predictive capabilities, standardized formats) shape human-AI collaboration in decision making?

• How do professionals make use of AI’s affordances to rapidly act on information while simultaneously maintaining critical oversight of algorithmic outputs?

• How are boundaries between automation and augmentation negotiated and reconfigured in practice as AI becomes embedded in professional work?

• How does the introduction of AI into decision making processes reconfigure professional expertise, identities, and the development of skills?

• How do distributed teams across different locations and specializations coordinate decision making through AI systems?

• What new forms of collaboration emerge between previously separate professional groups (e.g., data scientists and domain specialists) in AI-mediated decision making?

• How do organizational actors balance the benefits of AI-driven standardization with the need for contextual judgment and professional discretion?

• How do AI systems transform collective problem-solving and the temporal dynamics of urgent decision making?

• What methodological approaches are most appropriate for studying the distributed, sociomaterial dynamics of human-AI decision making in organizational settings?

 
The insights generated from this sub-theme will not only advance theoretical understanding of AI in organizational decision-making but also provide practical guidance for organizations navigating the complex integration of AI into their decision processes.
 

References

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• Anthony, C., Bechky, B.A., & Fayard, A.L. (2023): “‘Collaborating’ with AI: Taking a System View to Explore the Future of Work.” Organization Science, 34 (5), 1672–1694.
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• Christin, A. (2020): Metrics at Work: Journalism and the Contested Meaning of Algorithms. Princeton, NJ: Princeton University Press.
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