Mayur Joshi, PhD

Artificial Intelligence as an Organizing Capability Arising from Human-Algorithm Relations

Stelmaszak, M., Joshi, M., & Constantiou, I. (Equal authorship; authors listed in reverse alphabetical order.)

In this article, we move beyond the prevailing view of artificial intelligence (AI) as an independent entity within organizations, which, we argue, risks obscuring potential explanations of the effects of AI on organizing. Drawing on posthumanism, we propose an ontological shift in conceptualizing AI. We theorize that, instead of residing within algorithmic actors, AI arises from the relations among human and algorithmic actors as an organizing capability. This capability is characterized by connectivity, codependence, and emergence as core properties, and contributes to organizational analysing, learning, and acting in pursuit of organizational goals. The shift from the entity view to the organizing capability view of AI has significant implications for understanding its organizational effects and opens new avenues for research in human-algorithm collaboration, algorithmic management, and organizational intelligence, while counterbalancing tendencies to treat AI as autonomous agents.

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Data-Based Craft: How Data Scientists Craft Their Data, Models, and Products

Hopf, K., Joshi, M., Shollo, A., & Stelmaszak, M. (Equal authorship; authors listed alphabetically.)

In this study, we examine the work of data scientists, members of an emerging technical occupation, through the lens of craft. Drawing on 65 in‐depth interviews with data scientists, we show that their work cannot be adequately explained by the human–machine configurations characterized in the existing literature on craft in technical occupations, which primarily focuses on crafting products using ready‐to‐use tools and ready‐to‐be‐processed materials. Instead, we find that data scientists craft not only their products, but also their tools and materials, often in an iterative and non‐linear fashion. This distinct approach entails a unique human–machine‐data configuration that we refer to as data‐based craft, which stems from the unique nature of digital data and learning algorithms that data scientists simultaneously craft and use. This study advances our understanding of craft in the digital age by highlighting the need to reconceptualize human–machine relationships in data‐intensive occupations.

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Research Handbook on Artificial Intelligence and Decision-Making in Organizations

Constantiou, I., Joshi, M. P., & Stelmaszak, M. (Eds.)

Featuring state-of-the-art research from leading academics in technology and organization studies, this research handbook provides a comprehensive overview of how AI becomes embedded in decisionmaking in organizations, from the initial considerations when implementing AI to the use ofsuch solutions in strategic decision making. We advance a framework of decision redistribution defined as the migration of human decision making (with or without an existing technology) across the three facets of AI and organizational decision making: Making decisions about AI, making decisions with AI, and implications of decision-making with AI.

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Organizations as Digital Enactment Systems: A Theory of Replacement of Humans by Digital Technologies in Organizational Scanning, Interpretation, and Learning

Constantiou, I., Joshi, M. P., & Stelmaszak, M. (Equal authorship; authors listed alphabetically.)

Digital transformation has become a dominant phenomenon of interest among information systems scholars. To account for this phenomenon, it is imperative to develop a theoretical understanding of its processes and objects. We adapt a seminal organizational theory that conceptualizes organizations as interpretation systems to a possible future of organizations. We theorize digital transformation as a progressive replacement of humans by digital technologies in performing an organization’s fundamental activities underpinning the processes of scanning, interpretation, and learning that encompass an organization’s interaction with its environment. As a result, organizations cease to be human interpretation systems and instead turn into digital enactment systems, where digital technologies, instead of humans, nearly autonomously create and act upon information. We illustrate this digital transformation theory using the example of high-frequency trading. This transformation redefines the relationship among organizations, information, and the environment, changing the role of humans and reshaping strategic decision-making. Thus conceived, digital transformation offers a concrete way of theorizing and accounts for deep implications on the nature of organizations and organizing in the digital age.

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Algorithms as Co-Researchers: Exploring Meaning and Bias in Qualitative Research

Günther, W., Thompson, M., Joshi, M., & Polykarpou, S.

We examine how augmenting qualitative inquiry with computational tools—what we call algorithmic qualitative research—raises foundational epistemological and methodological questions. Using Father Busa’s early collaborations with IBM as a touchstone, we show that algorithms and researchers co-produce data and meaning, challenging empiricist assumptions about data’s naturalness, objectivity, and equivalence. We argue for a reflexive “dance” between researcher and algorithm, in which choices about data, models, and parameters are made transparent rather than masked by claims of objectivity. Illustrated through topic modeling, we outline practical checks and balances that help transform algorithmic assistance from a source of opacity and bias into a vehicle for clarity, rigor, and interpretive depth.

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Why So Many Data Science Projects Fail to Deliver

Joshi, M. P., Su, N., Austin, R. D., & Sundaram, A. K.

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so. To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and we suggest corresponding solutions to address them.

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🏆 AIS Outreach Practice Publication Award (2022)
⭐ Top 10 Most Popular Articles of 2021 @ MIT SMR