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Inventing with Machines: Generative AI and the Evolving Landscape of IS Research

Ram D. Gopal¹; Jingjing Li²; Kai Riemer³; Suprateek Sarker²; Param Vir Singh⁴; Anjana Susarla⁵; Martin Bichler⁶; Jason Bennett Thatcher⁷

¹ Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom · ² McIntire School of Commerce, University of Virginia, Charlottesville, Virginia 22903; · ³ University of Sydney Business School, University of Sydney, Camperdown, New South Wales 2006, Australia · ⁴ Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; · ⁵ Broad College of Business, Michigan State University, East Lansing, Michigan 48824 · ⁶ Department of Computer Science, Technical University of Munich, 85748 Munich, Germany; · ⁷ Leeds School of Business, University of Colorado Boulder, Boulder, Colorado 80309

Generative artificial intelligence (AI) is not merely changing how information systems (IS) research gets done—it is reshaping what research can be. We stand at a pivotal moment where machines can help generate hypotheses, synthesize vast literatures, and identify patterns that would take human researchers months to uncover. Yet, this unprecedented capability presents equally unprecedented risks to scholarly integrity. Because the field is uniquely positioned to understand sociotechnical transformations, IS research faces an extraordinary opportunity to pioneer “inventing with machines” while preserving the human insight and oversight that gives scholarship, as currently defined, its meaning. This transformation demands more than tool adoption. It requires a reimagination of scholarly infrastructure, norms, and practice. However, this transformation of research tooling creates a dangerous paradox: Powerful AI tools are now accessible to researchers who lack the technical literacy to understand and use them responsibly, threatening everything from citation accuracy to theoretical validity. Yet within this paradox lies the potential for revolutionary advances in how we craft our future as scholars. Informed by the sociotechnical perspective, we argue that the path forward requires coordinated community action that goes far beyond individual skill development. The IS community must lead the development of specialized AI tools that consider our theoretical traditions, create educational frameworks that preserve scholarly values while embracing computational capabilities, and pioneer review processes that harness AI’s analytical power without ceding human control, at least, in the short run. Success will determine not only the future of IS scholarship but our field’s capacity to guide other disciplines through this fundamental transformation of academic practice. The era of human-AI collaboration in research has already begun. How we govern and guide it will define the next generation of scholarly discovery.

DOI

10.1287/isre.2025.editorial.v36.n4

Volume

36 (4)

Pages

1949-1967

Language

en

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