The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

Summary

A systematic literature review of secondary studies and research agendas on GenAI in Information Systems, synthesising 28 papers published since 2023. The authors organise findings around three intertwined challenge clusters — technical unreliability (hallucinations, performance drift), societal-ethical risks (bias, misuse, skill erosion), and a systemic governance vacuum (privacy, accountability, IP). They interpret these through a socio-technical lens, arguing that GenAI's fast-evolving technical subsystem is persistently misaligned with the slower social subsystem.

Contribution

A consolidated research agenda that "reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions," emphasising hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.

Method

Systematic literature review of secondary studies (reviews and research agendas) published since 2023; interpretation through the socio-technical systems lens.

Relevance to RISE

Seventeen-author systematic synthesis of secondary reviews on GenAI in IS, framed explicitly as sociotechnical research. The most comprehensive recent map of where the IS discipline is on GenAI; useful as a stratified entry point into the broader literature.

Critique / open questions

A review of reviews compresses methodological detail; the agenda is high-level and does not yet evaluate concrete artefacts or pipelines. The 28-paper corpus is small and skewed toward IS-discipline publications, so adjacent literatures (NLP, HCI, science studies) are under-represented.

Key quotes

"Analyzing 28 papers published since 2023, we find that while GenAI offers transformative potential for productivity and innovation, its adoption is constrained by multiple interrelated challenges, including technical unreliability (hallucinations, performance drift), societal-ethical risks (bias, misuse, skill erosion), and a systemic governance vacuum."

"These findings reveal a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization."