The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas
- URL: http://arxiv.org/abs/2603.11842v1
- Date: Thu, 12 Mar 2026 12:06:25 GMT
- Title: The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas
- Abstract summary: Generative AI (GenAI) offers transformative potential for productivity and innovation, but its adoption is constrained by multiple interrelated challenges.<n>Interpreted through a socio-technical lens, these findings reveal a persistent misalignment between GenAI's technical subsystem and the slower-adapting social subsystem.<n>To bridge this gap, we discuss a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities.
- Score: 31.27141835012598
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As organizations grapple with the rapid adoption of Generative AI (GenAI), this study synthesizes the state of knowledge through a systematic literature review of secondary studies and research agendas. 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 (privacy, accountability, intellectual property). Interpreted through a socio-technical lens, 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. To bridge this gap, we discuss a 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--emphasizing hybrid human--AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
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