The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas
🤖 AI Summary
The rapid deployment of generative artificial intelligence in information systems confronts significant challenges, including technological unreliability, ethical risks, and inadequate governance, reflecting a misalignment between technical and social subsystems. Drawing on a sociotechnical systems perspective, this study synthesizes 28 secondary studies and research agendas published since 2023 to identify critical barriers and subsystem mismatches in generative AI adoption. It argues for shifting the focus of information systems research from reactive impact analysis toward proactively shaping the co-evolution of technological capabilities, organizational processes, societal values, and regulatory frameworks. To this end, the paper proposes hybrid human-AI systems and an adaptive governance framework, laying the foundation for a future research agenda oriented toward joint optimization.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Generative AI
socio-technical misalignment
technical unreliability
societal-ethical risks
governance vacuum
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative AI
socio-technical systems
adaptive governance
human-AI collaboration
probabilistic system design
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