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.

翻译：随着各组织努力应对生成式人工智能（GenAI）的快速采用，本研究通过对二次研究及研究议程的系统性文献综述，整合了当前的知识状态。通过分析2023年以来发表的28篇论文，我们发现，尽管GenAI为生产力和创新提供了变革性潜力，但其采用受到多重相互关联的挑战制约，包括技术不可靠性（幻觉、性能漂移）、社会伦理风险（偏见、滥用、技能侵蚀）以及系统性治理真空（隐私、问责、知识产权）。通过社会技术视角解读，这些发现揭示了GenAI快速演进的技术子系统与适应较慢的社会子系统之间持续存在的错位，从而将信息系统研究定位为实现联合优化的关键。为弥合这一鸿沟，我们讨论了一个研究议程，该议程将信息系统学术研究的重点从分析影响转向积极塑造技术能力与组织流程、社会价值观及监管制度的协同演化——强调人机混合协同系统、情境化验证、概率系统的设计原则以及适应性治理。