@Article{	  eInformatica2027Art01,
  author	= {Aleksander Jarzębowicz and Adam Przybyłek and Jacinto Estima and Yen Ying Ng and Jakub Swacha and Beata Zielosko and Lech Madeyski and Noel Carroll and Kai-Kristian Kemell and Bartosz Marcinkowski and Alberto Rodrigues da Silva and Viktoria Stray and Netta Iivari and Anh Nguyen-Duc and Jorge Melegati and Boris Delibašić and Emilio Insfran},
  title		= {The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas},
  pages		= {270101},
  doi		= {10.37190/e-Inf270101},
  year		= {2027},
  volume	= {21},
  number	= {1},
  keywords	= {Generative AI (GenAI), Large Language Models (LLM), ChatGPT, Information Systems, Systematic Literature Review, Research Agenda, Roadmap, AI Ethics, AI Governance, Socio-Technical Systems},
  journal	= {e-Informatica Software Engineering Journal},
  url		= {https://www.e-informatyka.pl/EISEJ/papers/2027/1/1/},
  abstract	= { Context: The post-ChatGPT surge has rapidly reframed Information Systems (IS) research and practice. As organizations and society grapple with Generative AI (GenAI) adoption, a body of secondary studies and research agendas has emerged to synthesize early evidence and chart directions for future inquiry. 
Objective: This study conducts a systematic literature review of secondary studies and research agenda/roadmap papers to synthesize the state of knowledge on GenAI's benefits and challenges in IS, and to identify future research directions. 
  Method: We performed a systematic search across Scopus, Web of Science, and the AIS eLibrary for publications from 2023 onwards. Following a rigorous, multi-stage screening process, we selected a final set of 28 papers (18 secondary studies and 10 research agendas) for analysis using bibliometric mapping and thematic analysis. We also conducted a quality assessment of all sources to gauge confidence in each source's contribution to the findings.
Results: GenAI offers transformative potential to drive productivity, accelerate innovation, personalize services, and democratize access to expertise. However, its adoption is constrained by interrelated challenges: technical unreliability (hallucinations, performance drift), societal-ethical risks (bias, malicious misuse, skill erosion), and a governance vacuum (privacy, accountability, intellectual property).
Conclusions: Interpreted through a socio-technical lens, our 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 propose a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions -- emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance. For practitioners and policymakers, responsible adoption requires balancing automation with human augmentation alongside transparent governance and adaptive regulations to ensure broadly shared benefits. },
  note		= {Available online: 24 Aug. 2026},
  month		= aug
}