Researcher as manager of the academic writing process with generative artificial intelligence. Framework for systematic literature review in management

As generative artificial intelligence (GenAI) becomes increasingly integrated into academic workflows, the traditional approach to conducting systematic literature reviews (SLRs) is undergoing a fundamental shift. This paper argues that researchers should move away from viewing GenAI merely as a passive tool and instead adopt a managerial perspective. In this model, the researcher acts as a manager, while various GenAI applications function as digital employees that perform operational tasks such as scoping, literature identification, and drafting.

The author maps the standard three-stage SLR process—planning, conducting, and reporting—to core managerial functions: planning, organizing, leading, and controlling. By aligning specific GenAI tools (e.g., Elicit, SciSpace, ChatGPT) with these phases, the framework provides a structured approach for researchers to maintain oversight. The researcher remains responsible for defining the research purpose, verifying AI-generated outputs, and ensuring the ethical and methodological integrity of the final review. This shift allows for greater efficiency and scale, enabling a single researcher to manage tasks that previously required a larger team.

While this managerial model offers significant productivity gains, it introduces complex ethical and methodological challenges. The author highlights concerns such as the "Butler effect," where over-reliance on AI may erode a researcher's own critical thinking and analytical skills. Furthermore, the "black box" nature of many GenAI tools complicates the transparency and replicability required for rigorous academic research. The paper also notes that current publishing policies are often restrictive regarding AI-generated content, creating a tension between technological potential and institutional standards.