This practitioner paper discusses the use of genAI in a strategy essay-type assignment in a Business School. It explains the nature of the assignment, the preparation provided to students on the use of genAI: design of input prompts, the process of token generation using the transformer architecture of large language models, and evaluation of the output from a genAI. The paper discusses the outcome of the assignment from a learning point of view having examined the student results following the grading process. The paper compares the outcome of this work with the work of students in 2025 when students used genAI to analyse an assigned case. In 2026 an assigned case was not provided and students were allowed select an organization for analysis. The paper also considers the appropriateness of genAI (Grassini, 2023; Lappin, 2024; Lazovsky et al., 2024) for supporting strategic analysis using a variety of tools; it considers whether or not genAI provides better support for certain kinds of tool, e.g. for those tools such as PESTEL analysis that consider themes, patterns and regularities, than it does for other kinds of tools such as financial statement analysis that requires detailed analysis of numbers.

Grassini, S. 2023. Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692

Lappin, S. Assessing the Strengths and Weaknesses of Large Language Models. J of Log Lang and Inf 33, 9–20 (2024). https://doi.org/10.1007/s10849-023-09409-x

Lazovsky, Gal Sasson, Tuval Raz and Yoed Kenett. 2024. The art of creative inquiry – from question answering to prompt engineering. Journal of Creative Behavior, 59(1):1-16 DOI:10.1002/jocb.671