MLPESTEL : the new era of forecasting change in the operational environment of businesses using LLMs

Abstract

This study explored the integration of futures studies into business strategy, focusing on the development of a novel theoretical framework and computational methods for forecasting future operational environments. Recognizing the critical role of anticipating technological paradigm shifts, as evidenced by the downfall of companies such as Blockbuster, Palm, and Nokia, we proposed a new framework called MLPESTEL or Multilayer PESTEL. The framework combines PESTEL analysis with Bronfenbrenner’s Ecological Systems Theory. This amalgamation aims to provide a more holistic understanding of a company's operational environment, extending from macro to micro levels. However, adapting Bronfenbrenner’s model, originally focused on children's social development, to a business context presents a unique challenge.

Our methodology involved employing advanced AI tools, specifically large language models (LLMs), to analyze and predict changes in various business environments. This approach marks a significant shift from traditional AI applications, which predominantly rely on numerical data, to leveraging LLMs for textual data analysis. Our goal was not to focus on specific companies but to develop and validate generic models applicable across different organizational contexts. By analyzing forecasts for several existing companies, we aimed to validate our model's reliability.