Dr Khalid Alnajjar and Dr Mika Hämäläinen explored in their MBA thesis the capability of artificial intelligence (AI) to forecast change in the operational environment of companies. For this task, they employed a large language model (LLM) and developed a new theoretical framework called MLPESTEL.
The Paradigm Shift that Made Forecasting Possible
Traditionally, machine learning (ML) techniques have relied on learning patterns form data for individual tasks. Therefore, such models have been able to formulate predictions only in a very limited application area such as weather forecasting or financial forecasting. However, the dawn of LLMs made it possible for AI to conduct reasoning in domains outside of narrow topics and on textual data instead of numerical data.
A Call for a New Framework
Although LLMs such as ChatGPT have incredible capabilities in terms of reasoning and answering a variety of prompts, they cannot tackle such a difficult problem as forecasting change by a mere prompt. LLMs can reason, but they need to be given the tools to do so – just like us humans. Furthermore, such a complex task must be split into smaller subproblems.
The researchers elaborated a new framework called MLPESTEL, which draws its inspiration from PESTEL, a framework traditionally used in business research, and the Ecological Systems Theory, a framework commonly used to understand social development of a child. The former framework is important for the business application area of the research whereas the latter was used to divide each individual PESTEL category into four different subsystems – micro, exo, meso and macro systems.
The resulting framework was quite complex for a person to conduct analysis with, but not at all too demanding for an LLM which can easily operate on such a level of complexity.
Early Results on AI-based Forecasting
The researchers investigated the viability of their method by studying the predictive capabilities of an LLM using the MLPESTEL framework on two international companies: Nokia and Tesla. The method was able to correctly predict the opportunity 5G technology brought to Nokia and the difficulties of global chip shortage that impacted Tesla.
The results obtained in the thesis work are promising and serve as a proof of concept. LLMs have reached such a maturity level that they can be used in forecasting tasks. MLPESTEL has extended the theoretical capability of conducting forecasting in the context of operational business environment.
This research has paved the road for future studies on LLM-driven forecasting and futures studies. The findings serve as a stepping stone for a more comprehensive platform to be developed at Metropolia University of Applied Sciences.
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