The Use of Generative Artificial Intelligence for Business Decision-Making

Abstract

The purpose of this study was to develop autonomous artificial intelligence agents capable of working as a cohesive group to solve intricate decision-making processes, ultimately pushing the boundaries of artificial intelligence’s applicability in high-stakes and real-world scenarios. An autonomous multi-agent model was designed and deployed using the Analytic Hierarchy Process (AHP) decision-making framework, with autonomous AI agents, such as CrewAI and AutoGen, orchestrating multiple stages of activities performed by four distinct agents leveraging generative AI (GenAI). These agents were applied to a use case for proof of concept. The agents used a qualitative dataset to generate management recommendations based on cost and market feasibility considerations. The model was tested with publicly available customer feedback data on oatmeal cookies. It synthesized 913 customer reviews, identified common complaints, provided a summary of potential solutions, and generated a summary of market opportunities, along with relevant challenges. Although a subject matter expert's review is necessary to evaluate the practicality and relevance of the recommendations, the results demonstrated the high potential of multi-agent models in synthesizing and distilling large datasets into actionable insights, thereby augmenting decision-making processes in business contexts. The model's modular design allows for enhancements in quality, accuracy, and practicality by incorporating additional datasets and new agents. This study underscores that the integration of GenAI within a multi-agent model empowers businesses to swiftly transform vast amounts of business intelligence data into practical recommendations.

Keywords

• analytic hierarchy process
• artificial intelligence
• autonomous artificial intelligence agents
• autonomous multi-agent model
• decision-making
• generative artificial intelligence
• qualitative data analysis