BEACON: Business Enhancement through Adaptive COordinated Networks

Abstract

Artificial intelligence (AI) is rapidly altering competitive landscapes across industries, providing substantial advantages to large corporations through sophisticated generative AI systems. Meanwhile, small and family-owned businesses, constrained by limited resources and technological capabilities, face an increasing competitive disadvantage. Addressing this challenge, our research introduces BEACON (Business Enhancement through Adaptive Coordinated Networks), an innovative framework employing networks of coordinated and self-adaptive AI agents designed to simulate essential business departments. Using a hierarchical agent-based paradigm, BEACON distributes specialized knowledge across marketing, finance, human resources, and strategic planning agents, which collaborate seamlessly to derive actionable insights for business owners. We implement BEACON through a novel embedded Python-based programming language (Pel), specifically designed for efficient and expressive orchestration of generative AI agents. Key benefits demonstrated by BEACON include enhanced transparency, reduced computing costs, improved privacy and confidentiality of sensitive business data, and adaptive capabilities enabling agents' continuous self-improvement and learning. Experimental validations illustrate the superiority of BEACON over traditional single-model generative AI approaches in terms of information retrieval accuracy, cost-efficiency, and interpretability. Our research contributes practically to the democratization of AI-driven business intelligence solutions, empowering smaller businesses to remain competitive, while also offering theoretical insights into adaptive multi-agent generative systems within quantitative marketing scholarship.