LLM-Based Multi-Agent Support for Strategic Business Planning - Aleh IOTCHANKA; Jaroslaw A. CHUDZIAK (Communications of International Proceedings, 2025)
Strategic business planning has to be initiated to form flourishing businesses out of new ideas. Traditional strategic planning methods are, however, lengthy, disconnected, and subject to human mistakes and hence unsuitable for the dynamically changing environment of markets. This paper suggests a modular AI-powered system on a coordinated multi-agent system to enable strategic decision-making processes to become effective. Our method incorporates veteran autonomous agents, which are committed to activities such as market analysis, financial projections, risk analysis, and plan formulation. The agents use advanced computational methods such as Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) in order to facilitate combined, accurate, and responsive planning. The agents operate autonomously but share contextual information and output via an intelligent orchestration layer to provide coherent and consistent outcomes. The effectiveness of the suggested framework is illustrated by means of a detailed case study on a smart home energy management product. Findings indicate a significant improvement in efficiency, analysis depth, and consistency compared to conventional planning techniques. Most importantly, the system allows human intervention at strategic locations, supporting a productive human-AI collaboration model. The paper highlights the potential vulnerability of AI strategic planning, i.e., biased outcomes and dependence on data quality, and stresses ethical imperatives for leveraging advanced AI tools. The value added of the paper is to illustrate the applied potential and methodological innovation of multi-agent AI coordination for strategic business planning.
Keywords: Multi‑Agent Systems; Strategic Planning; LLM‑Orchestration; Decision Support