Abstract

Modern businesses face increasing challenges in strategic planning due to the immense volume of digital information. The rapid growth of available data sources – from market trends and competitor activities to real-time economic indicators – makes comprehensive analysis within tight timeframes arduous. To address these challenges, large language models (LLMs) have emerged as potential tools, efficiently analyzing extensive information across diverse domains. However, LLMs face critical limitations: they cannot access proprietary information or real-time data and cannot engage in collaborative refinement processes that human experts traditionally use to develop and improve strategic analyses. This study introduces the Collective Intelligence of AI Consultants (CIAIC) framework, where specialized AI agents function as individual consultants, collaborating like a consulting team to enhance strategic analysis. The framework combines real-time data integration with collaborative AI mechanisms in a five-stage process: (1) human-guided objective definition, (2) retrieval-augmented draft generation, (3) supplementary data retrieval through multi-agents, (4) draft revision via collective intelligence, and (5) multi-perspective strategic plan compilation. Experimental evaluations using PESTEL and SWOT analyses demonstrate the effectiveness of this collective approach through both quantitative metrics and human preference assessments.