A System Architecture for AI-Driven Market Entry Strategy Generation Using Large Language Models

Businesses looking to expand into international markets need high-quality market research to make strategic decisions and develop effective market entry strategies. However, conducting extensive secondary market research that contains all the necessary data is very time-consuming and resource-intensive. In recent years, Artificial Intelligence (AI) has been widely used for marketing applications. Conducting market research and generating market entry strategies are functions that AI, especially Large Language Models (LLMs), could support businesses with. In this paper we propose a theoretical model of how an LLM could be trained to ensure the quality of the output by conducting secondary market research and, based on it, to generate realistic market entry strategy for decision making about international expansion. One of the key elements of the proposed pipeline is an adjustable scoring system which ensures input data reliability and transparency and, as a result, helps to provide a reliable output which could be used for strategic decision-making. Compared to traditional market research approaches, the proposed methodology offers significant improvements in speed, resource efficiency, and accessibility and is aimed to support the businesses in their expansion into new international markets.