Design and Implementation of Small Language Models for Process Automation in Small Businesses

Abstract

The artificial intelligence landscape has undergone a significant transformation with large language models, yet these technologies often remain inaccessible to small and medium-sized enterprises due to their resource-intensive nature. Small Language Models (SLMs) emerge as a viable alternative, offering domain-specific capabilities with reduced computational demands. This technical article examines the architecture, deployment strategies, and practical applications of SLMs for business process automation in SMEs. Through a comprehensive analysis of implementation approaches, the article demonstrates how carefully selected model architectures, domain adaptation techniques, and strategic deployment options enable these lightweight alternatives to effectively streamline operations, enhance customer interactions, and support data-driven decision-making without imposing prohibitive costs. Case studies reveal substantial improvements in operational efficiency and rapid return on investment, while addressing common implementation challenges including data scarcity, integration complexity, and stakeholder expectations. As specialized architectures, federated learning approaches, multimodal capabilities, and automated optimization tools continue to evolve, SLMs represent a pragmatic pathway for democratizing advanced natural language processing across diverse business environments.