AI-Driven Intelligence Layer 4 SME-to-B2B Marketplace Optimization: The AISLE Framework

Abstract

Systemic inefficiencies in sourcing discovery, manual demand forecasting, and flawed pricing strategies are still an operational challenge for small and medium enterprises (SME) within the business-to-business (B2B) digital commerce. This paper presents the Adaptive Intelligence for SME-Led E-commerce (AISLE) framework, an adaptive intelligence layer for optimum operation of B2B marketplace for resource-limited organisations in emerging economies using artificial intelligence. AISLE combines three fundamental features – the machine learning-based supplier matching feature, the real-time demand prediction feature and the dynamic pricing optimization feature – all designed to run in the infrastructural and capital constraints typical of SMEs. The multi-phase empirical evaluation was performed over a six-month deployment period with 47 SMEs from retail, manufacturing and logistics from India and Southeast Asia. The quantitative results show that ML-driven supplier matching can cut the search time by as much as 68% and increase the accuracy of order forecasting by 28.7% while adding gross margin improvements of between 12% and 18%. Three approaches of scalable architectures are suggested: (1) lightweight recommendation engines capable of deployment on constrained infrastructure, (2) federated learning mechanisms that support organizational data privacy while also providing shared learning power across the organization, and (3) cost-effective API-first integration approaches that work with legacy ERP and in-store systems. The AISLE framework is vendor-neutral and has proven to have the potential to be widely applied to segments of micro-SMEs, which have not been able to be considered for mainstream digital commerce application before. The findings add to the theoretical conversation of accessibility of AI to SMEs, design of the B2B marketplace, and how digital is changing SMEs; and have practical implications for platform architects, policymakers, and practitioners in the context of the emergent economy.