An AI-Driven Framework for Automating SME Commercial Workflows with Robotics and Immersive Technologies

Abstract

Commercial operations across trading, import/export, logistics, and technology distribution are being reshaped by the convergence of artificial intelligence (AI), machine learning, multi-agent robotics, and Extended Reality (XR). Small and Medium-sized Enterprises (SMEs) feel this shift acutely: they face the same pressures as their larger competitors; labor shortages, high-SKU inventories that resist tidy categorization, narrow margins, and customer expectations set by Amazon-grade fulfilment, but rarely command the capital or the structured warehouse environments that make industrial automation straightforward. Existing frameworks for AI-driven automation and digital twins have been developed primarily for large-scale industrial settings and do not account for the capital, infrastructure, and organizational constraints specific to SMEs, leaving a gap in SME-scoped integration models. This research addresses that gap by asking how AI-driven robotics and immersive technologies can be integrated to optimize commercial workflows in SMEs operating in dynamic logistics and trading environments. The proposed framework is grounded in Sociotechnical Systems Theory, which treats technology and organizational workflows as jointly designed and mutually adapting, and follows a Design Science orientation in which the architecture itself is constructed as an evaluable artifact rather than a purely descriptive model. Methodologically, the study conducts a narrative synthesis of literature on embodied AI, computer vision, digital twins, VR training, and AR-assisted operations, combined with workflow analysis to identify where SMEs lose the most time and money. These are translated into a four-layer system architecture (perception, cognition, execution, integration) deployed through a four-phase implementation model: needs assessment, digital-twin and VR pre-training, controlled hardware pilot, and AR-supported scaling. The contribution of the study is twofold: conceptually, it brings together several technologies that are often discussed separately in the literature, while focusing specifically on the needs and constraints of SMEs while practically, it proposes a phased roadmap that can help SMEs adopt these technologies gradually, reducing both financial and operational risks. The approach also emphasizes human–robot collaboration rather than replacing human workers. The study does not include experimental validation, it presents a conceptual architecture and implementation roadmap consistent with a Design Science artifact-construction stage that can serve as a basis for empirical testing in real commercial environments.