Integrating Artificial Intelligence and Business Intelligence into SME Enterprise Resource Planning Systems

This review aimed to address two questions: what are the findings from the current literature on the part AI and BI play in SME ERP projects, and where does the literature fall short? The present scholarship has isolated the elements of this picture separately — barriers and drivers of ERP adoption in SMEs, the performance impacts of AI in organisational environments, and the theoretical frameworks that can explain IS value creation — but none have combined these elements in a coherent and empirically grounded account of their interactions in SME ERP environments. The second question is answered with five clearly formulated gaps in the existing literature that delineate a well-defined and truly relevant empirical research agenda. The proposed integrated TOE-D&M-RBV framework is a theoretical contribution in its own right. The TOE framework offers the adoption structure and outlines under which technological, organisational, and environmental conditions SMEs are in a position to benefit from the AI and BI capabilities of ERP. The D&M Model offers the performance mechanism, describing the connection between AI-enhanced System Quality, BI-enhanced Information Quality, and ERP adoption to Net Benefits. The RBV provides the strategic account — SMEs who acquire the absorptive capacity to leverage these capabilities are more likely to accumulate VRIN resources that create sustained competitive advantage. The Diffusion of Innovations theory, coupled with TOE, provides an explanation for the temporal dynamics of AI-ERP diffusion across the SME population. Importantly, this review has revealed that ethical AI governance is not an add-on but an integral part of the value creation model. The risks for SMEs without sufficient governance capacity are potentially greater than the performance gains achievable by adopting AI, and future studies should operationalise governance capacity alongside the more familiar technological and organisational dimensions. The proposed empirical study, following a quantitative research approach using primary data collected from SMEs in an emerging-economy setting, is expected to test the integrated framework, fill the co-mediation gap, and produce results with real-world applications for SME managers, IS researchers, ERP vendors, and policy makers working to address the growing SME AI adoption gap.  

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