AI-Driven Decision Support Systems for Strategic Business Intelligence in Small and Medium Enterprises (SMEs)

Abstract

SMEs face volatile market demands and resource constraints. AI-driven Decision Support Systems integrate predictive analytics and real-time data, empowering strategic Business Intelligence to optimize decisions. To evaluate the efficacy of AI-Driven Decision Support Systems in enhancing strategic Business Intelligence among SMEs, assessing system impact on decision accuracy, operational efficiency, and user satisfaction at Troy University. An observational study conducted at the Department of Computer Science, Troy University, from January 2024 to June 2024. A sample of 62 SMEs participated, utilizing a AI-DSS prototype for strategic intelligence tasks. Key metrics included decision accuracy, time-to-decision, and user satisfaction. Descriptive statistics provided means and SD; analysis involved paired t-tests, ANOVA, regression modeling, and p-value reporting with α = 0.05. Implementation of the AI-DSS yielded a significant improvement in decision accuracy (mean increase from 72.5% ± 8.3% to 88.9% ± 5.1%, Δ = 16.4%, p < 0.001). Time-to-decision decreased by 35.6% (mean duration reduced from 15.2 ± 4.5 minutes to 9.8 ± 3.2 minutes, p = 0.002). User satisfaction scores improved by 22.8% (from 3.4 ± 0.7 to 4.2 ± 0.5 on a 5-point Likert scale, p = 0.005). Regression analysis indicated that AI-DSS usage predicted 42% of variance in operational efficiency (R² = 0.42, F (1,60) = 43.5, p < 0.0001). SMEs with higher baseline digital maturity exhibited greater accuracy gains (interaction effect β = 0.27, p = 0.018). Inventory turnover error significantly fell 28.4% (MAPE from 12.1% ± 2.3% to 8.7% ± 1.9%, p = 0.007), and campaign ROI rose 18.0% (p = 0.012). AI-DSS implementation in SMEs significantly enhanced decision accuracy, reduced processing time, and improved efficiency and satisfaction. Integrating explainable AI and governance frameworks can facilitate adoption and ensure strategic intelligence.