AI-Powered Business Intelligence and Profit Management System for Small Retail Businesses: A Review

Abstract: Small retail businesses generate significant amounts of data through sales, purchases, inventory, expenses, customer payments, and supplier activities. However, many small retailers still depend on manual records and basic software, making it difficult to obtain timely insights about business performance and profitability. Artificial Intelligence (AI), Machine Learning (ML), and Business Intelligence (BI) technologies provide opportunities to transform operational data into useful information for decision-making. This review examines existing research on AI-enabled business intelligence, retail analytics, inventory management, demand forecasting, and profit-oriented decision support systems, with particular emphasis on small and medium-sized businesses. Recent studies indicate that machine learning can support demand prediction, inventory optimization, and business forecasting, while BI dashboards can provide accessible visual representations of business information. However, existing solutions frequently focus on individual functions such as forecasting, inventory, or analytics rather than providing an integrated and affordable system designed specifically for small retailers. The review identifies challenges including limited historical data, technical expertise, implementation cost, model reliability, and data quality. Based on these findings, an integrated AI-powered business intelligence and profit management system is proposed as a potential approach for combining sales, inventory, expenses, and profit analysis with AI-based insights.

Keywords: Artificial Intelligence, Business Intelligence, Machine Learning, Retail Analytics, Profit Management, Inventory Management