Abstract

Artificial Intelligence (AI) has shifted e-commerce competition from scale-and-supply to prediction-and-orchestration: who anticipates demand, reduces friction, protects trust, and personalizes value at the lowest marginal cost. This paper synthesizes secondary evidence from multilateral datasets, policy documents, market reports, and peer-reviewed research to analyze how AI changes measurable outcomes across the e-commerce value chain. Using a structured evidence-mapping method, the study integrates global indicators of e-commerce scale (e.g., UNCTAD estimates of total business e-commerce sales), national market trajectories in India, and operational performance proxies such as conversion frictions and fraud loss projections. Four analytical tables are used to connect AI capabilities (ML, NLP, computer vision, and generative AI) to e-commerce functions, quantify adoption-and-scale signals, compare pre-AI versus AI-enabled performance patterns, and evaluate risk–control trade-offs under emerging regulatory constraints. Results indicate that AI-enabled personalization is consistently associated with revenue and marketing productivity gains, while fraud detection and risk scoring become central to sustaining digital commerce growth amid rising attack sophistication. However, consumer resistance to automated service, data governance constraints, and “agentic AI” project failure risks create a non-linear ROI landscape that favors selective deployment, strong measurement discipline, and governance-by-design. The Indian ecosystem—characterized by rapid growth, platform competition, and expanding digital public infrastructure—shows accelerated opportunity for AI in vernacular discovery, logistics, trust and safety, and credit enablement, alongside heightened compliance obligations under evolving data protection and consumer protection rules. The paper concludes with a practical research-grounded framework for AI investment prioritization, measurement, and governance in e-commerce.