AI E-Commerce Projects Risk Failure Without Clean, Centralised Data: Report

E-commerce firms seeking success with artificial intelligence must prioritize data readiness, according to a Nisum report. Data infrastructure is crucial for AI platforms to access comprehensive business information. Fragmented product data, inventory records and customer details often prevent AI from delivering accurate forecasts and automated decisions.

Poor data quality can also exacerbate issues, as inaccurate inventory counts or duplicate customer records can be amplified by AI systems. India's e-commerce market is projected to surge to $250 billion by 2030, driven by Gen Z, AI, and 150 million new shoppers, but integrating systems for personalization, dynamic pricing, demand forecasting, and inventory management poses significant challenges.

Retailers need an integrated architecture where AI, predictive analytics, and automation work together seamlessly, rather than as isolated applications. Centralizing information across all sales channels could enable businesses to transition from broad customer segmentation to personalized shopping experiences. Nisum executive Anurag Chauhan warns that only 5.5% of AI-using organizations currently generate significant financial returns.

To improve outcomes, businesses must identify fragmented data sources, establish data quality ownership, and implement governance frameworks before scaling AI deployments. Failure to address these data readiness issues could result in unreliable forecasts, disconnected insights, and AI projects failing to achieve full-scale commercial deployment.