New findings show one-third of consumers now use AI as the decisive factor in purchases, bypassing traditional search engines. For Indian retailers, this shift makes AI-ready data infrastructure essential, as product visibility, pricing, and inventory accuracy now directly influence AI-driven discovery and final sales conversion.
Traditional search engines are increasingly being bypassed by AI-powered tools as consumers look for more curated and direct purchasing paths. Recent data highlights that AI users are now 3.2 times more likely to prioritize AI systems over conventional search methods when making buying decisions. This behavior suggests a fundamental change in the digital retail funnel, where the search engine is no longer the starting point for product discovery.
For the Indian retail sector, this transition presents both a technical challenge and a strategic opportunity. Companies operating large-scale e-commerce platforms, such as Reliance Retail, Tata Digital, and quick-commerce players like Zomato’s Blinkit or Zepto, must now ensure their internal data ecosystems are optimized for AI retrieval. Unlike traditional SEO, which focuses on ranking algorithms for web crawlers, AI-driven search relies on granular product information, real-time inventory levels, and dynamic pricing data. If a retailer’s backend systems are not feeding accurate, structured data to these AI agents, their products risk being invisible to the growing segment of AI-first shoppers.
The Platform Advantage and Margin Risks
While standalone AI platforms currently lead in the discovery phase, retailer-integrated AI assistants are proving more effective at completing the actual transaction. About 15% of users now prefer using retail-native assistants, largely because these tools can access real-time logistics and payment systems. This integration creates a defensive moat for large retailers that can control the entire customer journey from intent to delivery. However, this shift also introduces new risks. As AI agents gain more influence, there is a potential for margin pressure if these agents start demanding preferential placement or if retailers are forced to increase spending on data engineering to maintain their rankings within the agentic commerce ecosystem.
Data Compliance and Operational Needs
Efficiency is the primary driver for consumers moving to AI, with nearly 80% of users citing time savings and better filtering as key benefits. However, this shift places significant responsibility on retailers regarding data security. As Indian companies navigate the evolving requirements of the Digital Personal Data Protection (DPDP) Act, managing the high volume of user data required to train and run these personalized AI agents becomes a regulatory and operational priority. Companies that fail to protect this data while attempting to scale autonomous shopping features may face reputational risks and compliance hurdles.
Looking ahead, the next phase for the industry involves the scaling of autonomous agents capable of completing entire transactions with minimal human intervention. Investors may monitor how quickly Indian retail firms can transition from basic search-based apps to agentic commerce platforms, as well as the capital spending required to build the necessary data architecture to support these systems.