By Sameer Kanodia, Vice Chairman and CEO, Lumina Datamatics & TNQTech
Retail is getting smarter, and shoppers are already changing their shopping behaviour. AI is gradually helping consumers discover products, compare options and make purchase decisions. In many ways, “Hey AI, please tell me what to buy?” is becoming a new question for everyday shopping, with technology emerging as a new shopping assistant.
At the NIQ Insight Summit India 2026, 92% of surveyed urban Indian shoppers said they had used at least one AI tool while shopping in the previous month. Another 87% were comfortable receiving AI-powered product recommendations, while 93% expressed interest in using AI to manage household shopping in the future. Retailers are moving just as quickly, with 96% reporting current AI usage and plans to maintain or expand it. As AI becomes part of everyday retail, the quality of the product information behind these decisions matters more than ever.
Product information is becoming a business asset
Every retail decision begins with understanding the product. Its name, category, size, colour, dimensions, ingredients, specifications, price and availability may seem like basic details, but together they create the information layer that helps retailers and customers make sense of what is being sold.
The challenge is that this information rarely comes from one place. It moves between suppliers, brands, marketplaces, internal systems and retail channels, often in different formats and naming conventions.
Descriptions vary, attributes go missing, images may not match specifications, and the same SKU can appear under different identifiers. India’s fragmented retail environment makes this harder as businesses operate across marketplaces, brand websites, quick-commerce platforms and physical stores while managing constantly changing catalogs.
This makes product information more than a content management task. It becomes part of the infrastructure behind forecasting, inventory and customer decisions.
When fragmented data becomes a decision problem
Consider demand forecasting. If duplicate or inconsistent SKU records distort historical sales, forecasting models may struggle to identify genuine demand patterns. Incomplete information about sizes, colours or variants can make it equally difficult to understand what is actually selling.
The impact quickly moves through the business. A retailer may overstock an unpopular variant while running short of what customers want. Procurement becomes less accurate, working capital gets tied up in slow-moving inventory and markdown pressure increases.
Clean, structured product information allows retailers to look at performance at SKU level, understand regional preferences and make more informed decisions about what to stock, where to stock it and when to replenish it. This level of granularity matters in India, where consumer preferences can vary considerably across markets.
Better product content can improve discovery
The impact is also clear in the customer journey. Search works only when product information is clear and consistent. If one supplier calls a product a ‘sports sneaker’ while another calls it ‘running shoes’, relevant products may not show up together. Missing details can also make it harder for customers to filter, compare and find the right products.
This becomes even more important as consumers use AI to discover products. Recommendation engines and AI shopping tools need detailed, structured product information to understand what a product is, how it differs from alternatives and which customer needs it can address.
Product content is therefore no longer simply about filling a product page. Accurate descriptions, consistent attributes, relevant imagery and standardised categorisation can improve discoverability while reducing the gap between what shoppers expect online and what they eventually receive.
Pricing and assortment need the same foundation
Product information also influences decisions that directly affect profitability. Pricing increasingly requires retailers to connect product-level information with demand, competitor activity, promotions and customer behaviour. When these records are structured and unified, businesses can make more precise pricing decisions instead of relying only on broad category trends.
Assortment planning benefits in much the same way. Clean product and sales data can reveal which variants perform in specific markets, helping retailers build assortments around actual demand. For quick-commerce businesses, accurate product and inventory information is even more critical because what appears available needs to reflect what can actually be delivered from a nearby location.
Making product information ready for AI
The growing use of AI is changing what retailers need from their product information. As businesses use AI for demand planning, customer experience and logistics, the focus now needs to shift from simply collecting information to making it reliable and usable. That means having consistent product names and categories, standardised identifiers, centralised information and regular checks for duplicates, missing details and inconsistencies. Master data management and automated enrichment can help maintain this accuracy as catalogs grow and change.
The bigger opportunity is to make this information useful across the entire retail journey. A well-structured product catalog can support better search, sharper recommendations, more informed pricing and stronger assortment decisions. It can also give AI the context it needs to understand products more accurately.
As retail becomes more AI-led, product information is moving from the background to the center of decision-making. Retailers that build this foundation well will be better positioned to turn AI from a technology investment into a practical business advantage.