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By Sameer Kanodia, Vice Chairman and CEO, Lumina Datamatics & TNQTech
Picture a shopper in Mumbai asking her phone a simple question: “Which washing machine is best for a family of four?” She never opens a search engine or scrolls through a page of blue links. An AI assistant simply answers, drawing on whichever brand described its product clearly enough to be understood, compared, and trusted. This is no longer a hypothetical scene. It is already how a growing share of buying decisions unfold, and the numbers behind this shift are striking.
More than 80% of Indian consumers now use AI to support shopping decisions, while nearly 70% expect their reliance on AI to increase in the coming years. More than 60% also trust AI recommendations as much as, or more than, traditional expert advice.
These numbers signal a fundamental shift in how products are discovered. Instead of browsing multiple websites, consumers are increasingly turning to AI assistants for product recommendations, comparisons, and buying advice.
As a result, brands are moving beyond traditional Search Engine Optimization (SEO) towards Generative Engine Optimization (GEO), where success depends not just on being discoverable, but on providing product information that AI systems can accurately interpret, evaluate, and confidently recommend.
Product content has evolved beyond marketing copy into structured business data that shapes how products are represented across digital commerce. Rich product attributes, specifications, compatibility details, FAQs, warranty information, and contextual descriptions create a complete digital representation of every product.
When this information is incomplete or inconsistent, brands create gaps that limit accurate interpretation across emerging digital discovery channels.
In an AI-first environment, product content is no longer a supporting asset but the foundation of digital visibility, enabling brands to improve discoverability, strengthen credibility, and remain competitive in increasingly AI-driven customer journeys.
Consumers are moving beyond rigid keywords and asking increasingly specific, conversational questions such as, “Recommend running shoes for beginners with flat feet.”
Brands should move beyond feature lists and generic claims to explain how products solve real customer problems. Practical use cases, comparison tables, compatibility information, buying guides, and comprehensive FAQs provide the depth AI systems need to generate accurate recommendations.
This is particularly relevant in India, where conversational and multilingual search continues to grow. Product content that mirrors how people naturally ask questions is far more likely to appear in AI-generated responses than content optimized solely around traditional keywords.
Consistency has become just as important as completeness. Product information often exists across brand websites, marketplaces, retailer listings, review platforms, and partner ecosystems. Even small inconsistencies in specifications, pricing, or product descriptions can create confusion and weaken a brand's digital credibility.
Well-structured product data also makes information easier to interpret across AI-powered commerce ecosystems. Standardized taxonomy, enriched metadata, detailed product attributes, and machine-readable formats reduce ambiguity and enable products to be represented accurately across search, marketplaces, and AI assistants.
At the same time, AI evaluates signals beyond owned channels. Reviews, expert commentary, editorial coverage, and user-generated content collectively shape a brand's credibility. This makes content governance just as important as content creation.
The rapid rise of AI-generated content has made well-written product descriptions easier to produce than ever before. Ironically, this also makes them easier to ignore.
As more brands publish similar content, originality becomes the real differentiator. AI systems increasingly favour content that contributes something unique, whether it is expert guidance, proprietary insights, detailed product knowledge, or evidence drawn from real customer experiences.
Treating product content as a living asset rather than a one-time marketing exercise allows brands to continuously enrich catalogs, reflect changing customer needs, and maintain relevance across evolving AI-driven discovery platforms.
This shift already shows up in real client work. For a large online furniture and home décor platform managing a catalog of more than 100,000 products across 500-plus cities, richer, more consistent product content and an AI-assisted visualization layer helped shoppers understand products with greater confidence before they bought.
Such outcomes rest on the same disciplines described above: closing attribute gaps, standardizing taxonomy, and eliminating duplicate or inconsistent listings across catalogs running into hundreds of thousands of SKUs. Our enterprise retail work shows that product content built this way does not just read better. It performs better, whether the audience is a human browsing a page or an AI assistant deciding what to recommend.
AI can accelerate catalog enrichment, generate product descriptions at scale, and streamline content operations. However, creating product content that is accurate, nuanced, and genuinely useful still depends on domain expertise and editorial judgment.
Human oversight ensures technical accuracy, contextual relevance, editorial consistency, and information that supports informed customer decisions. Rather than replacing people, AI is most effective when it complements human expertise.
The strongest content strategies therefore combine AI's speed with human judgment to deliver product information that is reliable, trustworthy, and aligned with evolving customer expectations across increasingly AI-driven digital commerce and discovery experiences.
As AI becomes the first point of interaction for product discovery, brands must rethink what product content is expected to achieve. It is no longer enough to attract clicks. Product content must become a reliable source of truth that enables accurate discovery, meaningful comparison, and confident purchase decisions across AI-driven experiences.
The brands that succeed will be those investing in richer product information, stronger data quality, and content ecosystems built around accuracy, context, and trust.
In the AI search era, the question is no longer whether a product can be found. It is whether AI understands it well enough to recommend it.
