Why AI struggles to give accurate product details and how retailers can fix it

Retailers and shoppers alike have grown accustomed to AI assistants like ChatGPT and Gemini delivering instant answers. Yet when it comes to product details—prices, availability, and specs—these tools frequently miss the mark. According to recent analyses, over 70% of product-related responses from major AI models contain inaccuracies. The root cause isn’t the AI itself but the outdated data pipelines powering these systems.

The hidden flaws in today’s e-commerce data pipelines

Most AI agents and shopping assistants still rely on three outdated extraction methods: traditional web scrapers, static page parsers, and generic web extraction tools. While these approaches worked a decade ago, they fail to meet the demands of modern e-commerce, where information is fluid and location-dependent.

Dynamic pricing and regional discrepancies

A single product can display vastly different prices, delivery windows, or stock levels depending on the user’s location. For example, a wireless earbud set priced at $99 in New York might cost $109 in San Francisco due to regional taxes or inventory differences. Yet most scraping systems lack the sophistication to detect and adapt to these variations. They treat each product page as a static snapshot, ignoring the real-time fluctuations that define today’s online markets.

The complexity of product variations

Consider the humble smartphone listing. A single iPhone model may appear as one product, but in reality, it encompasses dozens of SKUs based on color, storage capacity, carrier deals, and regional bundles. Traditional scrapers often collapse these variations into a single entry, stripping away critical details that shoppers and AI tools need to make informed decisions. This oversimplification leads to incomplete data, which in turn produces misleading AI responses.

From raw HTML to structured commerce intelligence

Another persistent challenge is the format of scraped data. Many systems still return unstructured content—raw HTML, markdown, or poorly formatted text—rather than clean, structured commerce intelligence. This forces AI models to spend valuable resources parsing and organizing data before they can even attempt to answer a query. The result? Slower response times, higher error rates, and frustrated users.

Why merchants must rethink their data strategies

For retailers, the stakes are high. Inaccurate product information erodes customer trust, increases return rates, and drives shoppers toward competitors with more reliable data. Meanwhile, AI-driven shopping assistants—whether embedded in apps or used via voice search—risk becoming liabilities if they consistently provide wrong details about availability or pricing.

The solution lies in moving beyond static scraping. Modern commerce data pipelines need to account for:

• Real-time data validation to ensure prices and stock levels are current.
• Location-aware extraction to capture regional pricing and delivery options.
• Granular SKU handling to distinguish between product variations accurately.
• Structured output formats that AI models can process efficiently.

A new approach to commerce data for AI

At the heart of this challenge is a fundamental mismatch: AI models are evolving rapidly, but the data pipelines feeding them remain stuck in the past. To bridge this gap, businesses must invest in infrastructure that bridges the dynamic nature of e-commerce with the precision required by AI systems.

This isn’t just about improving AI accuracy—it’s about building a foundation for the next generation of intelligent shopping experiences. Retailers that prioritize clean, real-time, and location-aware product data will not only enhance AI performance but also gain a competitive edge in an increasingly digital marketplace.

The future of commerce isn’t just about selling products—it’s about delivering the right information at the right time, every time.