For decades, the core playbook for retail dominance was straightforward: undercut the competition, broadcast the discount, and clear the inventory. But as cautious consumers increasingly scrutinize promotional deals, the traditional race to the bottom on price is hitting a wall of skepticism.
Recent industry data from product experience platform Akeneo reveals a dramatic shift in consumer behavior: 62% of shoppers now cross-check prices across multiple channels before purchasing, while only 9% trust a promotional deal at face value.
More importantly, the mechanism used to verify these deals has fundamentally changed. Shoppers are no longer manually clicking through a dozen browser tabs. Instead, 43% of consumers actively utilize AI tools like ChatGPT or Gemini to evaluate products and navigate discounts, and 22% have already made direct purchases based on AI recommendations.
This marks the arrival of Agentic Commerce—an operational shift where artificial intelligence sits directly between the consumer’s intent and the retailer’s checkout page. In this environment, price may earn a shopper’s initial attention, but complete, machine-readable product information is what earns the AI’s recommendation and secures the sale.
How Search Is Evolving: From Keyword Matching to AI-Driven Audits
The fundamental difference between traditional search engines and generative AI shopping assistants lies in how data is processed. Traditional search algorithms index keywords to match queries with web pages, prioritizing the old model: Broad Promotion to Search Engine to Retailer Landing Page to Price Comparison to Purchase.
Generative LLMs and autonomous agents, by contrast, evaluate context, suitability, and data completeness, creating an entirely new loop: Consumer Intent to AI Agent Spec Audit to Automated Compatibility Check to Machine Shortlist to Instant Checkout.
When a consumer asks an AI assistant for a product recommendation—such as a specific laptop, home appliance, or fashion item—the model does not simply sort results by the lowest numerical price. It conducts a multi-variable audit across several non-price attributes. It checks dimensional precision and compatibility to determine whether the item meets explicit physical specifications or ecosystem constraints, such as cabinet height or operating system support. It audits warranty terms and included accessories to verify if a $500 offer includes essential cables and a 2-year warranty, or if a $520 competitor offer is actually cheaper once bundled accessories are accounted for. It also conducts sentiment verification, assessing whether recent customer reviews confirm product durability or point to defects in the current production batch.
If a retailer’s product page lacks structured data for these critical attributes, the AI assistant cannot verify the deal’s true value. As a result, the product is quietly omitted from the AI’s shortlist—regardless of how deep the price discount might be.
Global & Regional Retailers in Action: Deploying AI for Product Truth
Retail leaders around the world—and across the Middle East—are restructuring their digital systems to ensure their product information is accurate, structured, and instantly readable by AI agents.
On a global scale, Amazon and Shopify are driving agentic personalization and discovery. Both platforms have integrated conversational AI engines (such as Amazon’s Rufus) that ingest structured product specifications, customer Q&As, and review metadata in real time. Rather than relying on simple keyword matches, these engines evaluate precise product attributes to guide shoppers to high-confidence purchases.
At Newegg, the electronics retailer integrated generative AI engines directly into its product management workflows. By using structured AI models to standardize technical specifications, refine SEO metadata, and eliminate missing attribute fields across thousands of complex SKUs, Newegg improved search visibility, email engagement, and customer conversion rates. Sephora utilizes visual and attribute precision through its AI-driven recommendations and Virtual Artist ecosystem, matching skin tones and ingredient formulations while relying on standardized product data behind the scenes to ensure recommended routines reflect physical inventory.
In the MENA region, leaders are navigating complex multilingual and multi-channel environments. Majid Al Futtaim (MAF), operating across hypermarkets, lifestyle brands, and shopping destinations, has prioritized unified data integration across its digital ecosystem.
By maintaining a single source of truth for product pricing, localized Arabic/English descriptions, and live inventory levels across its Carrefour and lifestyle platforms, MAF ensures that external search engines and internal conversational interfaces return accurate, trustworthy details. Similarly, Landmark Group and regional e-commerce marketplaces are increasingly investing in AI-enabled Product Information Management (PIM) architectures. In a market where cross-border fulfillment, varying regional warranties, and multi-currency pricing are standard, AI tools are used to automatically translate, enrich, and standardize product listings, ensuring consistent data across web apps and regional marketplace channels.
The Role of Service Providers and the Agentic Ecosystem
To support agentic commerce at scale, enterprise technology providers are transforming traditional data infrastructure into active, machine-readable interfaces.
Product Information Cloud Platforms, such as Akeneo, Syndigo, and Pimcore, have evolved from static databases into dynamic engines that feed structured product data directly to AI agents. By centralizing technical attributes, media assets, warranty documentation, and localized channel variations, these platforms act as the primary “single source of truth.”
Simultaneously, Cloud Infrastructure and AI Protocol providers—like AWS, Google Cloud, IBM, and Kore.ai—are building specialized agentic middleware. These include emerging Agentic Commerce Protocols (ACP) and delegated payment mechanisms like Google’s Agent Payments Protocol or Visa’s tokenized AI credentials. These frameworks allow AI agents to securely query a merchant’s inventory APIs, verify warranty details, execute price-matching logic, and complete transactions on behalf of the user, creating a vertical stack from the Conversational UI down through Protocol APIs, PIM Platforms, and core ERP systems.
Deal Events as Stress Tests: Why Inconsistent Data Kills Conversions
During high-volume promotional periods—such as Amazon Prime Day, White Friday, or seasonal sales—the operational strain on product data increases dramatically. Prices fluctuate hourly, bundles are created dynamically, and inventory turns over rapidly across channels.
When product data is not synchronized across all endpoints, cracks appear immediately. An AI shopping agent might cite a product specification or promotional price that became outdated two hours ago. A promotional bundle might fail to explicitly state whether a required accessory is included. An item might show as “In Stock” on a brand’s primary website but “Sold Out” on an integrated marketplace feed, or customer reviews might reference an older hardware generation, confusing both human buyers and AI sentiment parsers.
None of these errors are catastrophic on their own, but during a major shopping event, incomplete or conflicting product information transforms urgency into uncertainty. When faced with uncertainty, shoppers do not take a risk—they step away to double-check the deal or buy from a competitor whose offer is clearly defined.
Generative Engine Optimization (GEO): Competing on Information Quality
As AI agents become a primary discovery tool, retail strategies must expand beyond traditional Search Engine Optimization (SEO) to embrace Generative Engine Optimization (GEO).
While traditional SEO focuses on driving human web traffic through keywords and backlinks to achieve a high SERP rank, GEO focuses on structuring product information so that LLMs and autonomous AI shopping agents can easily parse, verify, and recommend it.
To win under GEO rules, product data must be machine-readable, utilizing clear schema markup for materials, dimensions, power requirements, compatibility, and warranty terms. It must be contextually enriched, including precise usage scenarios, safety certifications, and verified customer Q&A data. Finally, it must be real-time synchronized, exposing direct APIs for pricing, stock availability, and shipping promises so AI agents can validate transactions instantly.
Source: Akeneo Global Consumer Research (Sept 2026): AI Deal-Hunting and Product Experience Benchmark Study. IBM Thought Leadership (2026): Understanding Agentic Commerce Protocols, Delegated Authentication, and Merchant APIs. Kore.ai Enterprise Retail Disclosures (2026): AI Agents in Retail: Real-World Use Cases, Operational ROI, and Inventory Forecasting. IDC Asia/Pacific Retail Forecasts: Generative AI Adoption in Product Content Creation and PIM Systems.
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