"Every vertical has its own unique technical challenges. In retail, for instance, incorrect or incomplete product data has for decades plagued the world's largest retailers. Here are some of the retail nightmares faulty product data has caused: Retailers have tried and failed to alert shoppers to recalls of products they recently purchased. Retail sites and mobile apps constantly tell shoppers that an item is available for purchase when it isn't. Retailers can't alert customers when a product they searched for two months ago becomes available. Apps tell customers the location of a product in a brick-and-mortar store, but the item is nowhere near that location."
"Much of this stems from unreliable data from retail suppliers, such as Procter & Gamble, Unilever, Colgate-Palmolive, Johnson & Johnson, Kraft, Mars, General Mills, and thousands of others across the planet. Unreliable data is a broad category that includes incorrect information, but also data placed in the wrong field and data that is materially incomplete or not included at all. Have you ever visited a grocery site and clicked on the "ingredients" button and were only shown a nutrition chart with no ingredients listed at all? That is an example of data placed in the wrong field or cell."
"Various genAI vendors - with Google arguably taking the lead - say that genAI and agentic AI may be able to improve the product data situation, in much the same way that agentic may dramatically change ecommerce interactions. Could an army of software agents that tirelessly scout out and correct faulty data be the solution? The implications stretch far beyond retail. Observing how agentic AI can address specific technical challenges in retail may show IT leaders in a variety of industries how AI might help them fix their own tech head"
Unreliable product data causes retailers to fail to notify shoppers about recalls, to misrepresent item availability, to miss re-stock alerts, and to mislocate items in stores. The root cause often lies with suppliers providing incorrect, misplaced, or incomplete information. Examples include ingredients listed as a nutrition chart or missing entirely. Generative and agentic AI are proposed as tools to discover, correct, and enrich product data at scale. An automated fleet of software agents could continuously scout for faults and fix records. Lessons from retail implementations could guide IT leaders in other verticals facing domain-specific data issues.
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