Many big retailers have long struggled with unreliable product data. From incorrect details to missing information, faulty data causes real headaches. Retailers often can’t alert customers about recalls or stock updates because the information isn’t accurate or complete enough. This leads to frustrated shoppers and missed opportunities.

The Retail Data Dilemma

Retailers source product data from suppliers like Procter & Gamble, Unilever, and others worldwide. But this data isn’t always trustworthy. Experts estimate that supplier data accuracy hovers between 60% and 80%. Some say it’s even lower, around 50% or less. The problem isn’t just wrong info; it’s also data in the wrong places or incomplete details. For example, a grocery website might show nutrition info but no ingredients, simply because the data was entered in the wrong field.

Can AI Improve Data Quality?

Now, companies are looking at artificial intelligence, especially agentic AI, to clean up this mess. These AI systems can analyze supplier data, flag errors, and even correct some mistakes automatically. Google is among the leaders exploring this approach. The idea is to have AI evaluate what’s missing or doesn’t make sense, then notify suppliers to fix it. Over time, the AI could learn to correct errors without human help, speeding up the process significantly.

Reaching Higher Data Standards

But how good does the data need to be? Retailers want near-perfect accuracy—around 90% to 99%. That’s a big jump from current levels. Achieving this would mean fewer out-of-stock items, more accurate product info, and better customer trust. Some experts believe even a small improvement in data accuracy can make a big difference. If AI can help suppliers improve their accuracy, retailers might finally get the reliable data they need.

Beyond Retail: Broader Impact of AI Data Cleanup

The benefits of better data go beyond retail. Other industries like healthcare or manufacturing face similar challenges. AI’s ability to spot errors and fill gaps can help in any field where data quality matters. For example, in healthcare, accurate ingredient or medication info can be critical for safety.

The Future of Product Data and QR Codes

One exciting possibility is moving from barcodes to QR codes for product identification. QR codes can store much more data, including detailed info and links to online resources. But this shift depends on having reliable supplier data. If AI can ensure that product info is accurate, QR codes could become the new standard, offering richer details and better tracking.

Challenges and Opportunities

Of course, implementing AI solutions isn’t without hurdles. Suppliers might need to dedicate staff to work with the AI tools. There are also questions about how much investment is needed and whether suppliers will cooperate fully. Still, even incremental improvements in data accuracy can reduce manual work and improve the shopping experience.

In the end, AI has the potential to transform how retailers manage product data. Better data means fewer errors, happier shoppers, and more efficient operations. As AI systems get smarter, we might see a future where faulty product info is a thing of the past. That could make online shopping more reliable and stores more responsive to customer needs.

Inspired by

• https://www.computerworld.com/article/4074007/ai-agents-might-smooth-some-of-retails-worst-data-problems.html

Sources

• gmpg.org
• moorinsightsstrategy.com
• everestgrp.com
• forbes.com
• aifi.com