Omnichannel
The hidden retail challenge no one talks about and how AI solves it
Without trusted, AI-enhanced product data, even the most advanced omnichannel strategies risk failure.
October 15, 2025 by Vandana Singal — Vice President – PDES, Happiest Minds Technologies
Retailers today compete on customer experience, personalization, and omnichannel engagement. From smooth checkouts to highly tailored recommendations, much of the innovation focuses on what customers see and interact with.
However, behind this shiny front end lies a silent challenge that often goes unnoticed: fragmented, inconsistent product data.
This issue rarely comes up in boardrooms, yet it has significant consequences such as long delays in launching products, increased return rates, compliance risks, and a loss of customer trust.
The silent challenge: Fragmented product data
Product data is essential for retail operations. Every product listing, digital ad, supply chain decision and customer interaction relies on its accuracy. Yet most retailers face several issues like:
- Inconsistent product attributes across marketplaces, apps, and stores.
- Slow time-to-market because of manual catalog updates.
- High product returns due to inaccurate product descriptions or missing details.
- Delays in compliance, especially for global retailers dealing with different regulations.
Industry insight: According to a survey by Loqate, 41% of businesses reported poor physical address data leads to operational challenges. The study also noted 41% of deliveries are delayed and 39% completely fail when the data are inaccurate or incomplete.
In short, fragmented product data is more than just a back-end issue. It is a critical business challenge.
Why traditional fixes fall short?
Retailers often think that ERP, CRM, or e-commerce platforms can fix their product data issues. However, these platforms were designed for transactions and customer management, not on managing and improving data.
Common limitations:
- ERP systems manage inventory but don't standardize or enrich product attributes.
- E-commerce platforms provide great storefront experiences but do not control the data that supports those experiences.
- Teams working manually to fill gaps only add more costs, delays, and mistakes.
Case Example – Walmart
During its Q2 2025 earnings call, Walmart executives revealed the company is leveraging generative AI to improve the data quality of its product catalog. This initiative aims to deliver better customer experiences and enhance efficiency for store associates.
AI as the game-changer for retail product data
AI transforms retail product data management from manual operations to smart, scalable systems. An AI-driven Product Information Management helps retailers centralize, optimize, and enrich data. The solution makes it easier for retailers to maintain product data accuracy, speeds up time to market and improves customer experience.
How AI helps:
- Automated data cleansing and enrichment: AI can standardize attributes, find duplicates and automatically fill in missing details.
- Fill product content gaps: Instead of leaving empty fields, AI generates relevant product descriptions, specifications, and attributes. This results in a full, engaging catalog that boosts searchability and conversions.
- Image recognition for cataloging: AI tags images and videos, allowing retailers to launch SKUs, quickly and consistency.
- Regulatory compliance: AI validates product descriptions against local standards (e.g., FDA for food, CE for electronics).
- Predictive attribute insights: AI forecasts which product attributes are most important in specific categories and markets.
Case Example – Zara
Zara integrated AI-driven catalog enrichment into its fast-fashion cycle. Instead of weeks of manual data entry, collections are digitized, tagged, and launched within days—helping Zara maintain its agility and trend responsiveness. Read here.
Business impact: From fragmented experience to customer trust
AI's impact goes beyond efficiency. It directly drives profitability and loyalty.
- Faster market entry: A consumer electronics retailer reduced its product launch cycle from 21 days to 5 days with AI-enabled cataloging.
- Lower return rates: Retailers that adopted AI-driven validation cut return rates by up to 20% (RSR, 2024).
- Consistent omnichannel experience: Accurate data ensures customers see the same product attributes, whether browsing on Amazon, a brand app, or in-store kiosks.
According to McKinsey, mentions of artificial intelligence in retailers' earnings calls surged last year. This reflects the growing adoption of GenAI's in retail. It is projected to unlock $240 billion to $390 billion in economic value for the retail sector, which is about a margin increase of 1.2 to 1.9 percentage points across the industry.
How retail leaders should respond?
For retail industry leaders, solving this challenge requires strategic action:
- Recognize data fragmentation as a business issue, not an IT problem.
- Invest in AI-enabled PIM platforms that unify, enrich, and validate product information.
- Incorporate data quality into customer experience metrics, as customers associate accurate product information with brand reliability.
- Pilot AI in high-value categories like fashion, electronics, beauty, then scale enterprise-wide.
Case Example – Amazon
Amazon is using GenAI to enhance personalization across its marketplace. By analyzing customer shopping behavior, the company generates customized product recommendations and more relevant product descriptions. This not only makes the shopping experience better but also strengthens customer trust by delivering experiences that feel unique to everyone.
Conclusion: The challenge that can no longer be ignored
Retail is evolving rapidly, but customer experience cannot outpace data accuracy.
Without trusted, AI-enhanced product data, even the most advanced omnichannel strategies risk failure.
Retailers that thrive in the next decade will be those who:
- See product data as a strategic asset.
- Use AI not just for customer-facing experiences but also to maintain back-end accuracy.
- Build trust on a large scale by ensuring accuracy across every channel and market.
With the challenge now in focus, GenAI shows the path ahead. It enables retailers to transform product data into a source of efficiency, trust and growth.
About Vandana Singal
Vandana Singal is the Vice President and PDES Pre-Sales Head at Happiest Minds Technologies, based in Noida, with 23 years of experience in bridging the gap between technology and business needs. She has deep expertise in Pimcore solutions and implementation and is skilled in crafting compelling proposals, delivering impactful product demonstrations, and providing strategic consulting to drive client success. Vandana has a proven record of collaborating with sales teams to secure new business opportunities while ensuring clients receive tailored, value-driven solutions.
