The retail industry is at a turning point with automation and AI. For many retailers, the goal is simple: use technology to improve efficiency and effectiveness, whether for store associates, developers, customers, or suppliers. While industry giants like Walmart are setting the pace, the core principles of successful retail innovation apply to stores of all sizes.
“When everyone has AI, competitive advantage vanishes. True potency lies in how you secure, differentiate, and trust your data, processes, and people.” — Ananda Chakravarty, VP Research, IDC Retail Insights
The scale of innovation
Walmart currently holds a unique position as a “blue chip” benchmark for retail innovation. Its success is driven by a massive physical presence—providing abundant testing grounds for new technologies—and an executive team that combines deep technical expertise with retail operations know-how. Furthermore, their worldwide network and influence over supplier relationships give them capabilities that typical retailers cannot easily match. They aren’t just doing retail; they are also expanding into adjacent areas like retail media and advertising.
Same Models, Same Data, and Same Results: Why Retail AI Needs Competitive Potency:Companies such as Sephora, Kroger, Tesco, The Home Depot, and Dollar General have all witnessed the benefits of AI. But widespread adoption erodes competitive advantage. IDC examines how retailers can build “competitive potency” to grow faster and capture more market share.
The automation imperative
For every retailer today, automation is top-of-mind. It is no longer a question of if but how to implement technology to streamline store operations. The challenge for most is moving from historical, anecdotal data to predictive, real-time insights.
Retailers are grappling with a “data problem”, one that includes ensuring collection and use of the data appropriately to make decisions. They must collect and make sense of large amounts of scattered information—from product and demographic data to sales metrics and demand forecasting. Updating these systems to support better, long-term decision-making is the groundwork for moving forward. While cost savings are a common promise of AI, the reality for retailers is a mix of ongoing investment and changing expenses as they bring in newer, more powerful tools.
AI has proven measurable ROI in retail—but the gains it brings are just table stakes. Winning in the next wave requires building true competitive potency through differentiated data, proprietary processes, and trusted people.
Win-Win Product Discovery — A Retail Agentic AI Use Case: IDC dissects how agentic AI is transforming product search and discovery across online retail channels. The next generation of solutions will use multilevel and orchestration agents to align customer demand with retailer assortment plans—turning product discovery into a competitive advantage.
Key technologies: Vision and labels
Several technologies are moving from pilot programs into everyday use:
- Computer Vision: Widely used by point-of-sale (POS) and fraud avoidance vendors, this technology is increasingly common for product identification, age verification, and fraud detection. While Walmart’s implementation of self-checkouts for fresh produce is notable, the capability itself is well-established in the market, just not mainstream.
- Digital Shelf Labels (DSLs): These allow for flexible pricing, significantly reducing the manual labor required to update store tags and category resets. While they offer real advantages in speed and labor savings, they represent a significant financial and logistical challenge regarding hardware, maintenance, and the complexity of rolling out across thousands of stores with tens of thousands of individual products.
IDC ProductScape: Worldwide Retail Price Optimization and Management Solutions, 2025–2026: A comprehensive guide to key functionalities across 20 retail price optimization vendors—from Blue Yonder and Oracle to RELEX and Revionics. Essential reading for retailers evaluating flexible pricing tools and digital shelf label strategies.
Strategic advice for smaller retailers
Smaller retailers should avoid the trap of simply trying to copy the giants. Walmart often runs experimental programs that they may discontinue, such as their 5-year experiment with Bossa Nova for tracking on-shelf facings and inventory using gliding robots and cameras in the store. For a smaller retailer, the margin for error is much tighter and programs must translate into real ROI.
Instead, prioritize:
- Foundational Operations: Keep your data clean and your inventory counts accurate. If you don’t know where your stock is, or your inventory records are unreliable, advanced AI won’t solve the core issue.
- Strategic Selection: Identify which technologies deliver the greatest, most lasting financial impact for your specific business. Don’t chase trends; focus on fixing real problems in your day-to-day operations.
- Partnering: Smaller retailers don’t need to build everything in-house. Work with software vendors or technology partners to put proven solutions in place that fit your budget and store size.
THREE PILLARS FOR RETAIL AI SUCCESS
1. FOUNDATION Clean data, accurate inventory, reliable systems
2. SELECTION Pick tools that solve your problems, not the market’s
3. PARTNERSHIP Leverage proven vendor solutions, scale smart
Ultimately, the most successful retailers will be those who master the basics—clean, orderly, engaging stores—and use technology to enhance, rather than replace, that human experience. Technology is a powerful tool, but it cannot fix a fundamentally broken retail operation. Success lies in identifying the unique value of your store and applying the right tools to build on that strength.
Want to go deeper?
Explore the full IDC research behind this blog