Walmart Says AI Merchandising Patents Shouldn’t Raise Dynamic Pricing Fears

eCommerce has introduced higher fulfillment and return costs, while supply chain volatility has increased inventory risk. At the same time, consumers have been conditioned by years of price competition and digital transparency to expect both low prices and frequent promotions.

This creates a structural tension. Retailers must offer value without sacrificing profitability, a challenge that becomes more acute at scale. And few retailers operate at a larger scale than Walmart, where even marginal improvements in pricing efficiency can translate into billions of dollars in impact.

On Wednesday (March 18), Walmart took aim at that challenge of offering greater value without slimming down profits by securing U.S. patents for two systems that would use machine learning to inform the company’s pricing,

The two patents are among almost 50 that Walmart has secured from the U.S. Patent and Trademark Office since January, per the report. The company has stressed that the patents, one of which is specific to markdowns while the other enables human-led decisioning, are “unrelated to dynamic pricing” and that the retailer “doesn’t participate in surge pricing.”

What the two patents do reveal, however, is that, increasingly, retail success is being determined not just by merchandising or scale, but by the ability to optimize complex systems using data. Pricing, inventory, logistics and customer engagement are becoming interconnected components of a broader optimization problem.

See also: Walmart Names New CEO as Retail Moves From Shelves to Software 

Retail Moves to ‘Algorithmic Merchandising’

Traditional retail pricing has often relied on historical patterns and human judgment, supported by periodic adjustments. By contrast, Walmart’s patented system appears designed to evaluate multiple variables simultaneously and optimize markdown timing and depth across different time horizons.

Algorithmic merchandising, in contrast to dynamic pricing, operates largely behind the scenes. It focuses on improving the quality of discounts rather than their visibility, aligning pricing decisions with operational realities rather than short-term demand spikes.

Instead of over-discounting to guarantee sell-through, retailers can use artificial intelligence (AI) tooling to calibrate markdowns more precisely, applying them where and when they are most effective. The result is smarter prices that can help achieve the same sales outcomes with less margin erosion.

See also: Amazon, Walmart Shift Retail Competition From Price to Technology 

Meeting Consumer Expectations Without Breaking Trust

Any discussion of AI-driven pricing must grapple with consumer perception. In recent years, the idea of algorithmically determined prices has drawn scrutiny, particularly when it resembles surge pricing or individualized offers that vary by user. Walmart’s framing of its technology is notable in this regard. The emphasis is on improving markdown decisions rather than introducing real-time price fluctuations tied to external factors like time of day or customer identity.

Markdown efficiency determines how much margin is lost in clearing inventory, how quickly products move through the system and how effectively retailers respond to changing demand. AI has the potential to transform this area from an art into a science.

Still, the algorithmic merchandizing initiative dovetails nicely with Walmart’s announcement earlier this month confirming a sweeping rollout of digital shelf labels across its roughly 5,200 stores in the United States by 2027.

The PYMNTS Intelligence and ACI Worldwide collaboration “Big Retail’s Innovation Mandate: Convenience and Personalization” found that 32% of grocers think consumers would be very or extremely likely to switch merchants if not given access to digital price tags or smart shelf tags.

At the same time, lawmakers fear that this technology would make it easier for grocery chains to use dynamic pricing, a strategy in which they could raise prices during times of high demand.

Walmart operates at the intersection of physical and digital retail, where price consistency remains more visible and more sensitive. Its investment in predictive markdown optimization suggests a different path than that of dynamic pricing: one that prioritizes planning over reaction, and operational efficiency over price volatility.