4 Myths About AI Pricing — and What Retailers (and Shoppers) Need to Know

From metaverse hype to “retail apocalypse” doom-saying, the media sometimes misses the mark when reporting on retail shifts. Today, we are seeing that pattern repeat, with headlines spreading inaccurate information about AI pricing and electronic shelf labels (ESLs).

Gartner analyst Sandeep Unni captured this frustration perfectly in a recent LinkedIn post, noting that “the ‘Dynamic Pricing’ panic is almost entirely hyperbole” and that “this media narrative that digital price tags are a corporate conspiracy to pickpocket customers in real time is peak fearmongering overtaking basic retail economics.”

And, of course, he’s right. Hardly a week goes by without a headline warning shoppers about the ills of AI pricing or without news of proposed legislation seeking to limit the technology retailers use to optimize (AI) or execute (ESLs) pricing decisions.

As someone who has spent over two decades in retail — with much of that time focused on merchandising and pricing strategies — I’ve been shocked by the disconnect between what’s actually happening in retail and what’s being reported in the news.

The reality is that shoppers are more price- and value-conscious than they have been in recent history. Any retailer that fails to adjust their strategies to account for that — particularly in the fast-moving consumer goods space — will cede ground to competitors at a rapid rate.

AI-powered pricing isn’t a tool designed to extract every last cent out of shoppers. When deployed strategically, it is one of the single most effective ways retailers can deliver better value, build trust and stay competitive.

Let’s dismantle four of the biggest myths surrounding AI pricing right now.

Myth 1: AI Price Optimization Is Bad for Consumers

The Reality: AI helps retailers lower prices on the items shoppers care about most.

Consumers are more price-sensitive today than they have been in years. To remain competitive, retailers need advanced tools that allow them to offer the most attractive pricing possible.

At its core, AI price optimization works by analyzing massive volumes of sales and market data to pinpoint a retailer’s key value items — the specific products that drive a customer’s perception of value. Once they are identified, AI recommends lowering prices and deepening promotions on those exact items.

The retailers winning market share, building price perception and earning customer trust across global markets are using Revionics to do precisely this. Conversely, retailers clinging to manual or legacy pricing methods struggle to know where to lower prices effectively. As a result, they risk losing both customer loyalty and market share because their prices fail to resonate.

Myth 2: AI Pricing Is Driving Inflation and the Cost of Living Crisis

The Reality: AI gives retailers the leverage to push back against rising supplier costs.

Wholesale price increases have been driven by genuine macro pressures: labor shifts, raw material inflation, geopolitical conflicts and supply chain disruptions. Unfortunately, retailers haven’t always done a great job of communicating that higher prices stem directly from the rising costs charged by suppliers, not from an opportunistic retailer profit grab. In fact, multiple industry sources have shown that businesses absorbed the bulk of tariff costs in 2025 rather than passing them on to consumers.

Far from stoking inflation, sophisticated AI analytics enable retailers to push back on vendor cost increases. Rather than blindly accepting higher wholesale prices and passing them on to shoppers, retailers can use AI to model different scenarios and negotiate win-win-win pricing structures that protect shoppers, preserve retailer margins and keep vendors accountable.

Myth 3: Dynamic Pricing, AI Pricing and Surveillance Pricing Are All the Same

The Reality: They are fundamentally different concepts — and AI pricing does not invade consumer privacy.

These three terms are frequently conflated, but they describe entirely different mechanisms:

• Dynamic pricing: Refers to the cadence and speed of price adjustments.
• AI pricing: Uses advanced data science to analyze aggregate demand and recommend optimal prices based on product, channel and market conditions.
• Surveillance pricing: Uses individual consumer data to alter prices for specific individuals.

To be clear: Revionics does not develop software that recommends pricing targeted to specific individuals, and we do not use individual consumer data.

We do, however, lead the market in AI price optimization, which relies on market-level factors when recommending optimal prices to retailers. These factors include competitive prices and historical sales data.

Shoppers vote with their wallets, and those collective demand signals inform localized pricing strategies that benefit both consumers and retailers. Ultimately, Revionics clients leverage science to find prices that are most likely to win customer loyalty in every market where they operate. This is a key reason why retailers using Revionics are known price perception leaders that continue to grow share.

Myth 4: Dynamic Pricing and ESLs Are Designed to Harm Shoppers

The Reality: Dynamic pricing and ESLs are most frequently used to drop prices, not raise them.

Consumer skepticism around dynamic pricing is understandable — surge pricing for concert tickets and rideshares has left a bad taste in people’s mouths. But in physical retail, the primary use case for dynamically adjusting prices is completely different.

ESLs give retailers operational speed, accuracy and labor savings. More importantly, they give stores the agility to lower prices instantly when market conditions change or competitors drop prices. Retailers also use ESLs to execute timely markdowns on perishable items near their expiration date, clear out seasonal inventory or push steeper promotional discounts.

Look around at the global retailers leading the rollout of ESLs and dynamic pricing. They are almost universally the brands known for having the lowest, most aggressive pricing in the market.

The Bottom Line: Value Wins in an Open Market

Retail is not a closed market. Consumers have endless choices, real-time access to comparison tools and now large language models that make instant price comparison effortless.

In this hyper-transparent environment, arbitrarily raising prices isn’t just bad strategy — it’s a recipe for rapid market share loss.

AI pricing has never been about gouging the consumer; it’s about giving retailers the intelligence and agility needed to deliver maximum value where it matters most. Those that leverage these tools will earn long-term customer trust. Those that don’t will simply be left behind.