How AI-Powered Pricing Tests the Line Between Surveillance and Fairness

Key takeaways: AI-powered pricing is now essential for retailers to stay competitive and respond quickly to market changes, according to Matthew Pavich, Senior Director of Strategy & Innovation at Revionics. Proactive pricing strategies, supported by analytics, help retailers optimize prices and drive both sales and margins.

There’s a critical difference between surveillance pricing and dynamic pricing. Building consumer trust requires transparent pricing practices and strong guardrails to ensure fairness. Between tariff swings, inflation and a new generation of AI-powered deal hunters, pricing has become one of the most complex factors in retail strategy.

Matthew Pavich, Senior Director of Strategy & Innovation at Revionics, shared how retailers are navigating that complexity — and also what it takes to deploy AI-driven pricing without losing consumer trust.

From Disruption to Necessity Pavich, who spent more than a decade as a buyer and in strategic merchandising roles at Target before joining Revionics, said the shift toward AI pricing tools has been building for years.

Revionics itself has been in the space for more than 20 years, introducing machine learning into pricing algorithms before the technology had a mainstream name. But the last five to six years changed the calculus. “We’ve seen so much disruption,” Pavich said on this week’s episode of Retail Remix.

“COVID was obviously a massive disruption and it set off a chain of events, running from [the pandemic] to some of the supply chain challenges from Suez, and then we had inflation and then, going straight from inflation, we’ve had tariffs, and now we’ve had conflict thrown into that.”

That relentless cycle of disruption — paired with accelerating technology investment from competitors — has pushed AI pricing from a competitive advantage to a baseline requirement. “It’s a must-have in today’s world if you’re a sophisticated retailer trying to win share,” Pavich said. Proactive vs.

Reactive Pricing One of the key distinctions Pavich drew was between reactive and proactive pricing strategies. Traditional retail, he said, defaults to reaction: a cost increase arrives, prices go up; a competitor drops a price, you follow. The best practice, according to Pavich, flips that ratio. “The healthier mix is definitely more proactive,” he said.

“It’s important to get in front of your strategies and really understand what items my customers care most about. How can I lower prices on them proactively?

“ With the right analytics and optimization tools, retailers can model price changes before any external event forces their hand — and in many cases, find a path to lower prices, stronger volume and improved margins simultaneously. Reactive capability still matters, he noted, adding that retailers need to move quickly when market conditions shift.

But the goal is a pricing operation that plans ahead while retaining the speed to respond. The Consumer AI Factor The urgency around pricing agility has intensified as consumers increasingly use AI tools to comparison shop.

Pavich cited a statistic that he said every retailer should know: during the most recent holiday season, there was an 805% increase in customers using AI to shop and compare prices online. “Consumers are now using some of the most advanced technology in the world in real time to compare your pricing versus competitors,” he said.

“They can do this very simply. [Going beyond] comparing price on a single item, they can ask a simple question: who has a better price, Retailer A or Retailer B?” The implication is clear: if consumers have sophisticated tools and competitors are deploying AI-driven pricing, retailers that don’t invest face a structural disadvantage.

“You need to come to battle armed with the best analytics, the best solutions, the best AI to ensure that you’re always offering competitive, really good prices in the market for consumers,” Pavich said. Separating Surveillance Pricing from Dynamic Pricing Consumer concern around AI pricing has grown alongside its adoption.

Pavich addressed what he called the elephant in the room: the wave of negative press around surveillance pricing and dynamic pricing — two terms that he said are frequently conflated, but describe very different practices. Surveillance pricing, as Pavich defined it, refers to how a retailer gathers data to make pricing decisions.

The problematic version uses personal shopper information to charge different individuals different prices for the same item. “This is information I know about Matt or Nicole and I’m going to charge them or treat them differently,” he said. “Most consumers are rightfully concerned about that.”

The alternative — and the approach Pavich said responsible retailers use — is aggregated behavioral data. Every purchase decision a customer makes functions as a vote. That data is pooled at the store or channel level to identify which products matter most to shoppers, without ever tracking individuals. “It’s not