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AI in Consumer Markets: How Your Data May Be Influencing Grocery Prices

In 2026, AI has moved beyond being a “buzzword” and is now rapidly shaping our day-to-day lives. It already influences what we watch, what we buy and how we work. Increasingly, it is extending into the retail sector and is set to influence how much consumers pay for groceries in physical stores.


Photo Source: Pricer Supermarkets and other retail stores are increasingly adopting AI-supported technology and pricing tools that allow prices to change frequently and dynamically, based on consumer data and behaviour.


This raises a simple but important question: What happens when grocery prices are no longer fixed? Who benefits and who might be at a disadvantage?


The Technology: What are Electronic Shelf Labels (ESL’s)?

ESLs, also known as Digital Shelf Labels (DSLs) are electronic price tags that are replacing traditional paper tags in stores. These digital displays update information in real time, allowing retailers to adjust prices dynamically and display additional information such as product ratings and allergen information, improving the overall shopping experience (Barnett, 2023). 


These systems rely on real-time data to determine pricing strategies, aimed at maximising efficiency and profitability. Instead of manually changing prices, retailers can now update them instantly based on:


  • demand levels

  • stock availability

  • competitor pricing

  • promotional strategies


When combined with AI systems, ESLs facilitate real-time price adjustments, moving retail markets closer to a dynamic pricing model where prices are increasingly responsive to demand, supply, and consumer behaviour.

For firms, this creates significant advantages. Prices can respond instantly to changes in market conditions, paper waste from paper tags reduces and labor costs are saved by reducing the time employees spend on changing price tags. However, it also increases the complexity and opacity of pricing systems for consumers.

Walmart, a major US retailer, has begun rolling out ESLs and plans to expand the technology across all US stores by the end of 2026 (Reuter, 2026). This signals a broader shift toward more automated and data-driven pricing systems, especially in large-scale retail markets.


The Economic Context

The adoption of AI is fundamentally changing how retailers approach pricing decisions.


Dynamic pricing and Algorithmic Pricing

Dynamic pricing is a price discrimination strategy where prices adjust in response to external factors, such as changes in demand, supply conditions, and market competition (Dublino, 2025). Dynamic pricing became prominent in the airline industry following deregulation in the late 20th century. Airfare prices began depending on how close to departure you booked your ticket, or how high demand was for a seat in that flight. The hotel industry followed, and now it has rolled out and expanded even into the concert ticketing industry. 


When combined with AI, the strategy extends into algorithmic pricing. Under this model, decisions are automated using algorithms that process large-scale datasets (Spann et al., 2025). These systems track general market factors such as demand, consumer purchasing behaviour, stock availability, and competitor pricing, allowing prices to be updated in real time without manual intervention.


Surveillance and Personalized Pricing 

Surveillance pricing takes algorithmic pricing a step further by incorporating consumer-specific data into pricing decisions. Rather than general, market-based trends, these systems allow firms to personalise prices based on consumer-specific behavioural and personal information, such as purchasing behaviour, location data, and estimated willingness to pay (Cornell Law School, 2026).

Retailers collect this information through loyalty programs, online accounts, mobile apps, and increasingly through in-store technologies such as cameras, sensors, and biometric systems. Some technologies are capable of generating demographic estimates, including approximate age and gender analysis, allowing firms to build detailed consumer profiles.

These systems provide retailers with large amounts of information that are influencing pricing strategies in increasingly personalised ways.


Information Asymmetry 

Through AI, algorithms and large-scale datasets, retailers have significantly more information about consumer behaviours than the average consumer. This imbalance in information is known as information asymmetry (Mohn, 2025). 


This creates an issue of pricing transparency, as consumers may not fully be aware of how prices are determined. As pricing systems become more data-driven, it becomes increasingly difficult for consumers to compare prices on equal terms or determine whether prices reflect market conditions or personalised adjustments. 


As a result, two people in the same store at the same time may be charged different prices due to factors beyond their knowledge or control. 


Real World Adoption and Ethical Concerns

In the United States, major retailers such as Kroger and Walmart have begun adopting Electronic Shelf Labels (ESLs), signalling early-stage implementation of AI-driven pricing systems in large-scale grocery markets. 


While dynamic pricing has been widely used in sectors such as travel and hospitality for decades, the technologies used to achieve surveillance pricing - facial recognition, surveillance through apps and loyalty schemes, the constant collection of data raises significant privacy concerns. Where should the line be drawn around consent, data ownership, and transparency in consumer markets? Furthermore, its expansion into essential goods raises further concern given rising cost of living pressures. This has  attracted regulatory attention, with unions and policymakers beginning to examine its implications for transparency and consumer fairness. 


In Australia, the discussion has taken a different form. Major retailers such as Coles Group and Woolworths Group have faced scrutiny over accusations of price gouging amidst rising cost-of-living pressures. This has contributed to growing public and policy debate around price transparency and the potential need for stronger protections against perceived price exploitation in essential goods markets (Seeto, 2026). While firms will no longer be able to charge “excessive” prices compared to its supply costs (James Purtill, 2026), dynamic pricing still remains a gray area, with it being legal in both regions. However, its application in essential retail markets has raised questions about where regulatory boundaries should be drawn. 


Ultimately, the shift toward AI-driven pricing represents a broader trade-off between efficiency and transparency in consumer markets. While these technologies may improve efficiency, responsiveness, sustainability and profitability for firms, they also create new concerns surrounding transparency, privacy, and consumer fairness (Spann et al., 2025). 

This begs an important question: when consumers’ ability to make informed economic choices is weakened by information asymmetry, what should fairness look like in increasingly automated consumer markets?


Until next time, 

WIF 💙 

Written by Tishya Anand


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