With the rise of AI and automation, technology has become a tool for sophisticated pricing strategies and operating efficiency for large food retailers. New technologies are designed with the goal of improving energy efficiency, food waste reduction, product cost, consumer prices or profit margins for retailers. They argue that new technologies allow food retailers to be more environmentally friendly and able to keep food prices lower for consumers. Critics argue that many new AI driven technologies are for the companies to increase profits at the expense of the consumer. Today we will examine some of the new technologies in retail food, what they do, the arguments for their potential positive impacts for consumers and the arguments for potential negative impacts for consumers.
- Dynamic Pricing and Electronic Shelf Labels (ESL)
Dynamic pricing is the practice of adjusting food prices in real-time based on fluctuating supply, demand, and inventory levels. This is enabled by Electronic Shelf Labels (ESL), which allow stores to change prices across the entire shelf in seconds.
Arguments for ESL: The primary argument for dynamic pricing is its ability to reduce food waste through discounting food that would otherwise expire. AI-driven systems can identify products nearing their expiration dates and automatically drop prices to ensure they are sold rather than discarded. Proponents argue that by recapturing the value of food that would have been wasted, retailers can maintain lower base prices for all customers. Additionally, removing the manual labor and cost required to handle and dispose of expired food can reduce overall costs.
Arguments against ESL: Critics point to surge pricing and surveillance pricing as significant risks. In 2026, legislation in states like Maryland has sought to ban these practices, arguing that algorithms can identify peak shopping hours and nudge prices upward when demand is highest. There is also the concern of personalized pricing, where loyalty data is used to set different prices for different individuals, potentially leading to higher costs for shoppers who the algorithm identifies as less price-sensitive.
- Micro-Fulfillment Centers (MFC)
Micro-fulfillment centers are small, automated warehouses located in the back of existing grocery stores or other facilities. They use high-speed robotics to pick and pack online orders.
Arguments for MFC: The argument for MFC’s centers on the cost of fulfilling orders. Traditionally, manual picking for an online order costs a retailer approximately $10 to $15 in labor. MFCs reduce this cost by roughly 75%, bringing the expense-per-order down to under $2. This allows retailers to offer pickup and delivery services at prices that are competitive with, or even identical to, in-store shopping, effectively lowering the total cost of convenience for the consumer.
Arguments against MFC: The counter-argument focuses on the massive capital expenditure required to install these robotic systems, which can cost millions per location. Critics suggest that retailers may increase shelf prices for all shoppers including those who do not use online services to subsidize the cost of the technology. Additionally, MFCs often lead to a rationalization of inventory, where only high-turnover items are stocked. This can drive up the price of niche or specialty goods as they are pushed out of the automated system and into more expensive distribution channels. Additionally, the potential impact on job loss from automation can cause people to have less ability to purchase food in general.
- AI-Driven Demand Forecasting
Retailers are now using sophisticated neural networks to ingest data points like local weather, social media trends, and regional events to predict inventory needs with high precision.
Arguments for AI-Driven Demand Forecasting: Proponents say AI forecasting model accuracy can reduce out-of-stock items by up to 65% and lower store-level waste by 35% to 80%. This reduction in food waste would reduce environmental impacts and allow for a direct reduction in the retail price of food. When a store knows exactly how many gallons of milk it will sell, it avoids the cost of over-ordering, which keeps the price of staples stable.
Arguments against AI-Driven Demand Forecasting: The main argument against AI forecasting is algorithms are often programmed to maximize gross margin rather than lowest price. If an AI identifies that a person or group of people have a preference for or history of buying a specific item, it may choose not to offer promotions on that item, effectively keeping the price higher than it would be in a traditional competitive market. There is also the risk the AI ignores lower-cost alternative brands because they don’t have enough historical data to fit the model, or simply because there is a higher profit margin on a different item.
- Computer Vision and Frictionless Checkout
Computer vision systems use cameras and sensors to track what shoppers pick up, allowing them to exit the store without stopping at a traditional checkout lane.
Arguments for Computer Vision and Frictionless Checkout: Retailers argue that “shrinkage” from theft, which costs the industry over $130 billion annually, is a primary driver of price hikes. Frictionless systems virtually eliminate traditional shoplifting and other theft. By reducing these losses, retailers can maintain lower margins. Additionally, the reduction in cashier labor is a major cost saver that can prevent price spikes in regions with high minimum wages.
Arguments against Computer Vision and Frictionless Checkout: The hardware required for these systems is among the most expensive in the industry. Opponents argue that the return on investment for these systems is often five to seven years, meaning consumers may see higher prices in the short term as the store pays off the technology. Furthermore, the removal of human cashiers can lead to upselling through digital screens, which can subtly increase the average transaction value.
- Blockchain for Supply Chain Traceability
Blockchain provides a digital, immutable ledger that tracks a food product’s journey from the farm through processing and into the store.
Arguments for Blockchain for Supply Chain: Blockchain’s most direct impact on price occurs during food safety events. Historically, a single case of E. coli would force a retailer to dump their entire national supply of an item, leading to artificial scarcity and massive price spikes. Blockchain allows for a precise recall, identifying only the specific affected batches. By preventing the loss of safe food, blockchain maintains a steady supply and prevents the phenomenon of panic pricing that often follows a recall.
Arguments against Blockchain for Supply Chain: Critics point to the compliance cost for small-scale farmers and suppliers. The cost of digitizing their operations and joining a blockchain network can be prohibitive. If only large, corporate farms can afford to participate, it can lead to market consolidation. This lack of competition among suppliers can eventually lead to higher prices at the retail level as smaller, more price-competitive growers are squeezed out of the supply chain.
- Retail Vertical Farming and Controlled Environment Agriculture (CEA)
Vertical farms grow produce in climate-controlled indoor environments, often located within or near major cities.
Arguments for Retail Vertical Farming and CEA: The argument for vertical farming is price stability. Because these farms are immune to droughts, floods, and seasonal shifts, they provide a consistent supply regardless of external conditions. This removes the price volatility that causes consumers to pay higher premiums when a storm in California or a freeze in Mexico causes the price of lettuce or berries to double overnight.
Arguments against Retail Vertical Farming and CEA: Vertical farms are highly dependent on the electrical grid for LED lighting and climate control. Making them potentially worse for the environment than conventional agriculture. Proponents of traditional farming argue that these energy costs create a high “price floor,” making vertically grown produce consistently 20% to 30% more expensive than field-grown alternatives during their natural growing seasons.
Ultimately, these technologies are here to stay. Companies will continue to look to technological innovation and the incorporation of AI to improve their operations and lower costs. The question is will this benefit the consumer as well as the companies that use them? Or will it only benefit the profit margins and stockholders of those companies?
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