What place does artificial intelligence have in fresh produce? It’s simply, the future.

Mike Duff

For all the excitement about ChatGPT, it requires significant effort for companies to harness the power of artificial intelligence and related technologies, such as machine learning. However, doing so isn’t just advantageous, it’s crucial — because any company that doesn’t keep pace with developments is going to watch as its competitors master it.

Walmart is looking at the opportunity comprehensively.

In the company’s latest quarterly conference call, Walmart President and Chief Executive Doug McMillon says the company sees AI and technology applicable to retail as central to its operational advancement.

“As it relates to technology, our approach to new tools like generative AI is to focus on making shopping easier and more convenient for our customers and members, and helping our associates enjoy more satisfying and productive work,” says McMillon. “Ultimately, the power of generative AI or any technology is only as good as the data that powers it. Our data assets are unique, and we’re excited about the potential to leverage them in new and impactful ways.”

Walmart is exploring how such technology can help it tailor operations and communications in the everyday low price environment, and, so, create a more personalized shopper experience. Walmart also is working on ways of using technology to make employee jobs more satisfying, in part by cutting back on tedious repetitive tasks it can relegate to AI.

In addition, via AI, it is making supply chain improvements based on greater efficiency and the availability of more concise and precise information about what’s moving through it.


The key to successful AI implementation is defining the business purpose or goal before tapping the technology.

In terms of potential, Waqqas Mahmood, director of strategic consulting for business advisory Marcum LLP, New York, NY, says the firm is advising its clients to look at AI as a force they can apply across departments — in finance, budgeting and forecasting, as well as operations. He says companies can identify those operations that saddle employees with simple repetitive tasks and jump on the AI-enabled opportunity to automate them.

Mahmood says a thorough beginning assessment and reassessment of IT practices should be a precursor to AI initiatives, including data management and enterprise resource planning software to ensure that ERP, accounting and other systems can talk to one another. Then, businesses should create a road map that puts AI to work in meeting immediate needs and then in addressing longer term goals. The effort should include solid security measures.

Ensure systems provide clean data so that a business has “one source of truth,” or, to put it another way, to ensure the data is accumulated in a single pool that’s reliable and available across the entire operation.


Mahmood says this process is necessary because companies often use different codes to identify the same thing, whether a product or person or process, and even mix in vendor codes. In that circumstance, AI is going to generate errors, just as a person would if they sorted through a file not realizing three different codes identified the same person.

A company needs to ensure its stored data is consistent and recognizable by AI. The cleaner the data, the better performing the AI model, and the less humans have to get involved trying to edit and rework dubious results.


In some quarters, AI is long established and past applications illustrate where it can take various functions. Pricing optimization isn’t new, as anyone purchasing constantly repriced airline tickets today understands — AI has been part of its operation for years. However, as it has progressed, AI has made pricing optimization more capable of dealing with complex cases, such as produce pricing, giving retailers the ability to make evidence-based decisions on what to charge when.

The ability to rapidly change prices to suit demand and promotional strategies is ubiquitous online, but has been slower to take hold in stores. Where retailers use electronic shelf labels linked to a communications system such as Wi-Fi, they can automatically update prices, but it’s rare for store runners to use them throughout a store.

Produce is a product category that is subject to a lot of change in stores, and Matt Pavich, senior director of strategy and innovation at Revionics, Alpharetta, GA, says, pricing optimization for perishables was more complicated to develop, but some companies have been mastering it.

“One of the bigger differentiators in how sophisticated an AI solution is, is whether or not it can handle fresh categories.” — Matt Pavich, Revionics, Alpharetta, GA

Given that appearance, size, shelf-life, expiration dates, seasonality and other variable factors affect produce, price optimization has to cope with a lot of data and learns over time, particularly coupled with machine learning that can take the mass or material and refine it to satisfy business needs.

Retailers that have good measurements and data capabilities can work with AI, machine learning and other relevant technology to generate more precise information that can enhance the effectiveness of the entire produce supply chain, by improving purchasing flow at the store level and providing data that extends back through the system to ensure sufficient supply.

AI can provide efficiencies because retailers can price product in the pipeline based not only on established everyday and promotional schedules, but also on actual condition and demand at the moment.

Retailers can use pricing optimization to meet various business goals, such as getting maximum results from the introduction of seasonal fruits and vegetables. It can adjust prices down to local levels so products move in good time and curtail food waste and boost profitably. Across a large or small geography, AI-based systems can take local demand, price tolerances and favored product mix into account, as well as cost, condition and other relevant factors, in setting prices that balance movement and profitability based on forecasting, ongoing purchasing and adjustments to actual results.

With recent inflation, retailers that used sophisticated pricing optimization capabilities enjoyed advantages that led to market share and margin gains simultaneously, says Pavich.


AI has been critical in the development of self-checkout technology, and plays a role in security and detecting scanning evasion. Produce recognition — a critical issue given the diversity of products and varieties moving through stores, and their many attendant price points — is another way AI is helping retailers make the most of their produce operations.

Toshiba’s ELERA Commerce Platform provides self-checkout produce recognition, which identifies produce and eliminates the need to input produce codes at checkout manually. Retailers are already benefiting from the produce recognition innovation through improved inventory accuracy and throughput in the self-service experience.

“Toshiba’s ELERA Produce Recognition solution uses AI and computer vision technology to increase scanning accuracy and reduce the need to manually input codes and intervention from store associates.”

— Yevgeni Tsirulnik, senior vice president, Incubation and Innovation, at Toshiba, Durham, NC

“Toshiba’s ELERA Produce Recognition solution uses AI and computer vision technology to increase scanning accuracy and reduce the need to manually input codes and intervention from store associates,” says Yevgeni Tsirulnik, senior vice president, Incubation and Innovation, at Toshiba, Durham, NC.

“When an item is incorrectly entered, the ELERA Produce Recognition system automatically intervenes with prompts for the shopper to correct the error and continue the checkout process on their own,” explains Tsirulnik. “If a shopper is unable to resolve the issue on their own or is ignoring the prompts, then a store associate can be notified to assist with the checkout. This gives shoppers a self-checkout experience that is faster, friendlier and smarter.”

Toshiba recognizes the shopper experience should be the primary driver of retail technology.

“To simplify the process for consumers, instead of relying on PLU numbers, ELERA Produce Recognition provides the shopper with a curated set of produce items to select from based upon what was scanned by the camera and the application’s AI,” says Tsirulnik. “The benefit is twofold: supporting ease of use for shoppers and reducing the likelihood of mistake-driven shrink.”

Tsirulnik says AI-based solutions enable the produce recognition capability to evolve into a customized solution that better suits retail needs in the store and corporate dimensions.

“This solution supports the retail industry with more accurate inventory insights, shrink and loss prevention, and provides more tailored consumer experiences,” he says.

Toshiba’s ELERA Commerce Platform provides self-checkout produce recognition, which identifies produce and eliminates the need to input produce codes at checkout manually.

“The AI-powered learning capability enables the system to continually scale to the needs of the business,” he adds. “The expected benefits include reduced transaction time by providing the shopper with a smaller and more accurate set of items to select from, resulting in an improved overall shopper experience.”

Based on recent retailer data, Toshiba concluded that ELERA Produce Recognition, on average, can save customers as much as five seconds per produce item lookup during checkout, compared to other traditional self-checkout systems, he says.

“The self-service experience can be riddled with challenges,” says Tsirulnik. “In one study, as much as 23% of shoppers reported avoiding self-checkout due to the inconvenience of checking out produce. But over the last two to three years, consumer interest in frictionless experiences, where they can get in and out of a store as quickly as possible, is growing.”

He says retailers realize that having the system recognize and suggest items at checkout “is vital to helping eliminate slow transaction times, inaccurate store inventory, reduce loss and give shoppers an experience that keeps them coming back.”


Afresh, San Francisco, CA, is applying AI in a more direct assault on food waste. The company has established a goal of eliminating food waste and making fresh food more broadly available. Afresh helps stores place accurate orders that mitigate waste and keep shelves stocked. As such, Afresh is helping food retailers not only reach sustainability goals but to command bigger profits.

Matt Schwartz and Nathan Fenner launched Afresh to directly address the lack of purpose-built technology for fresh food.

“Afresh leverages AI to help grocers make smarter decisions in fresh department store ordering and inventory management amid the complexity and uncertainty that comes along with fresh.”

— Matt Schwartz, chief executive, Afresh, San Francisco, CA

“Afresh’s first product, an AI-powered predictive ordering and inventory management solution, is the only built-for-fresh solution that intelligently navigates hard-to-predict and error-prone data to drive optimal decisions for grocers in their fresh departments,” says Schwartz, Afresh chief executive. “Afresh enables grocers to reduce waste, empower store teams, and drive profitability across the business.”

The founders began operations by speaking with, and shadowing, people in the food supply chain to learn about their unique challenges, then built their AI-powered solution to address the needs they encountered, says Schwartz.

“Variables like perishability, changing display sizes, and seasonality make fresh departments particularly tricky to navigate, but the Afresh platform uses cutting-edge machine learning to deliver a unified view of all the factors that influence an optimal decision for fresh ordering,” he says.

The Afresh AI-based system gathers accurate data by using targeted human inputs, which helps assure they enter the system clean.

“Each order day, fresh department managers follow a short list of required inventory checks that help generate AI-managed, pre-filled orders,” says Schwartz. “By asking for targeted counts only where needed, this data is ultimately more accurate, and store associates have more time to focus on value-additive tasks, such as interacting with customers.”

The first product from San Francisco, CA-based Afresh is an AI-powered predictive ordering and inventory management solution. It is the only built-for-fresh solution that intelligently navigates hard-to-predict and error-prone data to drive optimal decisions for grocers in their fresh departments.

The system has its own approach to demand forecasting as well, leveraging AI to model inventory position, data input quality, perishability and other specific factors.

“The system leverages a new and rapidly evolving field of AI specifically focused on decision-making in uncertain conditions to arrive at order recommendations for hundreds of items that fresh department managers accept, on average, 94% of the time,” says Schwartz, adding grocers who leverage Afresh’s AI for ordering and inventory management “see lighter backrooms, faster inventory turns, more efficient store teams and happier customers.”

“Retailers create 40% of all food waste, and over two-thirds come from fresh categories,” says Schwartz.

He says Afresh grocer partners enjoy a 25% reduction in food waste, which means, since 2019, the company has prevented over 43 million pounds of products getting trashed.



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