Vending

How AI is making vending smarter

While the vending industry has embraced technology for decades through cashless payments, telemetry and remote machine monitoring, AI is opening the door to a host of operational improvements.

Photo: Adobe Stock

June 12, 2026 by Richard Slawsky — Editor, Connect Media

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Artificial intelligence has dominated headlines in recent months, but for vending operators, the more important question is whether it can improve profitability, reduce costs and deliver better service.

Increasingly, the answer appears to be yes.

While the vending industry has embraced technology for decades through cashless payments, telemetry and remote machine monitoring, AI is opening the door to a host of operational improvements. Rather than simply collecting data, operators can now use AI-powered tools to analyze that information, identify patterns and make decisions faster than ever before.

The result is a shift from reactive management to proactive operations.

"I think that technology will continue to mature, but the biggest value for me has been using AI to operate the business better," said Matthew Rauls, CEO of Atlanta-based Red Hawk Vending, in an email interview. Red Hawk has more than 100 machines deployed. "That is where it has really helped us move faster, make better decisions, improve our internal systems, and create efficiencies that would have been much harder for a company our size to build just a few years ago," he said.

From data collection to data analysis

Many vending operators already collect large amounts of information via remote management systems. Machines can report inventory levels, sales activity, payment status, temperatures and equipment performance in real time.

A typical vending operation may generate thousands of data points every day. Reviewing that information manually is often impractical. AI systems can continuously analyze those data streams, identifying trends and highlighting issues that might otherwise go unnoticed.

"Vending has always produced a lot of data but turning that data into usable information is the key," Rauls said. "Over the next three to five years, AI will help operators connect sales history, inventory levels, service schedules, product costs, customer feedback, warehouse picking, route planning, equipment status, and purchasing decisions into one clearer operating picture."

For example, AI can identify products that consistently sell out before scheduled service visits, allowing operators to adjust stocking levels. It can also detect machines that are underperforming compared to similar locations, helping operators investigate potential issues.

Instead of relying solely on experience and intuition, operators can use data-driven insights to guide decision-making.

"The biggest day-to-day wins are in predictive restocking and demand forecasting, based on sales velocity and a deeper understanding of customer preferences in local markets," said James Boushka, Aramark Senior Director of Technology and Growth, in an email interview. Aramark operates one of North America's largest workplace refreshment networks, providing vending machines, smart vending, micro markets, office coffee and breakroom services to businesses, healthcare facilities, educational institutions and industrial sites.

"We've also been leaning into sensing technologies and artificial intelligence to guide maintenance and machine functioning," Boushka said. "Aramark is using artificial intelligence to identify potential equipment issues earlier, enabling faster response times and significantly reducing machine downtime. Ultimately, these capabilities ensure customers have access to the products they want while creating a reliable, convenient experience."

Predictive inventory management

One of the most promising applications of AI in vending involves inventory management.

Traditionally, operators stocked machines based on historical sales patterns and route schedules. While experienced operators often develop a strong understanding of customer preferences, demand can fluctuate significantly.

A university campus may experience spikes in demand during exam periods. A manufacturing facility may see increased traffic during overtime shifts. Seasonal weather changes can affect beverage purchases, while special events can dramatically alter consumption patterns.

"AI looks at buying habits, past sales numbers, weather, local events, seasons, and other related factors to predict what's likely to sell," said Aušrinė Babonaitė, communications manager with the trade publication Cybernews, in an email interview. "Rather than simply reporting that a machine is running low on a product, AI can estimate when inventory will need replenishment and recommend stocking adjustments before shortages occur."

This predictive capability can help reduce out-of-stock situations while minimizing excess inventory.

Smarter route planning

Route efficiency remains one of the largest operational challenges facing vending companies.

Traditional service schedules often rely on fixed routes that may not reflect actual machine needs. Drivers may visit machines that require little attention while other locations experience stock shortages or service issues before the next scheduled stop.

AI-driven route optimization helps address this problem by analyzing machine conditions, inventory levels and service requirements in real time.

"Instead of servicing locations only based on a fixed schedule, AI will help operators prioritize stops based on sales velocity, inventory risk, product expiration, equipment status, and customer needs," Rauls said. "That should help operators improve service while reducing wasted labor, fuel, and unnecessary trips."

The benefits extend beyond operational efficiency. Fewer vehicle miles traveled can also reduce fuel consumption and support sustainability goals.

Improving equipment reliability

Machine downtime represents one of the most costly challenges in vending operations.

A malfunctioning machine not only results in lost sales but can also damage customer confidence. If consumers repeatedly encounter out-of-service machines, they may stop relying on vending altogether.

"AI-enabled diagnostics are allowing faster mitigation of equipment to address performance issues," Boushka said. "Addressing equipment performance proactively is driving an increase in overall customer satisfaction and brand reputation."

By analyzing equipment performance data, AI systems can identify patterns that may indicate an impending failure.

"If a card reader fails, the service team can know before the next complaint," Dave Berman, co-founder of UK-based automated retail operator VendEase, wrote in a recent blog post on Vending Times. "If a chilled machine shows a temperature issue, that can be flagged quickly."

This allows operators to address problems proactively rather than waiting for customer complaints. In many cases, preventing service disruptions is far less expensive than responding to an emergency repair.

Understanding consumer behavior

AI also provides operators with a better understanding of customer preferences.

Historically, product selection decisions often relied on sales reports and operator experience. While those remain valuable tools, AI can uncover relationships that may not be immediately obvious.

For example, AI might identify that certain beverages sell better on specific days of the week or that particular snack combinations are frequently purchased together.

These insights can help operators refine product assortments, improve merchandising strategies and maximize revenue. As consumer preferences continue to evolve, the ability to respond quickly becomes increasingly important.

Challenges remain

Despite the potential benefits, adopting AI is not without challenges. Successful implementation depends on access to high-quality data, reliable technology infrastructure and clear business objectives. Operators must also evaluate costs and ensure that AI investments deliver measurable returns.

Many older vending machines lack the sensors and connectivity needed to generate reliable data, limiting the effectiveness of AI-driven analysis. Upgrading a fleet with edge devices, telemetry systems and cloud-based AI platforms can require significant upfront investment, while smaller operators may struggle to wait six to 12 months for measurable returns.

Operators must also be cautious about vendor lock-in, as some AI platforms restrict access to raw data, making it difficult to switch providers. In addition, early AI systems can generate false maintenance alerts or inaccurate recommendations, potentially eroding user confidence if the technology is not properly implemented and monitored.

There is also the challenge of separating genuine AI capabilities from marketing hype. Not every software platform marketed as "AI-powered" offers meaningful operational advantages.

Industry experts recommend focusing on practical applications that solve specific business problems rather than pursuing technology for its own sake.

The future of AI in vending

Artificial intelligence is unlikely to replace the expertise of experienced operators. Instead, it is becoming another tool that helps them make better decisions.

"For owner-operators just getting started in the unattended retail space, AI helps take the guesswork out of the business," said Mike Burnett, CEO of SmartMarket Solutions, in an email interview. The company offers refrigerated, cashierless "SmartMart" retail units that use computer vision and AI to track what customers take and automatically process payment.

"They can see what their best sellers are, what needs to be restocked, which locations are performing the best, and so forth," Burnett said. "The result is a lot less wasted inventory, better product decisions and more sales."

As telemetry systems become more sophisticated and machine connectivity continues to improve, AI's role in vending operations is expected to expand.

When it comes to items such as salads or sandwiches, for example, AI can adjust prices based on spoilage risk, time-of-day demand or competitor pricing nearby. Cameras combined with edge AI will be able to identify which shelves and product slots perform best at different times of day. Screen-based machines may eventually be able to estimate demographic characteristics and suggest items based on customer preferences.

Ultimately, AI may be able to order products, negotiate with distributors and dispatch local gig workers to restock, making the process completely hands-off for the operator. While many of these capabilities remain in the early stages of development, industry observers believe they could become increasingly common as machine connectivity improves.

"And where will it continue to evolve is the personalization of product recommendations for an intelligent shopping experience," Boushka said. "Evolving the vending machine from a product-dispensing machine to a smart and relevant experience for the consumer will drive the future of vending experiences."

About Richard Slawsky

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