Restaurant Tech Execs Warn Against AI Overdependence — Here Is Where It Actually Works in 2026

At the 2026 National Restaurant Association Show in Chicago, Restaurant Dive asked every tech leader they interviewed the same question: “When people talk about the restaurant sector right now, what is one thing they are getting wrong?”

The answer, almost universally, was “artificial intelligence.”

This is not what you would expect at the industry’s biggest trade show, where AI booths dominated the floor and every vendor had something “powered by AI” to sell. The skepticism came not from Luddites but from the very people building restaurant technology for a living. Square head of food and beverage James Schonzeit called AI “the biggest thing of this entire show.” Then he and every other executive proceeded to explain why most of it is overhyped.

For independent restaurant operators watching from the sidelines, the signal is clear: AI is real, but the rush to buy AI solutions without understanding the problem is burning money.

The AI Overdependence Warning in Their Own Words

Brendan Sweeney, CEO of Popmenu, put it bluntly. “No one can tell you what is going to be in place a year from now. They can tell you what they think, like Microsoft AI CEO saying all white-collar work will be done by AI in 18 months. That is obviously bullshit.”

Sweeney pointed to the economics of AI itself as a reason for skepticism. The cost of AI tokens required to operate coding services can exceed the salaries of the workers the technology purports to replace. With OpenAI pursuing an IPO and facing pressure to turn a profit, and the rising cost of electricity making AI services more expensive to deliver, the promised cost savings are not guaranteed.

“Being first is not always the best,” Sweeney said. “Being right and doing it the right way is the best.”

Kelly Esten, chief marketing officer at Toast, emphasized reliability. “There are some parts of running a restaurant that are all about reliability,” she said. AI can augment robust underlying programs and improve workflows, but it cannot replace the core aspects of restaurant operations. It cannot be another thing for operators to do. It has to actually do some of the work for you.

Kevin Bryla, SpotOn chief marketing officer, warned against the idea that AI fixes everything. “Some proponents think an overdependence on AI is going to fix everything.” Instead, AI that is embedded in restaurant tech platforms can give operators better insights into operational strengths and weaknesses. “AI at its best is going to serve up an option to an operator to make an informed decision.”

R.J. Hottovy of Placer.ai offered the most grounded perspective: “There are a lot of great technology solutions here, but technology cannot replace the consumer experience. When we look at those companies that have done more investment in labor, whether that is Starbucks or somebody else, Cava is a good example, they are the ones who win right now.”

The Starbucks Case Study: AI Inventory Tool Killed After 9 Months

The most concrete example of AI overdependence came in the same week. Starbucks eliminated its computer vision AI inventory management system after nine months of testing. The system, which used cameras and machine learning to track inventory levels and predict restocking needs, failed to deliver the promised operational improvements.

The lesson is not that AI inventory management is bad. It is that AI tools need to solve a real problem that existing solutions cannot handle. Starbucks spent months implementing a system that might have worked in a controlled environment but could not handle the variability of real restaurant operations. Product packaging changes, staff workarounds, and the sheer chaos of a working kitchen broke the assumptions the AI was trained on.

For independent operators, the message is direct. If a company with Starbucks resources and technical talent could not make computer vision inventory management work, the vendor selling you a similar system for $500 per month is either oversimplifying the problem or selling you something that does not work in the real world.

Where AI Actually Works in US Restaurants Right Now

The executives who warned against overdependence were not anti-AI. They were specific about where AI delivers measurable results today versus where it is a solution in search of a problem.

Demand forecasting and purchasing. This is the most proven use case. AI models that analyze historical sales data, weather patterns, local events, and seasonal trends to predict inventory needs are delivering 12-18 percent reductions in food waste and measurable improvements in margin. Restaurant365 data from 2026 shows 61 percent of operators report lower food costs from AI-powered forecasting.

Labor scheduling. AI scheduling tools that match staffing levels to predicted demand are cutting labor costs by 10-25 percent. Nory Agentic AI reported prime cost reductions of 10-25 percent for multi-unit operators using AI scheduling combined with food cost management.

Marketing personalization. AI that analyzes customer purchase history and preferences to send targeted offers and recommendations is the most widely adopted use case. Twenty-six percent of operators using AI in 2026 are using it for marketing, per the National Restaurant Association February 2026 report.

Back-of-house analytics. AI systems that monitor kitchen workflows, identify bottlenecks, and suggest process improvements are gaining traction. These tools do not replace kitchen staff. They give managers data to make better decisions about station assignments and prep schedules.

Where AI Is Not Ready for Independent Operators

The flip side is equally important. Independent operators should be skeptical of AI promises in several areas until the technology matures.

Voice AI for drive-thru and phones. McDonald’s scaled back its AI drive-thru partnership with IBM. Taco Bell drew customer complaints over AI voice ordering. While DQ is expanding Presto voice AI to franchises and technology is improving, reliability is still not consistent enough for a busy independent restaurant where a single bad order costs a customer.

Full kitchen robotics. Miso Robotics acquired Zume Pizza IP, but the vision of robot-run kitchens remains distant. CaliExpress showed what autonomous restaurants look like today: impressive demos that break down during peak hours. The burger-flipping robot works until it does not, and when it fails during a Friday night rush, there is no backup.

Computer vision inventory. Starbucks just demonstrated the limits of this approach. Camera-based inventory systems struggle with the variability of real kitchen environments. The technology is improving, but it is not ready for independent operators to bet their food cost margins on.

AI replacing human judgment. Every executive interviewed at the NRA Show agreed on this point. AI can present options and context, but it cannot replace the experience of a manager who knows their neighborhood, their regulars, and their kitchen. “It is what you do with the output that matters,” SpotOn’s Bryla said.

The Investment Framework for Independent Operators

The NRA Show 2026 consensus points to a clear framework for evaluating any AI purchase for your restaurant:

Does the AI solve a specific, measurable problem? Not “improve operations” — that is vague. A specific problem is: “I am throwing away 22 percent of my produce every week because I cannot predict demand accurately.” If the AI tool does not directly address a concrete number you can measure, it is not ready.

Does it work without adding tasks? Esten from Toast said it best. “It cannot be another thing to do. It has to actually do some of the work for you.” If the AI tool requires daily input, monitoring, and manual override, the net benefit is negative.

Is the vendor transparent about failure modes? Every restaurant technology executive interviewed said the same thing: the vendors who tell you what their AI cannot do are more trustworthy than the ones who claim their AI does everything. Ask specifically: “Under what conditions does this system fail?” If the vendor cannot answer, walk away.

Can you test it on one problem first? The most successful AI deployments in restaurants start with a single use case. Nory started with scheduling. Toast IQ started with sales analysis. Do not buy a platform. Buy a solution to one problem. If it works, expand.

What Independent Restaurant Owners Should Do in 2026

Ignore the hype. Focus on the three areas where AI is delivering real returns today: demand forecasting, labor scheduling, and marketing personalization. These are the tools that the world Toast, Square, and SpotOn are building their AI strategies around. They are proven, measurable, and available to independent operators at accessible price points.

For everything else, wait. Watch the chains deploy voice AI and learn from their mistakes. Watch Starbucks figure out what went wrong with computer vision. Watch the robotics companies solve the peak-hour reliability problem. Your advantage as an independent operator is not being first. It is being smart.

Popmenu Sweeney said it best: “Being first is not always the best. Being right and doing it the right way is the best.” That advice applies perfectly to restaurant AI in 2026.

Frequently Asked Questions

What percentage of US restaurants use AI in 2026?

The National Restaurant Association reported in February 2026 that roughly 26 percent of operators use AI tools. Marketing is the top use case. A separate Popmenu survey found 44 percent of restaurants using some form of AI. The difference reflects how broadly the term AI is applied to different tools.

Did Starbucks really kill its AI inventory system?

Yes. Starbucks discontinued its computer vision AI inventory management tool in mid-2026 after approximately nine months of testing. The system used cameras and machine learning to track stock levels and predict restocking needs but failed to deliver the expected operational improvements.

What AI tools do restaurant tech leaders actually recommend?

Toast IQ for sales analysis and labor management, Restaurant365 for AI-driven financial and food cost management, Nory for agentic AI scheduling and prime cost reduction, and platform-based AI features from Square and SpotOn that are embedded in their existing POS systems rather than sold as separate tools.

Is voice AI ready for independent restaurants?

Not yet. McDonald’s and Taco Bell faced reliability issues with voice AI in drive-thrus. Dairy Queen is expanding Presto voice AI to franchises, but the technology is still being refined. Independent operators should wait until the major chains work out the reliability issues before investing in voice AI for phone or drive-thru ordering.

What is the safest first AI investment for a restaurant?

AI-powered demand forecasting and inventory management. The data is clear: 61 percent of restaurant operators using AI report lower food costs. This use case requires the least workflow change and delivers measurable ROI in waste reduction and purchasing optimization.