Why QSRs Require Reliable Self-Service AI to Grow | Modern Restaurant Management

The self-service kiosk market is growing fast, projected to reach $37.2 billion in 2025, with restaurant installations doubling to 700,000 by 2028. 

The growth coincides with the changing preferences of customers too. According to research on restaurant technology, 80 percent of customers say technology influences where they eat, with two out of five customers even calling tech, “extremely important.” That means that when technology like order kiosks fail during peak restaurant hours, the very hours when restaurants generate 40-60 percent of their daily revenue, the result is lost sales and increased costs.

Now, AI brings new opportunities to restaurants. Dozens of restaurants already use AI in their drive-thru operations today. Before long, every QSR (quick-service restaurant) will look to use AI to create personalized ordering experiences across all channels, ensure order accuracy from kitchen to delivery, and reduce restaurant wait times through intelligent demand forecasting.

The catch is that AI can only achieve these kinds of objectives if the system it depends upon is reliable.

Let’s examine how AI is being used by quick-service restaurants today as well as what’s required to make endpoint restaurant tech data reliable. We’ll also look at how QSR leaders are staying ahead.

From AI Drive-thrus to Self-Service Kiosks

As many as eleven major QSR chains have deployed AI drive-thrus. Wendy’s FreshAI has improved the speed of service by 22 seconds. Checkers & Rally’s deployed their Hi Auto voice AI across 350 locations, reporting shorter order times and improved upsell performance. 

These early successes prove that AI technology can lead to improved operational metrics. But why stop at the drive-thru? AI will be added to other self-service applications to help customers order the food they want faster.

Restaurant adoption of AI for self-service technology and automation will help solve a current problem: 45 percent of restaurant operators lack sufficient staff to meet demand. Self-service kiosks solve a labor problem. They also offer benefits. Research indicates that kiosks increase average order values by 10-30 percent. 

Putting AI technology in self-service kiosks will surely coincide with adding AI to other restaurant technologies like digital signage too. So long as AI tools can access device data and integrate that data into broader systems and software, the only thing that could slow adoption down is reliability.

New Restaurant Tech Requires Reliability

The networked system of technology used by modern QSRs must work reliably to prevent process failures. Self-service kiosks, drive-thru systems, kitchen displays, POS systems, equipment sensors, kitchen inventory systems, and more can all be affected if one technology breaks down or misbehaves. Should a component fail, from a fryer to a receipt printer, the entire experience can fall apart, leaving employees scrambling and customers frustrated. 

For example, when a customer places an order at a kiosk, the POS system must properly charge the card and the order must be delivered to the back-of-house kitchen operations, becoming visible to staff on kitchen display systems. If a receipt isn’t printed or a KDS goes down, the process breaks: the order may be ready without the customer knowing it’s their order number being called; or the staff may fail to realize an order needs to be made in the first place.  

AI services on top of these systems similarly depend on the technology working in order to make useful observations, run automations, fulfill agentic responsibilities, or any other task. Ensuring endpoint technology at restaurants is maintained and operational, and then integrating those systems into the business is an ongoing problem to solve. 

But some of the biggest brands are already showing what’s possible.

What  QSR Leaders Are Doing

McDonald’s demonstrates how scale and reliability work together. Across 43,000 restaurants, the company’s Google Cloud partnership enables predictive monitoring of kitchen equipment, catching fryer and freezer failures before they happen. The results tell the story: On-time preventive maintenance on cooking equipment improved from 68 to 92 percent, unplanned equipment downtime dropped by 55 percent (avoiding $42 million in annual repair costs), and each store saves $18,000 annually in labor costs. Computer-vision guidance in the kitchen reduced assembly errors by 35 percent, pushing order accuracy above 96 percent. But not everything is going as planned. For example, McDonald’s ended its IBM AI drive-thru partnership in June 2024 after customers complained about order errors and accent recognition problems. 

Chick-fil-A built reliability into its culture. With 3,000+ locations processing eight million daily orders and generating $21 billion in annual sales, the company couldn’t find commercial solutions that met their needs, so they built their own. Their philosophy: “Buy when possible, build when necessary.” Site Reliability Engineering practices including Prometheus monitoring, OpsGenie incident management, and 24/7 cellular backup deliver near-100 percent POS uptime. The result? The highest drive-thru output numbers in the world and top customer satisfaction ratings. Their robotic lemon processing facility alone saves 10,000 labor hours daily. 

Panera Bread took a different approach. The company invested $42 million in its Panera 2.0 kiosk program, becoming one of the first major chains to go all-in on self-service. With 2,100+ cafes and 60 million MyPanera loyalty members, the company leverages massive consumer data for personalization. Their DoorDash SmartScale system uses precision weighing to catch order errors before they leave the kitchen, reducing guest-reported missing items by 42 percent. Their strategy is to be a “fast follower.” Let others test technology, then implement refined versions. 

What Is the Connected Restaurant?

McDonald’s, Chick-fil-A, Panera, and most quick-service, fast-food, or fast-casual restaurants today can be thought of as “connected restaurants,” as each restaurant requires dozens of synchronous, networked devices to operate, like POS solutions, digital menu boards, kiosks, card readers, tablets, drive-thru systems, IP cameras, and on and on. All of these technologies must work reliably and consistently from location to location, which is what customers today expect.

But with so many technological parts in motion, a lot can and will go wrong. 

Restaurant operators and technical support teams need visibility and control over these tech deployments. Remote monitoring and management, a kind of software that originated within enterprise IT teams, is becoming standard in restaurants. 

“RMM” has historically been associated with large-scale deployments of computing devices like laptops, printers, and servers. However, as restaurants have become “connected,” they have realized a need for RMM capabilities such as real-time visibility into the status of every device and peripheral in the restaurant environment, or the ability to deploy software and firmware updates. RMM makes it possible to spot issues before they impact service, so a printer being out of paper or a kiosk screen becoming unresponsive can be fixed before restaurant-goers or employees notice the problem.

Monitoring device health continuously also means addressing potential failures proactively. For multi-unit restaurant operators, RMM can lower equipment downtime, resulting in fewer emergency truck rolls. The software can be centrally controlled to sustain thousands of locations.

Through RMM, connected restaurants increase uptime, improving device reliability, while also ensuring upstream data needs by AI services are met.

AI Requires Device Reliability

If a voice AI service is unable to track when customers need staff help, the result could be misleading metrics, like false success rates. 

Or if ordering kiosks aren’t well-integrated with inventory systems, AI will be unable to predict stockouts or flag supply issues. 

When systems operate in silos, AI optimizes for phantom problems while missing real ones. Remote monitoring and management offers the possibility of a unified data layer across all restaurant technology, ensuring AI learns from complete, accurate operational information. 

QSRs are already beginning to use RMM to manage their restaurant technology deployments. The technology offers a foundation upon which remote devices become reliably able to feed business data into larger restaurant software.

Operators know that restaurant technology is make-or-break today. But how restaurants serve up reliability depends on operators maintaining a watchful eye on what tech is working (or not). As McDonald’s CIO Brian Rice stated: “If we can proactively address issues before they occur, that’s going to mean smoother operations in the future.” 

AI is part of the connected restaurant today. To ensure AI works tomorrow requires building restaurant tech on a strong foundation of reliable data.