Five years after COVID-19 hit, it’s clear that some pandemic-driven shifts in how consumers engage with quick service restaurants (QSRs) have become permanent challenges the industry must address. 45% of people now prefer takeout or delivery over dining at restaurants, for example, and drive-throughs are booming, with traffic up 30% over 2019. At the same time, economic pressures have squeezed QSRs from all directions. Costs for food and labor are up 29% and 31% over the past four years, forcing many restaurants to raise prices—and leading to pushback. The percentage of reviews describing QSRs as "overpriced" jumped 43% from 2023 to 2024, signaling growing price sensitivity among customers.
And competition has intensified on multiple fronts. International brands are increasingly targeting the US market, bringing fresh concepts and deep pockets to an already crowded landscape. Even convenience stores are expanding their food offerings to appeal to on-the-go customers who might otherwise visit traditional QSRs.
Amid these challenges, QSRs sit on a wealth of useful data. From refrigerators and freezers to ovens and grills, security cameras to HVAC systems, the modern restaurant has numerous internet-enabled devices generating valuable information, on top of more traditional data sources like point of sale systems and customer loyalty programs. However, many QSRs struggle with a fundamental problem: there's no single source of truth for this data, making it difficult to gain real-time visibility into operations—let alone coordinate across multiple locations.
This is where AI can make a difference. By driving insights and actions with data from across operations, AI empowers QSRs to make better decisions, satisfy evolving customer demands, and keep costs in check.
In this blog post, we'll explore five key challenges for QSRs and how AI can help tackle them by turning fragmented data into actionable intelligence.
1. Improve inventory management
Inventory management has always been a balancing act for QSRs.
Order too much, and you risk waste: 4% to 10% of food that is purchased by restaurant leaders never even gets to the customer, with restaurants losing an estimated $1,000 of revenue per 3.3 pounds of food waste.
Order too little, and you face shortages that lead to missed sales and disappointed customers.
Without a clear picture of inventory levels across locations and sophisticated demand forecasting, QSRs are essentially making educated guesses about how much to order and when.
AI transforms inventory management by providing real-time visibility and predictive intelligence:
- Unified data view: Create a single source of truth for inventory across all locations, giving managers instant visibility into stock levels without error-prone manual counting.
- Smarter forecasting: Predict demand based on diverse factors (past purchases, local events, weather, and more) and get recommendations on precise ordering amounts.
- Proactive alerts: Get notifications when inventory approaches critical levels, enabling managers to restock before shortages impact customers.
Cava is experimenting with AI to monitor how quickly ingredients are being depleted, giving them real-time visibility into inventory usage patterns and minimizing stockouts that impact sales.
2. Process orders quickly and correctly
Social media reviews mentioning that orders took "a long time" increased 34% between 2023 and 2024. Meanwhile, order errors—which North American consumers rank as their top factor for choosing a fast-food restaurant—frustrate customers and lead to waste.
People visit QSRs primarily for convenience, so long waits and order errors directly undercut the value proposition. AI helps QSRs deliver on their promise of fast, accurate service:
- Take care of tedious tasks: Automate routine steps such as collecting orders and payments to minimize errors and let staff focus on delivering high-quality food.
- Order optimization: Use AI-powered kiosks that stay up to date with current inventory, recommend upsells, and queue orders for efficiency in the kitchen.
- Personalized experiences: Leverage customer data from loyalty programs to make recommendations based on past orders, streamlining orders and fostering brand affinity.
Taco Bell is testing AI ordering in drive-throughs to automate the process and improve order accuracy, making life easier for employees and customers alike. Concessions areas at Seattle's Lumen Field are using Amazon's Just Walk Out technology to streamline checkout so guests can purchase food and gear without missing out on the game.
3. Ensure food quality and safety
As consumers become more cost-sensitive, ensuring consistent quality is more important than ever. Yet variability in food preparation can lead to disappointing results and safety risks. Directors and operators point to people-related factors as the greatest risk to food safety, citing challenges such as staff turnover and a lack of employee knowledge.
Without clear visibility into food preparation and storage conditions, QSRs struggle to address issues before they affect customers. AI helps QSRs take charge of quality and safety:
- Real-time monitoring: Track critical parameters like temperature across all equipment, with instant alerts when metrics fall outside acceptable ranges.
- Prep optimization: Deploy smart cooking systems that automatically adjust their settings and alert cooks to potential issues.
- Visual quality inspection: Use computer vision to detect quality and safety issues in real time.
- Compliance tracking: Document food safety practices automatically, ensuring consistent adherence to protocols.
Chick-fil-A's IoT platform uses AI with cameras located in food prep areas to manage food quality and safety, giving them unprecedented visibility into their operations. A Sbarro pilot is showing how the Viam platform can analyze how long pizzas have been sitting out and generate alerts when they need to be replaced, ensuring customers always receive fresh food.
4. Optimize workforce management
Though pandemic-era labor challenges have eased, 77% of QSR operators still report that recruiting and retention are "a significant hurdle," and 32% don't have enough employees to meet demand.
AI helps QSRs optimize staffing, ensuring the right team is in the right place at the right time to keep lines moving and make customers happy. In the kitchen, QSRs use AI to optimize food prep and stocking. On the floor, AI helps customer-facing staff deliver swift service and turn over tables quickly. Shake Shack’s AI considers factors such as a restaurant's channel mix, menu, and historic data on traffic to create a custom schedule for each restaurant, ensuring they have the right staffing levels to meet demand. And Yum Brands is testing a mobile app that lets employees get quick answers to questions instead of having to search through training materials.
5. Promote cost-efficiency
Rising costs for everything from ingredients to equipment are putting pressure on QSRs' profitability. While many cost increases are out of QSRs' control, other factors—such as inefficient processes and a lack of preventative maintenance—present opportunities for savings.
But without comprehensive visibility, QSRs struggle to identify these opportunities and address them systematically. AI helps QSRs glean actionable insights across operations:
- Energy usage analytics: Monitor and optimize energy consumption across equipment and locations, identifying patterns and anomalies that drive up costs.
- Predictive maintenance: Detect early warning signs of equipment issues before they lead to costly breakdowns and service interruptions.
- Business intelligence: Analyze data from across all operations to accurately forecast sales, manage costs, and make data-driven investment decisions.
Sweetgreen uses AI to analyze its performance and customer satisfaction across a diverse range of data sources, including customer feedback from surveys, POS transaction data, and data from its mobile app.
Go from surviving to thriving with AI
Amid headwinds in the industry, AI-powered technologies are a powerful tool to help QSRs deliver quality food and delight customers while keeping costs in check.
The key to success lies not in automation alone, but in creating a single source of truth for operational data, generating intelligence from that data, and sharing insights across the organization. And this doesn’t require an expensive, years-long transformation project. The data from your existing hardware will often provide opportunities to improve efficiency, enhance customer experience, and drive revenue.
Viam's platform transforms restaurant data into actionable insights that drive revenue by providing real-time visibility across all aspects of your operation. Learn how we can help your QSR thrive in today's challenging environment.
