AI Adoption Soars in Restaurants as 70% Use or Test Technology for Loyalty and Customer Experience

70% of Restaurants Are Using or Testing AI to Improve Loyalty Programs

The latest Deloitte survey reveals a clear trend: AI adoption in restaurants is accelerating. A striking 82% of restaurant leaders plan to boost their AI investments next year. While many are already applying AI to enhance customer experience and manage inventory, fewer than 30% feel fully prepared from a technical standpoint.

The global AI market is projected to grow from $235 billion in 2024 to over $631 billion by 2028. Restaurants are positioning themselves to capture a meaningful share of this growth by using AI to optimize operations, build loyalty, and stay competitive.

Why Restaurants Are Betting on AI

Deloitte’s survey, which covered 375 executives across 11 countries, found that AI spending is viewed as strategic. Only 2% plan to cut back. The top expected benefit is improving customer experience (60%), followed by smoother operations (36%), enhanced loyalty programs (36%), and more efficient supply chain management (36%). Other areas like food waste reduction, marketing, and crew engagement also show promise.

Different segments have distinct priorities. Casual dining focuses more on customer experience compared to quick service or fast casual restaurants. Regionally, U.S. and European operators emphasize guest experience, while Asian markets prioritize automation and labor support.

The Three Waves of AI Adoption in Restaurants

• First Wave – Core Operations: 63% use AI daily to improve customer experience; 55% apply it to inventory management using predictive analytics and IoT sensors.
• Second Wave – Loyalty and Labor: Around 70% are actively using or testing AI to strengthen loyalty programs and enhance employee experience. Quick service restaurants especially deploy AI for real-time crew scheduling and performance tracking.
• Third Wave – Food Prep and Innovation: Less than half currently use AI in food prep or innovation, but over 40% plan to. Use cases include AI-driven flavor profiling, quality checks via computer vision, and automated test kitchen modeling.

Casual dining and Asian restaurants lead across all three waves, while U.S. restaurants focus more on customer-facing AI like conversational assistants and voice ordering.

Popular AI Technologies in Use

• Chatbots: Used daily by 60% for ordering, reservations, and FAQs.
• Machine Learning: Utilized by 54% for pricing, demand forecasting, and guest analytics.
• Natural Language Processing and Intelligent Automation: Common in kiosks and drive-thru voice systems.
• Computer Vision: Applied for food safety and order accuracy, mostly in pilot phases.
• Generative AI: Only 9% actively use it; 25% are testing. Uses include personalized menus and automated marketing content.
• Avatars and Virtual Environments: Low adoption but growing interest for next-gen loyalty programs.

Challenges Remain Despite High Interest

Even with enthusiasm, readiness is a concern. Only 20% say their organization is prepared in governance and risk management, and less than 30% have adequate infrastructure or technical talent. Strategy is the strongest area, with 60% somewhat confident in their AI roadmaps.

Top barriers include:

• Identifying the right AI use cases (40%)
• Managing risks around data privacy and compliance
• Shortages of technical talent
• Meeting regulatory requirements, especially labor and privacy laws

Interestingly, executive buy-in and technology infrastructure are no longer major obstacles, indicating that the focus has shifted from securing approval to executing effectively.

Risks and Best Practices in AI Deployment

Key risks flagged by respondents include misuse of customer data, algorithmic bias, and cybersecurity vulnerabilities. Just over half of leading companies have integrated AI risk management strategies, while fewer than half include vendor assessments—a gap that could cause issues as reliance on third-party AI tools grows.

Best practices gaining traction among IT leaders involve:

• Cybersecurity measures specific to AI workloads
• Tracking ROI from pilot stages to full-scale deployment
• Auditing AI for bias and accuracy
• Designing AI with a human-centered approach, especially for customer interactions

Practices like explaining AI decisions and including human oversight are also becoming part of emerging ethics frameworks.

What Product Development Teams Should Take Away

AI is reshaping restaurant operations and customer engagement. Product teams should focus on selecting the right use cases and ensuring they have the technical capabilities and governance structures to support AI initiatives.

Building AI solutions that improve loyalty and operational efficiency can deliver real impact, but without preparation, projects risk stalling. Keeping an eye on emerging technologies like generative AI and computer vision, while managing risks around data and compliance, will be crucial.

For product professionals looking to deepen their AI expertise, exploring comprehensive training options can be a smart move. Resources like Complete AI Training’s latest courses offer practical skills in AI tools and strategies relevant to product development.