Artificial intelligence is top of mind for businesses. It has transitioned from being technical jargon to boardroom talk and management team discussions.
While the restaurant industry has traditionally been slower to adopt new technology, AI stands out as a top priority for enterprise brands according to Qu’s 5th Annual State of Digital report.
Unlocking AI’s true potential remains challenging for restaurants. AI innovations shouldn’t be a “standalone solution” for brands, but a value-added capability that’s integrated into the technology stack.
There are five AI focus areas that restaurant leaders should consider adopting in 2024:
- 1. Conversational AI
- 2. AI-Enabled Alerting and Analytics
- 3. Optimized Food Production & Delivery
- 4. Dynamic Commerce
- 5. Robotic Automation & Computer Vision
Taking these focus areas into account, what applications of AI can help drive the next phase of growth for restaurant brands?
1. Improve Operations with Conversational AI
Voice-AI Ordering
Many chains are experimenting with voice-AI ordering in the drive-through, and over the phone, to improve customer experience and help with labor efficiency.
Voice-AI ordering applications can seamlessly handle routine customer queries and effortlessly transition to human staff when complex requests arise. Brands gain order and data consistency and the flexibility to scale back on staff count, if needed, amidst rising labor costs.
The AI components of the technology—converting speech to text, translating that text into meaningful menu options, making sense of seemingly contradictory orders and/or changing orders—have and will continue to evolve rapidly. The real challenges of bringing AI into the store are the latency and speed issues related to voice ordering. Ensuring that the cloud platform the AI is integrated with can handle these issues is critical.
Real-time insights
Another use case for conversational AI is around real-time insights. Picture having an assistant capable of analyzing sales data and delivering real-time insights with a simple voice command in seconds, and then comparing that data to previous days, weeks, or even years. The power of actionable insights can include any data that’s relevant to a store’s performance. With a voice-enabled app, franchisees and owners could ask about this week’s revenue, which menu items are hot right now, or how labor costs are trending versus sales, and receive answers in seconds.
2. Harness the Power of Data with AI-Enabled Alerting and Analytics
Proactive AI promises a future where AI not only understands spoken language and can react to questions, but also anticipates needs before they’re even asked. AI-driven analytics apps powered by AI and machine learning (ML) can provide data-driven insights that brands can act on immediately, driving better business results. Franchisees could receive proactive alerts in real-time as issues occur, ensuring timely response without delay. Anomaly detection can highlight unusual sales trends, while predictive analytics aids in making informed decisions in real-time.
3. Optimize Food Production and Delivery
As digital orders have exploded, broken promises of delivery times have become the leading cause of guest dissatisfaction. For quick-service restaurant operators, solving for food cost efficiency versus speed of service has been the holy grail of challenges. These two problems present a perfect use case for advanced ML and AI algorithms. AI can help orders stay accurate and on-time, and keep the drive-through moving.
Kitchen production optimization boards use AI to help kitchens decide how much food to make and when. Using real-time omnichannel historical trends and promised order ready times relative to delivery actuals, combined with event, weather, and other local data, the AI-driven platform can dynamically suggest the right amount of food to produce in advance of expected demand—so brands can improve the speed of service and minimize waste.
4. Strategically Stimulate Demand with Dynamic Commerce
When applied to the right datasets, advanced machine learning (ML) can optimize pricing, discounts, upsells, labor scheduling, inventory management, and more.
Dynamic pricing has been a hot topic as of late and can be an excellent way to boost sales during off-peak hours, offering ‘dynamic promotions’ to entice guests to order during slower times. When rolling out dynamic pricing, lead with the true value proposition customers care about—discounts, promotions, and premium offerings. AI-powered dynamic pricing, personalized promotions, and menu optimization can help brands strategically stimulate demand.
5. Unlock the Power of Robotic Automation and Computer Vision
Automation of repetitive back-of-house tasks can enhance operational efficiency, reduce costs, and improve productivity. A well-known example is “Flippy,” the robotic device from Miso Robotics that automatically fries items from French fries to chicken nuggets. While still in prototype and early testing, it’s an example of exciting things to come.
Sensor fusion and computer vision technologies are top of mind for many brands, with 20% prioritizing AI-driven computer vision investments this year according to the Qu report. Several use cases are being tested, including camera-based drive-throughs to automate ‘knowing the guest’ and food preparation and packaging quality monitoring. These offerings provide real-time insights and support data-driven decision-making.
The Future of AI in Restaurants
As restaurant brands navigate the AI landscape, two guiding principles remain at the forefront:
- Always prioritize customer-centric innovations
- Support staff in delivering exceptional service
Brands should look for technology partners committed to developing intelligent solutions that streamline operations and elevate customer experiences.
Brian Crum is currently VP of Product at Qu, the first restaurant technology company to launch unified commerce for restaurants and the only POS company built to exclusively serve enterprise Fast Casual and QSR restaurants. Qu has been baking AI solutions into its foundational platform for over five years, providing the low-latency, high speeds that AI requires. Qu’s Notify app uses AI to provide real-time insights on the go. Prior to Qu, Brian worked with a number of companies creating platforms and products leveraging AI and machine learning, including Adapdix Corporation, Amazon Web Services (AWS) Industrial AI & ML services, Amazon Alexa Smart Home, and Microsoft.
