RTN's AI Share Group Spotlights Real-World Use Cases
Restaurant Technology Network’s (RTN’s) AI Share Group on August 1 showcased hot AI solutions already being leveraged in the restaurant space.
RTN Co-Founder Angela Diffly kicked off the call and facilitated short speaker chats to offer a glimpse into real-world use cases of AI, including voice, sales forecasting, payments, cybersecurity and more.
Voice AI
ConverseNow's Ben Brown shared how the ConverseNow solution automates drive-thru and phone orders for enterprise restaurant chains including Domino’s and Wing Stop. ConverseNow built its virtual assistant back in 2018 "before AI was cool," Brown said.
Recently the company integrated LLMs (large language models), "so that the conversations are more natural and free-flowing" than they previously were. Its "tremendous results" include doubling restaurant output during peak hour times (Hello, automated upsell!) plus higher customer satisfaction scores. "Customers enjoy speaking with the AI. They like how accurate their orders are," Brown explained.
Swipe Right for Last-Mile Logistics
Dlivrd CEO Chris Heffernan compared the last-mile logistics company to the dating-app Tinder for delivery drivers. Frederick, the name for its AI solution, matches brands’ orders from third-party delivery marketplaces and native online ordering channels with its delivery drivers.
Currently in use in 150+ U.S. and Canadian cities, Dlivrd’s Heffernan said the company doesn’t just quote deliveries to a crowdsourced delivery fleet. Instead, it asks thoughtful questions with the delivery driver’s best interests in mind. “When we are onboarding drivers onto our platform, we ask them… What kind of delivery equipment do they have? What kind of vehicle do they drive? What area do they want to deliver in? How much money do they want to make?” This information is built into the driver profile.
The AI (Frederick) analyzes order size, driver availability and preferred delivery area, then matches accordingly, resulting in maximized earnings and efficiencies for the restaurant and optimal delivery driver routes and experiences.
Accurate Forecasting with Machine Learning
Branden McRill, Co-Founder and CEO of 5-Out, shared how its ML (machine learning)-based forecasting solution sits on top of the restaurant's tech stack and doesn’t require integrations. 5-Out combines data like historical sales, weather forecasts and calendar of events to minimize restaurant forecasting errors associated with revenue, covers and labor costs.
Better GMs, Better Results with Computer Visioning
Daniel Dreymann Chief Product Officer & Head of Engineering, Agot.ai, explained how computer vision AI is being leveraged by GMs to perform at a higher level to accelerate table turns, reduce food waste and ensure SOP compliance.
Agot’s computer vision AI integrates with the restaurant's existing tech stack. The dashboard presents data insights to prompt management and guide staff in real time. Is a busy-line cook forgetting one of the key ingredients? Are diners leaving and tables need bussing? This tool helps identify these types of situations in real time, saving money and resources.
Contactless Payments in the Drive-Thru
Derek Canton, CEO and Founder, Paerpay, shared how the company is helping QSRs and full-service restaurants use AI for ordering and attaching payment / loyalty to the guest experience. Paerpay has partnered with Valyant AI, known for its conversational AI in the drive-thru.
Canton shared how Carl’s Jr. locations are facilitating the guest experience in the drive-thru: after using AI voice ordering, the guest is prompted to scan a QR code for payment. It works similarly for phone orders. The voice AI takes the order and the link would be sent via SMS for payment. Paerpay fully integrates into the POS, loyalty system and all the other components.
In addition to streamlining processes and automating upselling, the Paerpay solution is a goldmine for data, Canton explained.
Avatars
Craig Allen Keefner, Kiosk Manufacturers Association, pointed to a wide variety of avatars -- from human-like avatars to animated mascots – being used in kiosks and beyond. Imagine Ronald McDonald taking your drive-thru order, or a celebrity's likeness being used to engage with customers and upsell their favorite meal deal.
Cyber-Security Concerns
The more AI technologies are introduced into restaurant environments, the higher the risk posture, explained Courtney Radke, CISO, Fortinet.
Citing IBM research, the number of data breaches continues to rise. On average a data breach costs about $4.45 million across all sectors, Radke explained. The average time to detect is 212 days, and the average time to mitigate is a little over 70-75 days.
Bad actors are leveraging AI to streamline and increase their throughput as well. “It used to take days, maybe maybe longer, to craft a really good attack campaign against an organization or a person. Now it takes maybe a day, sometimes minutes, because all you have to do is pump a little bit of data in, and then the engine just pulls the rest of it in a tailored campaign,” he said.
Restaurants should leverage ML and AI in their cyber management practice to augment staff, increase speed of response and ultimately, to prevent attacks, Radke stressed.
A Call for Ethical AI
Jon Stine, executive director of the Open Voice Network, a non-profit, vendor-neutral arm of a Linux Foundation, proposed working with the RTN community, alongside Jay Ruparel, Co-Founder and CEO of VOICEPlug AI, to create a guide for ethical AI for the restaurant industry, addressing key questions and concerns. In a new era of generative AI, the ethical and legal risks are multiplying, Stine explained.
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