Bringing artificial intelligence (AI) into the full-service restaurant space doesn’t mean the advent of robot waiters. It’s 2020, and AI—or what might be more accurately described as machine learning—is helping restaurants improve efficiency, understand customers, reduce waste, and more, with nary a walking, talking robot in sight.
“AI is more approachable than people think,” says Joe Vogelbacher, president and cofounder of Sugar Creek Brewing Company in Charlotte, North Carolina, which has used IBM’s AI platform Watson to drastically cut down on waste, among other innovations. “If you’re not experimenting with this stuff, in 10 years you’re not going to be in business because it’s going to be a necessity to stay in touch with your customers.”
In the quick-serve sphere, AI has become increasingly popular, with brands relying on the technology to serve guests quickly and consistently. At full-service restaurants, where customers pay for a unique experience from a server or sommelier, AI assists in tasks beyond the parameters of the main dining experience.
“We see AI being adopted by these restaurants not necessarily during the dining experience itself but before and after the actual visit,” says Fredrik Tunvall, who manages all go-to-market–related activities for IBM’s Watson Assistant.
Watson can provide customers with quick answers regarding food and services at any time of day. Tunvall says he is particularly interested in how AI can help with personalization and provide unique experiences for guests. “What if a restaurant could use a virtual assistant to gather customers’ preferences prior to the visit to make sure their dining experience is exceptional?” he says.
Allset, a reservations, pre-ordering, and prepaying app, already resides in this future of AI-driven dining personalization. By accruing preferences and order histories, Allset uses machine learning to provide customers with custom suggestions about which restaurants to patronize and even which dishes are available.
“No one knows better what a customer is going to like than the customer themselves,” says Stas Matviyenko, Allset’s founder and CEO.
Recently, Allset used this technology to encourage customers to choose healthier options. The app’s reservation software also helps manage tables efficiently, making the predictive technology useful for even white-tablecloth restaurants.
“You can know that this kind of customer takes this long at the restaurant, so you can make waiting more productive and have fewer empty tables,” Matviyenko says.
AI can also improve the hiring process. Fountain is a fully customizable platform for the recruiting and hiring process that’s popular with several national quick serves and full-service restaurants. Among services like posting jobs and running background checks, Fountain’s AI also homes in on qualified candidates based on past experience, response time, and communication levels.
“Most hiring organizations lose large numbers of candidates because they aren’t able to quickly identify the best applicants from the flood of many unqualified applicants [or] quickly get them through the process,” says Fountain COO Micah Rowland. “This is true of customer-facing jobs as well as back-of-house jobs, and it’s one of the many areas Fountain is helping our customers to succeed in a tough environment for hiring organizations.”
AI can also prove useful in improving back-of-house business. IBM’s Watson helps restaurants understand the quality of food by monitoring the supply chain, Tunvall says. Furthermore, gathering and analyzing big data can lead to more streamlined operations and offer a roadmap to upcoming trends.
To that end, Sugar Creek Brewing Co. has used Watson to collect and scrutinize more data on its beer production than would ever be possible for a human employee.
“We historically have binders and binders of data collected by hand. … Now you can search relatively quickly in a digital database and get the information you’re looking for,” Vogelbacher says. “The Holy Grail is to have Watson give you insights into that data before you even look at it.”
This data—plus the connections and patterns Watson finds within it—have aided Sugar Creek in preventing excess foam or oxygen from getting into beer on the bottling line. This seemingly small change has broad-reaching effects: It cuts down on waste and provides a detailed understanding of what’s happening in the brewery’s back of house.
For Vogelbacher, big data is only the beginning of what he thinks Watson can do for Sugar Creek. He has plans to use Watson’s data, combined with social media reviews, to develop recipes based on customer preferences; to manage supply in the taproom; and to change prices in real time based on customer demand.
“In today’s marketplace, having access to your data is critical,” he says. “We were the sixth brewery in Charlotte and now there are more than 60. Watson has helped us stand out. You can do things the traditional way or look for an edge.”
