When you hear the words artificial intelligence (AI), what comes to mind? Robots gone rogue? Machines with brains? Will Smith in a black beanie and leather jacket?
The media and popular culture have done us a bit of a disservice over the years when it comes to AI by perpetuating the idea that we cannot control our own creations and will eventually become a society that serves technology (instead of the other way around). This in turn has produced false expectations and fears around gadgets, robots, and even restaurants (yes, restaurants!) that are rooted in fiction, not fact.
Because AI and machine learning technologies show no signs of slowing down, we need to think critically about the implications of these advancements. Everyday there seems to be a new article heeding the warning that robots will replace restaurant workers as they infiltrate every facet of the foodservice industry. However, these claims are not only hyperbolic, they’re also impractical, grouping all restaurants under a fast-casual or fast-food umbrella.
So which is it: man or machine? The good news is you don’t have to choose.
What is AI For Restaurants?
It’s time we give the old “man versus machine” axiom a 21st century update: man plus machine.
If chess players are ready to embrace AI-human hybrid intelligence, so too should restaurateurs. If we can rewrite the narrative when it comes to AI for restaurants, we can empower staff at all levels to make smart, better-informed decisions.
Understandably, being repeatedly told that restaurants should brace for sudden, significant, and shocking change creates anxiety for owners, managers and, of course, staff. How can food service workers possibly compete with technologies specifically designed to outsmart us? Is the industry as we know it doomed?
The short answer is no.
The long answer is below.
But, before we tackle the specifics of how AI and machine learning have, may, and will affect the restaurant industry, let’s first discuss what these terms mean.
Artificial intelligence is as it sounds: intelligence that does not occur naturally. Rather, it is fabricated or simulated to allow computer systems to mimic intelligent human behaviors. AI is static in that it can ingest, but not react to, real-world information. Your friends Siri and Alexa? AI by another name.
Machine learning, on the other hand, cranks things up a notch. This branch of computer science involves training computer systems to distinguish, anticipate, and respond to data patterns through complex statistical algorithms. Netflix, Spotify, YouTube… these are just a few systems that listen, learn, and serve up more of what you’re liking.
Computer scientists work tirelessly to make these highly technical pursuits accessible to the general public. We see evidence of this in a number of industries including healthcare, finance, entertainment and, increasingly, retail. From recommending television shows or books you might like to predicting emergency department admissions and personalizing workouts for your body type, AI and machine learning are reimagining how we live, work, and play.
And we’ve only just scratched the surface. In a discussion paper published earlier this year assessing “both the practical applications and the economic potential of advanced AI techniques across industries and business functions,” McKinsey Global Institute (MGI) predicted AI will generate up to $2.6 trillion for marketing and sales, and $100 billion in retail customer service management. These numbers look great for the economy… but are they bad for business?
AI’s appeal becomes even more self-evident when considering the foodservice industry’s current landscape. With restaurant employee turnover rates consistently in excess of 70 percent, a steady stream of local labor law changes, and ongoing struggles to recruit and retain restaurant employees, the opportunity to leverage technology to reduce some of these challenges (and costs) rightfully sounds pretty darn good.
The problem, it seems, is twofold.
First, writings about AI in restaurants tend to fixate on three things: robots, delivery bots, and chatbots. Yes, there is indeed a burger-flipping robot (who has since been temporarily “retired”), Domino’s absolutely did deliver a pizza by drone, and yes, KFC is exploring face-recognition technology to serve up a personalized customer experience, but these are exceptions—not the rule.
The second problem is one of assumptions: namely, that the average restaurateur has the resources and even interest in going deep into AI or machine learning. The above examples might save money and create value in the long-run – although the ROI is still largely undefined– but the short-term costs will remain unattainable for the vast majority of restaurateurs until such time when it becomes part of the restaurant base model.
However, this doesn’t mean that AI is entirely out of reach for small- and medium-sized businesses in the restaurant industry. There are still ways to make AI and machine learning work for your restaurant, helping you to empower operators and staff and run a profitable, efficient establishment.
Read More: 22 Ways to Delight Your Guests in 2018
How to Leverage AI For Restaurants
1. Get listed.
Where once on-demand ordering was considered cutting edge, today customer service is being automated and streamlined even further.
Halla is a great example of an application challenging what is now the norm. The recommendation engine amalgamates a number of food delivery applications to present relevant cafes and eateries based on a user’s location and predetermined “taste profile.” Ensuring your restaurant is available via these services optimizes your chances of making the cut as a “restaurant you might like.”
2. Keep up with your customers.
Say2Eat is another AI agent that ensures restaurant customers are “never more than 30 seconds from their next order” by conveniently connecting with them on their preferred platforms (Twitter, Slack, and Google Home are just a few examples).
Millennials and especially Gen Zers are much more inclined to open their wallets if they feel they are part of a two-way conversation; making use of tools that keep the conversation lines open not only fosters affinity and loyalty, but also contributes to increased foot traffic and revenue.
3. Go big for big data
You may not realize it, but some of the software solutions you’re currently using – like your employee scheduling software or point of sale system – contain a goldmine of information that can help you run your restaurant like a well-oiled machine.
These systems effortlessly manage and monitor high volumes of data on the daily; some even predict things like labor needs customer behaviors, food quality, and inventory counts, taking the guesswork out of what to do when. I
n the near future these same applications will be able to use the data your restaurant generates to do things like create labor-optimal employee schedules, or use your sales data to predict which items to sell and increase your profits.
4. Get on board with voice search
With 27% of the global online population using voice search, and nearly 40% giving preference to voice over smartphones when seeking information about a restaurant, if there’s one AI movement to get behind, it’s voice commerce.
Restaurants can easily create “skills” for tools like Amazon Alexa that can help people instantly order without ever having to lift a finger. Grubhub, for example, has enabled this technology to allow its users to place fast, hands-free orders.
The Future of AI For Restaurants
The bottom line for the vast majority of today’s restaurateurs with regard to AI is this: don’t sweat it, but also don’t forget it.
You should keep a watchful eye on AI and machine learning trends and breakthroughs, but don’t add unnecessary stress to your plate trying to squirrel away cash to afford a fleet of $50,000 self-serve kiosks like McDonalds. Let the early adopters do the dirty work, but remain vigilant of which technologies might serve you, your staff, and your customers in the future.
For now, you’ll find the greatest success in focusing your energies on learning from your in-store data and applying what you’ve learned to the ways you and your team work.