How AI Can Help Multi-Location Restaurants Prevent Another Health Crisis

What if restaurants could spot a health crisis before it spreads? With the help of AI, they just might.

McDonald’s burger patties may not have been the culprit of its recent E. coli outbreak, which the company quickly traced back to slivered onions, but customers temporarily took a step back from the quick-serve restaurant’s top product nonetheless. A nimble and comprehensive response to the outbreak minimized the damage to brand reputation, but that’s not always the case. With hundreds or thousands of locations and complex regional supply chains, many multi-location brands struggle to respond to health scares as efficiently as McDonald’s. (And McDonald’s still says it needs to spend over $100 million to recover).

For MULO brands, having a crisis response plan around an E. coli outbreak isn’t enough. Restaurants need to be proactive in preventing a crisis, and today, AI equips them with the toolkit to do so.

An issue bigger than brand reputation

The quick response from McDonald’s surrounding its E. coli outbreak included proactively pulling ingredients and regularly updating the public through press releases. Joe Erlinger, president of McDonald’s USA, said in a video, “Our partnership with public health authorities helped the McDonald’s supply chain team, the best in the world, get to work right away, and it has helped us get to the bottom of this situation.”

Still, the outbreak led to 90 cases of E. coli across 13 states, 27 hospitalizations and 1 death. While the McDonald’s team acted with full seriousness, and I give them my respect, a life is still gone. You can only imagine the detrimental effects of a less streamlined approach.

Take Chipotle, for example. The brand came under fire for an outbreak of norovirus and other illnesses lasting from 2015 to 2018 that sickened 1,100 people. The FDA fined the restaurant $25 million and said related criminal charges stemmed, in part, from the “highly contagious pathogen that can be easily transmitted by infected food workers.” Managers were overlooking the restaurant’s sick policy, encouraging people to come to work while ill and passing on the disease.

More recently, Panera Bread stopped selling its highly caffeinated “charged lemonade” only after two deaths and multiple lawsuits. They settled the first lawsuit, from the parents of 21-year-old University of Pennsylvania student Sarah Katz, in October. Customers and experts alike complained for years about the dangerously high caffeine content, reaching nearly 400 mg in the largest beverage options.

The scenarios above did more than damage brand reputations. They caused major health issues and in some instances, loss of human life. When it comes to managing food quality nad safety across dozens, hundreds or thousands of restaurant locations, chains need to take a more proactive approach.

Heard it through the grapevine

With AI-powered deep customer listening, restaurants can automatically flag early warnings about emerging problems like unsanitary conditions or unsafe food preparation practices. Much of today’s customer feedback exists as unstructured data, meaning restaurants need to move beyond traditional business intelligence and analytics tools to truly grasp what’s happening.

For example, before Chipotle’s norovirus outbreak made headlines, customers were already complaining online about unsafe food handling by staff and about getting sick after dining there. It’s a clear case for AI’s social listening capabilities, which could have helped Chipotle nip the outbreak in the bud and avoid long-term damage to brand reputation.

AI can analyze structured and unstructured data within reviews, customer surveys and social media chatter at scale for food safety-related feedback. For MULO leaders who can’t be in every restaurant to check in on food safety practices, this is invaluable.

Subsequently, restaurants can react to this feedback, resolving problems before they metastasize. Reactions can be as serious as pulling ingredients and launching official investigations, or as preventative as updating manager and employee trainings around sick time, updating and reinforcing policies, and incentivizing employees to practice thorough food safety.

A lot of the impact boils down to timing. Outbreaks are not wait-and-see situations. AI speeds the process up, makes customer listening more thorough, and enables companies to tap all the corners where their customers provide feedback.

Be a good listener

Health risks often stem from situations that harm a brand, even without an outbreak. Customers don’t tend to like dirty facilities or unsafe food handling conditions, even if they don’t cause serious illness. But with multiple locations, it can be hard to keep everything in line. That’s part of the reason why even the top-ranked restaurants get complaints.

Take Wingstop, for example. A recent AI analysis of its Google Reviews shows customers complaining of restaurant and restroom cleanliness. Knowing this, Wingstop can address the issue head on, improving cleanliness and training employees to adhere to food safety requirements and best practices.

From E. coli to norovirus and beyond, the next food-related health outbreak could be preventable. This is thanks in part to AI, which allows restaurant leadership to listen to customers in more places at once and spot emerging issues faster.

The early warning signals are there — it’s up to brands to hear them and act accordingly.