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How AI Helps Reduce Employee Stress While Improving Productivity, Satisfaction

People are still the connective tissue that make QSRs operate smoothly, and that will remain true even as more AI-powered technologies are introduced in restaurants. QSRs need to invest in the latest technologies not so much to replace workers, but to attract them.
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By now – if you had listened to generative AI’s most fervent believers – technology should have taken more quick service restaurant industry (QSR) jobs, or the prospect of this happening should at least feel more imminent. And yet, real people continue to take and fulfill orders, handing food from the same drive-thru windows where signs advertise free college tuition and hiring bonuses. Instead of people looking for work, restaurants are still looking for people. 

The fact is that people are still the connective tissue that make QSRs operate smoothly, and that will remain true even as more AI-powered technologies are introduced in restaurants.

The reality of cutting-edge technologies 

Where AI is best positioned to make a difference is taking over predictable – but not necessarily manual – tasks. Dunking the french fry basket is an easy task that automation could have taken over handling decades ago. What’s more difficult, and which AI is especially well-equipped to solve, is how many fries to prepare and when to dunk them.

While QSR industry prognosticators have often highlighted manual tasks as the ones that make QSR jobs unfulfilling, it is equally true that decision-making also stresses today’s employees. Did I prepare enough chicken for the lunch rush? Am I staffed appropriately for the week? Did I hear that order accurately? What order goes to which car? The stress these decisions cause workers is underestimated. 

In a competitive labor market, QSRs need to invest in the latest technologies not so much to replace workers, but to attract them. It is easy to see a future in which workers gravitate towards restaurants with technologies that make their jobs easier. Of course, these technologies can also increase productivity and drive more sales.

Here are my top picks for attractive, near-term, AI-powered QSR investments that delight workers and CFOs alike.

Predictive everything

Restaurants with advanced POS systems collect too many data points not to start utilizing the full power of predictive analytics. Data has been used to improve inventory management for years, but restaurants can now realize many more benefits, from analyzing historical trends at a particular location to weather reports and how similar conditions have impacted drive-thru orders in the past. These insights can inform front and back office functions, from food prep to staffing levels. 

Predictive technology can also create smart upselling opportunities. For example, digital menu boards can serve up bespoke deals tailored to specific vehicle types – for example, kids meals for minivans – or recommend upsells based on the content of what has already been ordered (sometimes it takes a nudge to order dessert!).

AI simplifies the drive-thru order taking process

Conversational voice bot ordering has made every “drive-thru of the future” prediction for the last decade at least, but adoption has been slow as QSR brands wade slowly into this technology.

The next step that many restaurants will make is investing in speech-to-screen technologies, which have gotten significantly better. These solutions feed a person’s voice order directly into a POS to automate the ordering process. QSR staff working the drive-thru will merely oversee the automated order to ensure accuracy. They may ask questions and adjust the order if there are anomalies or mistakes, but the POS will do the hard part. This takes an immense amount of stress off of the worker, while improving speed and accuracy. 

Cameras power POS, digital menu boards and the kitchen 

Ask anybody who has worked a drive-thru and they will agree that getting the right order to the correct car is a perennial challenge, especially as restaurants add lanes to meet post-Covid consumers’ desire to stay in their vehicles (CosMc’s four lane drive-thrus, which Xenial supports, is the latest example).

New “camera aware” software can leverage AI-powered cameras to recognize car types and colors, rendering a digital representation of vehicles in the drive-thru. For workers, this takes the guesswork out of fulfilling orders. Today, many restaurants include the vehicle make and color on a printed receipt, but cameras take this technology to the next level so that workers don’t have to input this information, enabling them to fully focus on serving the customer (your line busters will thank you). 

But that is just the beginning. Camera-based technologies integrated with a POS can detect the number of cars in the drive-thru line and turn that information into an anticipated number of chicken nuggets and fries needed to fulfill expected orders. When integrated with robotics, this could dramatically improve QSR jobs, easing the decision-making stress on staff.

Getting the technology right

Sometimes technology advances slowly for unavoidable reasons. Even investments with a clear ROI are often complicated to execute and take years of integration work. 

Today, however, macro factors like inflation and competition for workers are accelerating adoption. QSRs are focused on both the employee experience and productivity because workers are harder to find and more expensive to keep (ultimately, both impact the consumer experience, too). Technology that relieves pain points – like ordering, payment and order prep – not only improves operations but also employee recruitment and satisfaction.


Xenial's Chris Siefken
Chris Siefken Head of Technology, Xenial

About the Author 

Chris Siefken is Head of Technology, Xenial, a Global Payments company.  He lives at the cutting edge of innovation, leading teams that make the technology used by recognizable enterprise restaurant brands around the world. He has approximately 20 years of technology experience and earned an applied philosophy degree in computer science from St. Andrews University.

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