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AI Accelerates the Pace of Innovation in Test Labs

There is no question that COVID accelerated restaurants’ digital transformation with the need for alternative service and delivery models such as online ordering and take-out, curbside pick up and delivery, touchless ordering, and payments, just to name a few.


Restaurants were among the hardest hit industries with hundreds of billions in lost revenues and an estimated 100,000+ restaurants closing their doors permanently with millions of furloughed or laid-off restaurant workers.


Forced to adapt and rapidly innovate overnight to survive, restaurants realized technology that used to be a “nice to have” was now, and is now, a “must-have”. Full-service restaurants began offering takeout, delivery, and meal kits. Quick-serve restaurants had to change processes to accommodate increased demand with newly added safety protocols. The lines blurred between restaurant structures giving rise to hybrid models across dine-in, fast-casual, quick service, and casual with every restaurant vying for the new take-out and delivery business.


And the reality today is that although demand for food services may have dropped initially at the start of the pandemic, it has rebounded quickly and now many restaurants are facing increased demand amid massive labor shortages.


Most of the large Fast Casual and QSR chains have innovation labs or innovation kitchens, or innovation initiatives within their actual operations in the field. Consumers have changed how they select, engage, and interact with restaurants and the brands that realize this and have adapted to survive will grow. What has not changed, though, are consumers' expectations.


To meet and exceed their customer’s expectations, restaurants are looking to technology and artificial intelligence for solutions to new problems such as long queue lines, new food preparation processes, server inefficiencies, and the biggest challenge yet – labor shortages.


Visual intelligence, or computer vision as it is more commonly known, is growing in popularity among these large fast-casual and QSR innovation initiatives. Because computer vision can be quick, easy, and affordable, and does not require significant employee resources, it is a no-brainer for these innovative organizations.


Computer vision provides the accurate, real-time data and insights necessary for innovative organizations to accurately capture key operational metrics such as speed of service, labor productivity, labor utilization, product preparation, product layouts, impact on staffing plans, scheduling, and more.


By capturing this real-time and actionable data straight from innovation initiatives, brands have the critical data and insights they need to explore new and innovative ways to streamline operations, improve the customer experience and enhance operational efficiencies.


An example of one such use case is for the innovation lab of a large hamburger chain that partnered with Nomad Go to find innovative ways to shorten queue lines and streamline food preparation. Accurate and reliable data was collected in real-time including a 360-degree view of food preparation and service times so that they could understand inefficiencies and areas for improvement.




With the help of visual intelligence, this leading QSR brand was able to improve throughput and increase labor efficiencies while optimizing the new operations processes being tested in the innovation lab. With rollouts to the market, results included improving customer satisfaction ultimately leading to increased customer retention, sales, and keeping their margins in check. Read the full story.


As food services companies evolve to be more adaptive and innovative, the majority will be joining these leading brands and tapping into computer vision and artificial intelligence to improve their operational efficiencies, guest experience and support the need to innovate and grow.


For more information on how computer vision helped this large hamburger chain accelerate innovation, read the success story blog post.







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