3 Ways AI Will Disrupt QSRs

3 Ways AI Will Disrupt QSRs: Perspectives from Susan Sly

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Susan SlySusan Sly, Co-Founder and Co-CEO of Radius AI (RAI), grew up in the restaurant business in Canada. That’s one of many reasons she has taken a humanistic approach to artificial intelligence, robotics, and technology, believing that if AI is properly executed it can empower retail, QSR, C-store, and other multi-location employees to become confident and fulfilled superstars in their jobs.

I recently attended a panel that featured Susan. The moderator, Andi Huels, is RAI’s Head of Global Business Development. These forward-thinking professionals are among the small minority of women leaders in AI.

They take a very humanistic view of the application of AI in multi-location restaurants and other brick-and-mortar businesses to:

  1. Deliver better customer experiences
  2. Reduce food waste
  3. Improve customer and employee safety and security

RAI has already deployed 30+ use cases in the QSR world for how AI can streamline operations, improve business morale, and build a brand. Here is a small sample:

  • Sly pointed out that using AI to track wait time and then gamifying competitions across locations to improve it can get entire teams to embrace and respect new technologies.
  • Voice AI will enable customers to order in their native languages and get better and faster service.
  • AI applications are especially powerful in the drive-through environment, where speed of service, convenience, and safety all come into play. License plate data can be used to construct loyalty programs.
  • Inventory management is facilitated through AI. Sly says one brand saved about $300K by being able to predict order behaviors and adjust inventory accordingly, radically reducing food waste.
  • Equipment fails like broken gas pumps, or open refrigerator doors can result in customer frustration and lost revenue. AI can alert operators immediately so they can take action.
  • Computer vision enables stores to spot theft, slip-and-fall accidents, and other in-store incidents with amazing speed. Existing security cameras can be used for this purpose, reducing the investment brands need to make.
  • AI can also save millions of dollars in testing new products or service concepts. Rather than rolling out a new idea and then waiting to read results, multi-location operators can get immediate and accurate usage data and pull the plug immediately on innovations that aren’t working.
  • ChatGPT and similar applications can be used to write and standardize job descriptions, proposals, and other critical documents.

“No organization is too small to implement AI,” asserts Sly. Smart restaurants will use data to make smarter decisions, put the right people in the right jobs, and create each location’s footprint and processes to respond to the intelligence they get from AI.

Sly stresses the importance of interoperability in AI. As QSRS and other multi-location businesses seek to incorporate AI, they must ensure that all their technologies can “talk” to each other, ensuring that data is accurately captured in real-time at all contact points.

In closing, remember that artificial intelligence can only work well in the QSR environment if combined with and designed to serve human needs. AI may be much simpler and people-friendly to deploy than many media outlets would lead restaurants to believe.

Nancy A Shenker, senior editor with Street Fight, is a former big brand (Citibank, Mastercard, Reed Exhibitions) marketing strategist and leader. She has been featured in Inc.com, the New York Times and Forbes.
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