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AI in Retail: Separating the Steak from the Sizzle

Generative AI (artificial intelligence) has emerged as a hot topic in retail – and in various associated industries– this year. 

However, to borrow a popular idiom, AI has been “more sizzle than steak” this year, with many executives trying to pinpoint tangible business cases. 

Unlike the ill-fated Metaverse, AI boasts potential applications spanning a myriad of use-cases, from operations to consumer touchpoints.  

As we look ahead to next year, if you’re wondering how others are innovatively deploying AI, we have you covered. 

Many current AI implementations center on extracting information from centralized data sources or leveraging proprietary data to forge unique insights.  

For instance, Whoop (makers of fitness bands) recently announced a collaboration with OpenAI. This partnership enables members to glean tailored insights and feedback based on their personal data. 

Long-established examples of AI in retail, such as optimized forecasting, enhanced operations, and targeted marketing, have been around for some time.  

However, below are some novel ways companies have disclosed their AI usage, shedding light on its potential influence on retail in the upcoming year: 

  • Improved Productivity for Non-Store Employees: Announced by Walmart earlier this year, “My Assistant ” is a tool trained on corporate data, designed for non-store employees. It aids in expediting draft composition, serves as a creative ally, and can summarize lengthy documents. Other firms, like the consulting firm McKinsey & Co., have released similar tools to bolster non-store employees in their daily tasks. At Walmart, this comes after the rollout of tools for store employees, such as “Ask Sam,” which was introduced three years ago. 
  • Rapid Marketing Through Instant Image Generation: AI image generation, facilitated by platforms such as OpenAI’s Dall-e or Midjourney, is still nascent. Yet, the progress in the past year makes this technology ripe for broader acceptance. Smaller e-commerce businesses already employ AI-produced images on their sites. If this technology follows the trajectory of Clay Christensen’s renowned Disruption Innovation theory, its adoption by established retailers may be more imminent than anticipated. This is especially given the immense opportunities for productivity enhancements and tailored marketing for clients. 
  • Site-Specific Insights from In-Store Cameras: The computational and setup expenses for cameras facilitating in-store tech, like automated self-checkouts, are still significant. However, as these costs diminish and more applications surface, cameras collecting data to refine operations will become ubiquitous. For instance, Kroger is collaborating with Nvidia to harness in-store cameras, offering actionable insights on aspects like produce freshness for timely adjustments. Once these cameras are installed, a plethora of opportunities emerge, from minimizing stock shortages to dynamic scheduling based on foot traffic. 

While AI remains a highly touted topic for retailers, it’s crucial to temper expectations by looking at a longer-term (5-10 year) horizon, rather than year-to-year evaluations, especially given the data sharing concerns related to building Generative AI models.  

Although this year saw modest public-facing AI developments in retail, the drive to uncover areas for productivity and efficiency enhancement should persist, considering the vast potential of diverse applications. 

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