Artificial intelligence, or A.I., is becoming prominent around the world today. Just think of Amazon’s Echo, the smart device that sits on a table or dresser at home and provides everything from the weather to a music playlist to favorite recipes.
Some of the same forces powering Amazon’s Alexa technology are pushing the grocery store industry to adopt A.I. capabilities. As more consumers are focusing on convenience, they are gravitating toward A.I. applications that make shopping easier, quicker and more engaging.
Similarly, grocery stores are finding reasons to incorporate A.I. into their operations. For example, the predictive capabilities of A.I. may help to forecast inventory needs, determine products, promotions, prices and analyze customer behavior, loss prevention and other areas. The result of these external and internal forces is that A.I. is now playing a major role across the grocery store spectrum.
What A.I. is—and isn’t
With so much technology bursting on the scene, it’s easy to confuse one type with another. One common misconception is mistaking A.I. for predictive analytics, but the two technological models could not be more different. One way to differentiate the two: A.I. calculates results, learns from its errors over time and has the ability to simulate, whereas predictive analytics uses specific historical information to model future results.
While a predictive analytics tool can look at a specific set of defined data inputs—a single-purpose model—and support decision-making, it cannot make recommendations and determine decisions that drive storewide profits. The A.I. approach, in contrast, is based on analyzing all the available data and product relationships, simulating decisions around pricing, promotions and inventory forecasting, as well as all aspects of merchandise planning.
Applications for A.I.
The grocery and produce sectors are finding ways to incorporate A.I. in inventory management and food-waste reduction, theft prevention and marketing and promotion. While robotics is the most recognizable A.I. example in action, A.I. can be much more subtle and behind the scenes.
For example, grocery stores traditionally have struggled to accurately predict future demand for specific products, with forecasts often containing errors ranging from 40 to 60%. A number of sophisticated demand forecasting software product suites are currently available, but they have limitations as a result of technology and computing capacity. A.I. has allowed products from companies such as Daisy to achieve forecasting accuracies more than 10 to 15% over the standard, which was developed around 20 years ago. This type of prediction capability has an impact on lowering food waste on a large scale.
A.I. can also be applied in food retail marketing and promotion. A.I. can process operations data related to the point of sale and promotional history and take into consideration factors such as the relationships between products, seasonality and competition and provide regular promotional recommendations for various marketing channels. In theft prevention, A.I. can help detect checkout errors or even cashiers who avoid scanning at the grocery checkout counter.
It is apparent from decreasing prices that efficiencies are being realized and the grocery business has started to tap into the true potential of A.I. What is happening now is likely just a fraction of what is to come.
This post is sponsored by Daisy Intelligence
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