Challenges of Automation in Retail
Retail Tech Insights | Monday, January 02, 2023
The retail industry benefits from automated solutions but must also account for its challenges.
FREMONT, CA: The retail industry generated USD 5.4 trillion in sales in 2019 alone, making it the largest industry in the United States. Many Americans depend on retail giants such as Amazon, Walmart, and Target.
Retailers can increase profits by increasing in-store and online sales and improving supply chain efficiency. Given all of these potential benefits, a growing number of retailers are excited about artificial intelligence (AI), technology that has the potential to accomplish all of these goals, and more.
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In mobile shopping, AI can improve personalization, help maintain a better in-store experience, and enhance payments, customer service, logistics, and inventory management.
With the increasing adoption of AI solutions in retail, retailers face challenges while streamlining their businesses.
Customer-facing and non-customer-facing artificial intelligence: Customer-facing applications interact directly with customers, and this is the primary risk factor that retailers should consider when adopting AI. Applications that aren't customer-facing, such as the Tally robot at Giant Eagle store, are primarily used for tasks customers don't see.
A retailer must balance the risk of possible customer backlash and the benefit for the business if AI is implemented. Customer-facing AI applications are riskier to implement because customers can see them. Therefore, AI applications that have no direct customer interaction are more likely to be adopted by retailers.
Application value: According to predictions, AI will have the greatest impact on retailing out of 19 industries. There are several factors responsible for this potential value. Customers frequently interact with retailers, providing them with purchase histories and demographics. Retailers collect a great deal of data that AI can leverage, and AI applications can deliver high-value predictions and recommendations when AI analyzes the sum totality of such data.
Ethical impacts of artificial intelligence: As AI applications become more sophisticated, they can identify specific people based on limited information. Even if a customer does not provide their full name or credit card information, AI can still identify that person based on broader factors such as age, zip code, or gender.
It is also important to consider the sensitivity of consumer data. AI applications are likely to have higher levels of identifiability and sensitivity of customer data which may raise customer privacy concerns. As privacy concerns are primarily associated with customer-facing applications, retailers may be hesitant to adopt them.
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