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When Artificial Intelligence (AI) meets 3 retail industry pain points – The Enterprisers Project

At the beginning of 2020, artificial intelligence (AI) was predicted to become what some experts called a “key ingredient” technology across many industries over the next decade.
Fast-forward to today, and interest in AI is surging even more. As we are now deeply entrenched in the COVID-19 pandemic, the need to enhance manual business processes with more automation through AI has hit many industries with urgency, but especially retail. About two-thirds of executives surveyed by McKinsey in June said they had accelerated the implementation of robotics, artificial intelligence, and other emerging technologies in response to COVID-19.
The retail industry has a unique opportunity to learn from consumer behavior this year and implement AI solutions that can meet shoppers’ needs in the long term. From enhancing store intelligence to real-time inventory management, AI has the power to help companies withstand further shifts.
However, implementing AI brings challenges. Many organizations look to AI for its promising efficiency gains, only to find they lack the basic automation, product identification, and data quality best practices that are essential for success.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
Step one for many retail CIOs exploring AI is to leverage global data standards in their business processes to support digital transformation, as these provide a necessary bridge between the physical product and the data associated with it.
Here are three ways retail CIOs can leverage AI in combination with standardized, structured data to solidify their relevance, even after COVID-19 is no longer a driving force in the economy.
1. Supporting consumers with useful data
Retail CIOs can use AI to meet consumers where they feel most comfortable. The physical and digital shopping experiences are merging, creating an opportunity to provide consumers with the data they need to shop safely and efficiently.
Retail CIOs can use AI to meet consumers where they feel most comfortable.
Even as online sales of grocery delivery and pickup in the U.S. have surged from $1.2 billion in August 2019 to $7.2 billion U.S. in June 2020, according to Statista, the vast majority of grocery shopping still takes place in traditional brick-and-mortar grocery stores. Consumers now want to make the most of fewer shopping trips with as little contact as possible. This presents retailers and brands with a way to extract more valuable data from consumers to create future engagement opportunities.
For example, a startup called Locai uses artificial intelligence to parse recipes for ingredient information, enabling consumers to plan meals more easily by finding the items they need online and in store. AI that anticipates consumer needs and makes it easier to move between digital and in-store experiences will continue to grow, as long as the data being served up to consumers is accurate and complete.
2. Analyzing demand patterns
CIOs can use AI to analyze demand patterns and ensure consumers have what they want, when they want it. The pandemic has caused consumers to try new brands and stores that they had never tried before – 75 percent of Americans had changed how they shop as a result of the pandemic, according to the June McKinsey consumer sentiment study. Retailers and brands are leveraging new solutions to analyze store activity and help automate processes like on-shelf availability to keep up with pantry-loading shoppers.
Retailers and brands are leveraging new solutions to analyze store activity and help automate processes like on-shelf availability to keep up with pantry-loading shoppers.
For example, provides an AI-based spatial intelligence platform that evaluates store traffic patterns and how customers interact with products in-store. The company sees an opportunity to enhance the in-store experience and deliver targeted promotions and other loyalty incentives to consumers as they shop.
AI is most useful in the analysis of what already exists, so it’s important for retailers not to rush or cut corners when packaging or labeling items. If it’s not clear if the store has 100 cases, pallets, or individual bottles of water, for example, the AI will not present an accurate picture of what’s available and where it is located.
3. Reimagining fulfillment
Retail CIOs are collaborating with supply chain executives to accommodate the boom in online ordering, curbside pickup, and home delivery. According to data from Brick Meets Click, more U.S. households are using delivery and pickup services to satisfy all of their grocery needs – a shift from previous shopping patterns where shoppers stuck to only ordering shelf-stable items to supplement shopping in person.
In an effort to gain velocity in fulfillment, AI has been playing an increasing role in inventory management. However, some retail operations lack specificity in their product data management. AI needs as much information as possible to be successful, but if all product details are not set up, consumers may be presented with incomplete or inaccurate information. CIOs should focus on unique and consistent product identification for each flavor, color, or style.


For example, a store that rushed to place inventory online during the pandemic may have not accounted for all 27 different flavors of ice cream it carries. An AI that reads ice cream only as either chocolate or vanilla cannot accommodate growing consumer requests appropriately because it is not being given sufficiently specific data.
Ultimately, emerging AI applications can support consumers in the next normal if they have a data foundation that is accurate, complete, and able to be accepted universally by retailer systems. With the goal of creating value from the data it generates, AI will scale with the dedicated collaboration of brands, retailers, and technology providers to move the technology forward effectively.
[ Want lessons learned from CIOs applying AI? Get the full HBR Analytic Services report, An Executive’s Guide to Real-World AI. ]