Top 10 gen AI use cases in the retail industry

A quick-start guide to understanding AI opportunity areas to satisfy shoppers, grow sales, improve operations, and increase productivity 

Predictive and generative AI offer retailers tremendous benefits, but it can be challenging to determine how best to realize those benefits. This is true whether retailers are beginning their AI journey, accelerating existing efforts, or putting the technology to work against new business challenges.   

The reasons for uncertainty about the path forward are due to several factors. To begin with, there is wide variation among retailers’ competitive situations, strategic priorities, and technological sophistication. Then, there is the reality that predictive and generative AI are advancing rapidly and contributing to an environment of AI exuberance. At the same time, there is so much hype around AI that it can be challenging to separate unsupported claims from reality. 

While these issues are certainly real, retailers can move forward with pragmatic plans to capture the value that prudent AI adoption can unlock. SymphonyAI is providing guidance to businesses with insights, tools and forums. For example, the online panel discussion with industry AI experts, “Cutting through the AI hype to unlock true enterprise value” provided clarity on this subject. SymphonyAI is directly equiping retailers with the tools and information they need via ongoing Enterprise AI Bootcamps with content geared to companies’ specific needs. 

While retailers’ needs vary, certain business challenges are common throughout the industry. In this blog, we examine some of the most prevalent use cases where predictive and generative AI are already having a major impact, including:

1. Assortment optimization: Generative AI can analyze vast amounts of data, including sales history, customer preferences, market trends, and competitor offerings, to generate optimized product assortment recommendations. It can suggest which products to add, remove, or modify in the assortment to maximize overall category performance. For instance, it might recommend introducing a new product line to fill a gap in the current assortment or phase out underperforming items. An especially useful capability is “’what-if” scenarios to predict the impact of assortment changes on overall sales and profitability. 
2. Localize assortments: Generative AI can rapidly create tailored assortments for different store locations, clusters, or regions based on demographics, spending behaviors, pack size preferences, and different flavor profiles of food items. It also predicts the impact of such changes to give retailers confidence in the outcome of their actions.  
3. Planogram automation: Generative and predictive AI can create recommended planograms for optimal sales success by incorporating factors such as product dimensions, sales velocity, profitability, supplier agreements, transferable demand, and cross-sell opportunities. This ability to quickly create planograms that are store-specific, space-aware, and easily updatable is a game-changer for category managers.   
4. Space management: Generative AI can create virtual store layouts, allowing retailers to test different configurations without the cost and disruption of making physical changes. It can simulate shopper behavior in virtual environments, predicting the impact of layout changes on sales and customer flow while accounting for variations in store configurations and prototype sizes that are common throughout the retail industry. 
5. Planogram compliance: AI-powered computer vision ensures retailers derive the full benefit of the time and energy invested in making optimal assortment and space allocation decisions. Real-time images of shelves captured via AI-powered computer vision help retailers efficiently see what the naked human eye can’t assess as quickly, including whether a shelf that appears correct because it is fully stocked is actually in alignment with the proper planogram and vendor promotions. 
6. On-shelf availability: Computer vision is great for planogram compliance but does double and triple duty by identifying whether items are in stock and priced accurately. Both on-shelf availability and planogram compliance are hugely important use cases for every retailer, and the ability of AI to provide measurement and recommendations are major benefits that haven’t been heretofore efficiently solvable, regardless of the labor investment. 
7. Demand forecasting: AI transforms supply chain management by dramatically improving demand forecast accuracy. That in turn leads to a cascade of benefits throughout the business. With a solid master data management foundation, AI can analyze historical sales data, market trends, weather forecasts, upcoming events, and other factors to more accurately forecast demand across different product categories and locations, generating much more granular accuracy than traditional statistical models. AI supports “what if” scenario planning essential for supply chain resilience. With optimized inventory allocation plans, retailers know when and where to stock specific products to minimize both stock outs and overstock situations. In addition, plans can be adjusted in real time as market conditions change or disruptions occur. 
8. Mass personalization: The dream of customer-specific marketing becomes a reality with generative AI’s ability to analyze vast amounts of shopper data and identify insights that lead to personalized promotions and product recommendations. As a result, retailers improve shopper satisfaction because customers receive highly relevant promotions, which lead to increased basket sizes, trip frequency, and overall satisfaction. 
9. Data monetization: Predictive and generative AI allow retailers to develop alternative revenue streams through data-driven collaboration with supplier partners in areas such as supply chain, merchandising and marketing. AI makes it possible to unify disparate data sources to dramatically increase their value and facilitate the discovery of previously unavailable insights. Retailers and suppliers develop customer-centric, sales-boosting strategies that are mutually beneficial through a process known as value co-creation thus enabling retailers to more fully unlock the value of data. 
10. A comprehensive view of the customer: This is a growth imperative in retail channels such as fuel and convenience where shoppers interact at multiple touchpoints during a store visit. When retailers gain full visibility of customer behavior with AI-powered sitewide analytics they can make better decisions to more effectively serve shoppers and operate the business. This is especially true for convenience retailers due to the unique operating challenges associated with fuel retailing and smaller stores that leave little margin for error when it comes to category planning, inventory management and promotional execution. 

What all of these use cases have in common is they are part of an AI-powered connected retail ecosystem. Connected retail represents the future of an industry where predictive and generative AI serve as the catalyst for a new era of success that comes to life with the integration of the use case mentioned above. Connected retail ensures the seamless flow of data and insights by bridging silos across retail operations enabling retailers who work with innovative AI leaders to shift from slow, reactive analysis to near real-time responses to market demands and proactive anticipation of future trends.