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AI in the retail industry is having enormous impact, chiefly by improving workflows for workers and streamlining product selection for customers.
Some version of AI has been part of the e-tail and e-commerce experiences for several years, but particularly with the advent and growth of generative AI, digital retail experiences have become much smoother and less reliant on human intervention.
In this guide to AI’s role in retail, learn how AI is currently being used and how it impacts everyone from business leaders to hourly workers to customers.
Table of Contents: AI in Retail
- How Does AI in Retail Work?
- Benefits of Using AI in Retail
- Examples of AI in Retail
- Uses of AI in Retail
- Bottom Line: AI in Retail
How Does AI in Retail Work?
The way artificial intelligence works in the retail industry all depends on how and where business leaders choose to focus their AI efforts.
For most retail organizations, AI models are fine-tuned to act as digital retail platforms or are embedded into existing retail platforms, ERP systems, CRM software, and/or business websites. These models are trained to handle a variety of behind-the-scenes and customer-facing tasks, including helping to manage inventory, supply chain processes, customer interactions, and other features of the retail life cycle.
Retail AI deployments are trained to view every customer interaction, click, and movement of inventory as a unique data point. Often, this data is absorbed into the model’s training set and is used to further specialize and fine-tune the model’s ability to interact with humans and provide the support they need.
A variety of AI types can be used in retail, but these three broad categories cover most areas of the AI-retail process today:
- Machine learning: Machine learning algorithms are trained to recognize and act on the differences between different users and data points, including ad clicks, purchases, and customer service conversations; this type of AI is particularly useful for making predictions about overall brand sentiment, what users most want to purchase, and how purchase trends will change over time.
- Generative AI and natural language processing: Generative AI is best for building chatbots, virtual assistants, and other AIs that help customers directly by generating content in response to their queries. Generative AI models can directly answer customer questions and even make purchase suggestions based on their past purchases or preferences.
- Robotics: AI-powered robotics, which can power physical machines, may be used to scan, update, or shift inventory from warehouse to storefront; drive delivery vehicles and drones; or physically interact with customers in stores.
For more information, also see: Top Robotics Startup
Uses of AI in Retail
AI chatbots
AI chatbots with access to varying degrees of information can be embedded into customer facing e-commerce sites, social media, and other applications. Going a step beyond chatbots that rely on a few manual workflows for specific conversational topics, AI chatbots are trained on a variety of subjects — often including an organizational database or knowledge base — so they can more effectively understand and respond to diverse customer questions and requests.
Live customer service coaching
AI vendors like Nuance, Gridspace, and Zenarate don’t take over customer service interactions altogether, but instead provide live coaching and suggestions to human customer service representatives. These live suggestions are often combined with detailed customer dashboards that give reps both the tools and the language they need to have a more product-focused conversation with customers on the phone.
Customer sentiment analysis
While your organization may already be taking in and reviewing customer feedback on a regular basis, it’s often difficult to compare these sentiments across users and at scale, and even more difficult to apply that data to necessary business model improvements.
With the help of AI models, all customer feedback and interactions are automatically collected and analyzed, giving retailers a faster and better understanding of how customers feel about the brand. Particularly with large language models and generative AI models, unstructured data like text can be collected from multiple different sources, and at the same time, spammy reviews can be filtered out so they don’t muddy the waters of your analysis.
Beyond developing a better understanding of customer sentiment analysis, AI enables retailers to apply these new sentiment insights to more personalized customer experiences, including catered ads and more precise audience segmentation.
Also see: The Benefits of Generative AI
Demand forecasting and prescriptive analytics
AI-driven data analytics give users — including non-data scientists — a better understanding of all the different kinds of data at their disposal.
These models can analyze data in different formats and in large quantities, looking at historical and current purchasing metadata across different categories to more accurately forecast demand. For example, business owners may use AI analytics to learn that the sweaters they sold last year not only sold quickly but were reviewed favorably in customer reviews and conversations.
They may also learn that while the sweater sold well in the Midwest, Northeast, and most of Europe, it did not perform as well in other U.S. regions or most of Asia. Armed with this data, the retailer will know that it should bring back this product but that some markets require more stock while others require less.
For the non-data scientist, AI analytics tools are particularly effective at offering prescriptive analytics, or analytics that make recommendations for how to adjust business tactics in the future based on current data. The natural-language approach these AI tools can take help business users across departments and areas of expertise deploy this data for better results.
More on a similar topic: Generative AI and Data Analytics: Best Practices
Recommendation AI
Based on individual users’ metadata, past purchases, ad engagement, sentiment analysis, and other data-driven inputs, AI in retail can now recommend products and services to customers that they may not have otherwise considered purchasing but are likely to want.
Indeed, recommendation AI is one of the fastest-growing AI-retail areas because of how well it connects with and monetizes customer preferences.
Automated inventory management
AI-supported demand forecasting is one of the ways retailers are now more accurately predicting how much inventory they need and where and when it should be stocked. AI-driven data analytics may also help retailers determine when prices should be changed, how seasonal purchases impact inventory storage and supply chain movement, and where customer returns require more frequent inventory shifts.
In a more tangible sense, AI-driven robotics can also be used to support automated inventory management. A growing number of retail warehouses are relying on AI assistive robots to scan inventory and monitor stock levels and then restock or remove stock as needed.
Intelligent, personalized display ads
Human marketing and ad managers work behind the scenes to analyze how ads are performing and then decide what changes should be made to get more audience engagement. AIs are taking over this task on a widespread scale and are making more accurate targeting decisions, primarily because they are able to analyze a greater quantity of engagement data points more quickly.
AI software in retail also more frequently notices data patterns that humans may overlook, and can draw decisions based on past data, whether that’s updating the ad copy based on user sentiment or changing where the ad falls on a webpage based on previous heatmap data.
AI can also target ads to individuals based on their metadata, making it so they’re more likely to be interested in and engage with advertising materials. Finally, AI learn and update ads in real time, ensuring ads are always optimized for the current audience.
Also see: Generative AI Companies: Top 12 Leaders
Simplified checkout experiences
Through a combination of biometrics and AI recognition technology, storefronts are starting to simplify the user checkout experience, including in physical stores.
For example, some stores are now allowing repeat customers to simply grab their items and walk out the door; the store’s AI recognition and scanning technology recognizes the customer and automatically charges them without requiring them to physically check out.
For customers that want a more user-friendly virtual shopping experience, many companies have added AI assistive elements to their retail apps. These features may make purchase suggestions or integrate with virtual wallets for a smoother shopping experience.
Continue reading: Generative AI: Enterprise Use Cases
Examples of AI in Retail
Each of the following enterprise companies has incorporated a different kind of AI into its workflows to simplify retail experiences:
ai.RETAIL
Accenture, the global professional services firm, recently released ai.RETAIL, an AI and data analytics platform that helps retailers better understand their data both at an individual customer and big picture level.
Its features include customizable customer-level views that show historical buying patterns and loyalty, dynamic merchandising for different customer channels, supply network digital twin development, and customer targeting capabilities. Tim Hortons, the Canada-based restaurant chain, has used ai.RETAIL and other products and services from Accenture to set up its customer loyalty app with rewards and data analytics that help them to keep customers engaged with the brand.
IBM watsonx Assistant
IBM’s watsonx Assistant is an AI virtual assistant that can be adapted to various business use cases, including customer service chatbots.
In the case of Camping World, an RV retailer, this AI solution serves as the engine for its custom-built AI chatbot, Arvee. The virtual agent has been designed to handle more straightforward customer service engagements and triage more complex conversations to human customer service representatives through dynamic routing.
It is also able to save detailed information about customer inquiries that occur after-hours, allowing employees to step in and re-engage those customers as necessary once they’re back on the clock. Using IBM’s AI virtual assistant has freed up more time for the company’s employees and also helps them to focus on the most pressing and challenging customer inquiries.
Amazon One and Amazon Go stores
Amazon One and Amazon Go are two AI-driven solutions Amazon has built to simplify the customer-side of each retail interaction with a shopping experience it calls “Just Walk Out” shopping.
With Amazon One, a customer’s payment information is linked to their palm, and AI and machine learning are used to identify that palm and charge the appropriate person’s account when they leave an Amazon Go or other participating store with a purchase. This means that users can quickly pick out their items and leave without going through a traditional checkout process, and Amazon’s AI sensors quickly detect when an item is picked up or put back on the shelf.
Benefits of Using AI in Retail
Many retailers fear that using AI in their business model will cause them to lose their personal touch, but so far, it has offered early adopters a range of benefits — including helping them to create a more customer-friendly experience:
- Emphasis on customer experience: Ads are targeted to what users actually want, chatbots can more clearly answer user questions on a customer’s schedule, and AI-driven apps give users access to new types of shopping experiences that fit their preferences. Although there may be less human-to-human contact in retail as AI is adopted, customers are still on the receiving end of a customer-first experience.
- More automation opportunities: Artificial intelligence can automate conversational workflows, inventory and supply chain management, and other repetitive retail tasks that have traditionally required a human touch. This reduces the chance for human error and frees up employee time for more strategic tasks, both of which can lead to higher levels of organizational productivity.
- Lessened impact of employee-user error: AI often monitors the entire supply chain and inventory management lifecycle and can identify an error as soon as it occurs. This makes it easier to mitigate stocking and shipping errors before they lead to unhappy customers or inventory shortages.
- Optimized digital marketing and analytics: AI-powered analytics tools democratize the analytics process with natural language inputs, contextualized explanations, and more detailed and accurate predictive analytics that are useful to marketing and sales teams. These tools can also analyze greater quantities and different types of data than most traditional marketing analytics tools can.
- Fewer human touch points required: As AI takes over different customer service and inventory management touchpoints, retailers can reduce their staff or focus their attention on more strategic tasks. Especially as more and more of the workforce moves away from retail and service-based industries, these AIs will help to fill in a production gap with limited retraining and recruitment requirements.
Bottom Line: AI in Retail
Retailers are getting creative and finding all kinds of ways to incorporate AI into the work they do. This ranges from allowing AI to directly interact with or indirectly influence customer interactions, restock and monitor inventory across distributed sites, or give business leaders a more detailed glimpse into current performance data and future projections.
If AI is implemented thoughtfully and with appropriate usage policies and safeguards in place, it can benefit both your employees and customers with automation, personalization, and hands-off features that improve the overall retail experience.
Read next: Top 9 Generative AI Applications and Tools