8 Ways Developer Teams Can Apply AI to Retail Operations and Experiences

Here's how developers working in the retail vertical can harness AI to deliver operational efficiency and unprecedented customer experiences.

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The transformative potential of emerging AI-powered applications—including much-discussed generative AI and computer vision capabilities—offers particularly fertile ground for developer teams in the retail industry to build out differentiating capabilities and experiences. From precision dynamic pricing to customer personalization to optimized store and supply chain management and security, developers have a breadth of opportunities to apply AI and deliver new value.

Let’s look at how developers working in the retail space can harness AI to deliver operational efficiency and unprecedented customer experiences:

1Always-on, generative AI-powered customer care chatbot interfaces

In a year where ChatGPT and other generative AI-powered interfaces have captured the collective imagination, traditional customer care strategies such as call centers look more expensive, outdated, and less reliable than ever. Retailers that develop gen-AI customer care chatbots—trained on private large language models (LLMs) that allow retail businesses to retain control of their invaluable data—can provide customers with compelling conversational interfaces capable of meeting their needs 24/7.

2Frictionless checkout

AI has the power to intelligently connect systems across retail operations and to ensure seamless management of product inventory, warehousing, shipping, and order processing. Add computer vision-enabled checkout to that list, and retailers can harness AI to enable frictionless checkout experiences, where customers can securely complete touchless transactions with just a quick scan of their palm or similar means.

3Smart merchandising and personalization

AI/ML enables hyper-personalized product recommendations that allow retailers to take merchandising far beyond traditional limits. Whereas traditional methods identify only the most direct cross-sell and upsell opportunities, ML algorithms unlock far more incisive insights, leveraging complex pattern matching, market analysis, longevity curves, and more to optimize retail product catalogs. AI can also consider product trends, seasonality, and consumer sentiment alongside sales data to optimize and align merchandise with consumer behavior. The result: more lucrative margins, and more satisfied and loyal customers.

4Competitive pricing

Retailers can leverage AI data processing and pattern matching to wow loyal customers with effective price guarantees, dynamic couponing, and reward program discounts—all while still optimizing margins. The same processes can monitor published pricing from regional competitors to ensure price competitiveness and outmaneuver market rivals.

5Inventory management

Enlisting AI-powered applications to support inventory management yields all the advantages of predictive forecasting and trend analysis, resulting in optimized inventory levels and a seamless supply chain. Price book management, auditing, and other market-leading features transform retailers’ capabilities to eliminate inventory headaches and surpass competitors in efficiency.

6Planogram management

A retail store’s planogram—the schematics for optimizing the placement of merchandise within retail space—often comes down to store managers’ artistic whims. AI and ML algorithms receiving computer vision data from store cameras can replace that instinctual approach with a pure data-driven strategy. AI-guided planograms can observe purchasing patterns and incorporate product attributes to deliver on a retailer’s specified goals—increasing sales, margin, and average order value.

7Store management

With store planograms executed and merchandise in place, AI and computer vision-enabled in-store systems can go further by monitoring all aspects of store operations and deploying employees to maintain an optimal environment for customers. For example, AI can monitor video camera feeds to track customer traffic flows, and provide heatmaps of the busiest areas in the store. Computer vision also automatically recognizes issues such as broken displays, shelves that need restocking, spills on the floor, etc. Employees can then swiftly deliver customer assistance and immediately address issues to achieve higher-quality customer experiences.

8Threat detection

AI and computer vision have the capability to detect the presence of weapons and suspicious or threatening behavior. AI-enabled systems can then react in milliseconds, sending alerts to lockdown stores and contact authorities as appropriate. When compared to traditional security that relies on human observation and reaction times, AI’s constant vigilance can make all the difference when moments count.

Brian Sathianathan

Brian Sathianathan is the Chief Technology Officer at Iterate.ai, whose Interplay platform facilitates rapid prototyping of AI-based and digital solutions, and operates as innovation middleware in production. Previously, Sathianathan worked at Apple on various emerging technology projects that included the Mac operating system and the first iPhone.

Brian Sathianathan is the Chief Technology Officer at Iterate.ai, whose Interplay platform facilitates rapid prototyping of AI-based and digital solutions, and operates as innovation middleware in production. Previously, Sathianathan worked at Apple on various emerging technology projects that included the Mac operating system and the first iPhone.