AI Paired with the IoT Means Retail Goes Real Time

Internet of Things (IoT)-enabled devices and sensors are nothing new for the retail industry, but what about integrating artificial intelligence into this process? Retailers are now layering AI into IoT to better leverage data from the shop floor.

Using AI to Unlock New IoT Capabilities

A lot of early applications of AI in retail will likely focus on generative AI (genAI) and large language models (LLMs). But one of the biggest issues with today’s LLM-based AI is that it is relatively expensive and slow.

The biggest benefits from the convergence of AI and IoT in retail will be realized by retail organizations identifying intelligent use cases to deliver benefits to customers, staff members, and the business as a whole.

An Event-Driven Approach Facilitates the Merging of AI and IoT

Fine-grained routing via event streaming allows systems to be more selective in what is analyzed by AI so that it can be both cheaper and more reactive to events. An event represents a change in state, or an update, such as an item being placed in a shopping cart, a loyalty card application being submitted, or an order becoming ready to ship.

AI systems receive events to produce real-time results that allow for real-time solutions/actions to be automatically triggered—but this data feed also provides a stream for constant learning, through either ingestion into a vector database or for fine-tuning the model itself.

Three Use Cases That Prove the Point

Here are three use cases where the convergence of AI and IoT in retail, underpinned by event streaming, can make a real difference.

In-store direction: AI data analysis drives a hyperpersonalized customer experience. By using AI to analyze customer data from IoT devices, retailers can tailor product recommendations, offers, and even in-store experiences to individual preferences.

For example, a customer could tell the store app that they’re looking to build a fence. They no longer have to wait for the hardware store representative to advise them on where the product they need is and which they should use. Instead, an AI assistant would use store-specific information to provide a response tailored for each customer’s needs.

Being able to action these requests quickly, accurately, and effectively means event enabling all stock information and AI processing. Customers need to know in real time if the materials they require are available, and this would also require the contextual use of sensors in-store to direct them to the area of the store to find their goods.

An event-driven approach to integrate both this device data and AI processing would use an event mesh—a network of interconnected event brokers that enables the distribution of events information among applications, cloud services, and devices—to enable real-time processing and predictive insights. Once a purchase is made, events could also include back-end documentation and instructions that explain to the customer how to build their required project when they get home.

In the call center: AI copilots offer a digital helping hand. Modern customer contact centers now come with an AI copilot designed for better customer service. AI can help with processing recorded or real-time calls to customer service to highlight any serious issues that need emergency assistance.

By event-enabling this AI copilot and tying it in with the numerous data points across the customer service process, organizations can deliver new levels of real-time insights to the customer service rep.

AI agents can subscribe to a narrow set of events, provide a prompt template specific to that subscription and then use an LLM to enhance the event with additional information. For example, performing sentiment analysis on user interactions can identify customers with issues that need routing to an expert, or customers ripe for an upsell.

In the warehouse: AI can promote a safer and more efficient factory floor. Further up the retail operations chain, AI can also aid exception handling for factory workers. Most retailers are now using some kind of mobile or tablet device in warehousing operations, and these are supported by IoT devices on the floor for stock monitoring and other inventory-related tasks.

For example, a genAI solution could provide all workers with an extremely easy way of reporting issues, incidents/near misses, or thoughts for efficiency. This is qualitative information, but an LLM-based AI can then review, sort, group, and provide curated advice to management.

In an emergency situation, for example, there is also potential to greatly increase the speed in which organizations can respond in real time in the warehouse or factory floor.

Here the event mesh can link many AI agents, each tailored to a specific set of events. This can be as straightforward as subscribing to all events that contain raw audio and using a speech-to-text model to create the transcription, which is then published back into the mesh. All of these components communicate asynchronously via the event mesh using guaranteed messaging to ensure that no events can be lost in transit and that they are delivered to the appropriate person or device to trigger an emergency response.

The convergence of AI and IoT isn’t just a trend; it’s a pivotal turning point for the retail sector that, when underpinned by event-driven thinking, can unlock a whole new standard of customer service and shop floor operations.

Edward Funnekotter is chief architect and AI officer at Solace. Leading the architecture teams for both Cloud and Event Broker products, he also leads the company’s strategic direction for AI integration within products and internal tools. In 2004, Funnekotter began his journey with Solace as an FPGA architect. He later transitioned into management and led the Core Product Development team for several years before ascending to his present position.