The Tech Merger Made in Retail Heaven: Where AI Meets IoT to Deliver Retailers Real-Time Insights Inside and Outside the Store

The three use cases where the convergence of artificial intelligence and Internet of Things underpinned by event streaming is making a real difference to retailers and their customers are as follows.

Enhancing Every Customer Journey Within the Store

AIoT enables retailers to intelligently take advantage of in-store and customer data to offer highly customized shopping experiences. By using AI to analyze customer data from IoT devices, retailers can tailor product recommendations, offers, and even in-store experiences to individual preferences. Take the instance of providing an in-store customer service assistant that knows where the customer is and, more importantly, where everything else is located.

Smooth customer operations need robust back-end operations

Being able to action these requests quickly, accurately and effectively means event enabling all stock information and AI processing. Consumers 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 purchased, events could also include back-end documentation and instructions that explain to the customer how to build their required project when they get home.

Now for Employees — Ensuring Maximum Success Within the Contact Center

Modern customer contact centers now come with an AI copilot designed for better customer service. Microsoft Copilot, for example, is now inherent with Microsoft 365 and extends existing contact center channels with generative AI to enhance service experiences and boost agent productivity. AI can help with processing recorded or real-time calls to customer service to highlight any serious issues that need emergency assistance..

Pairing EDA with AI

By event-enabling this AI copilot and tying it in with the numerous data points across the customer service process (CRM data for customer history, type of device/channel they're communicating from, customer service scripts/protocols and BI reporting) 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 a large language model (LLM) to enhance the event with additional information. For example, performing sentiment analysis on user interactions to identify customers with issues that need routing to an expert, or a customer ripe for an upsell, or synthesizing new events based on the combination of accumulated data.

Keeping Workers Safe … Always

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.

These all provide a wealth of potential benefits from which AI can glean new insights and address potential issues. 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 a LLM-based AI can then review, sort, group and provide curated advice to management.

Even in times of emergency, safety is paramount

In an emergency, 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 they're delivered to the appropriate person or device to trigger an emergency response.

Edward Funnekotter is chief architect and AI officer at Solace, a market-leading enabler of the real-time, event-driven enterprise.