Event-enabled AIoT (the convergence of AI and IoT) brings game changing benefits to retail customers and employees in three areas: in-store, through customer service channels, and in the warehouse.
Here are three real-time applications where an event-enabled approach to AIoT will make a difference.
1. In-store direction: Layering AI into in-store apps to personalize customer interaction
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.
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. It would go to its databases and answer the query intelligently and say, OK, now that we've figured out the kind of materials you need, let's go walk around the store and find them.
Maximizing front-end customer experience requires back-end data movement
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 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.
2. In the call center — over the shoulder support for customer service representatives
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. Note that this isn't the AI taking the place of the customer support person but reacting to issues that have come up in a human-to-human call to provide real-time context about the customer and the problem that they are having.
Event enabled AI to further add context for customer service reps
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 are 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 an 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 customer ripe for an upsell, or synthesizing new events based on the combination of accumulated data.
3. In the warehouse — empowering and protecting workers on the 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.
These all provide a wealth of potential benefits from which AI can glean new insights and address potential issues. For example, a Gen-AI 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.
Emergency response in real time to keep operations safe
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. Having an event-driven system to deliver the information and AI to transcribe it, look at it, and then put it in front of the relevant person as soon as possible, could improve safety, time, and money on the 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 they are delivered to the appropriate person or device to trigger an emergency response.
The future of retail is intelligent and connected
The convergence of AI and IoT in retail isn't just a fad, it's a revolution that can already be achieved with the technologies and data available to retailers today.