AI and IoT – are we banking on another tech fad, or will AIoT really change retail operations for the better? The question is on the minds of retailers worldwide, but for Edward Funnekotter, chief architect and AI officer at Solace, the business value of AIoT, when underpinned by event-driven thinking goes way beyond a fad. There’s the opportunity to transform shop floor experiences, keep a track of products across an entire warehouse, and take customer service to a whole new level.
The retail industry is no stranger to IoT-enabled devices and sensors, in fact, today’s retailers commonly use such technologies in store, warehouses, throughout the supply chain, right down to the customer service channels. But it’s about going one step further, layering it with AI.
Understanding the Data: Let AI Help
A lot of early applications of AI in retail will likely focus on generative AI and large language models (LLMs). These can be used for direct customer interactions through store apps, through omnichannel customer service interactions, and even to aid workers in the warehouse.
But one of the biggest issues with today’s LLM-based AI is that it is relatively expensive and slow. Simply fire-hosing IoT data to an LLM for processing will quickly become unwieldy and very expensive. 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.
Merging AI and IoT With Event-Driven Architecture: It’s all in the Fine-Tuning
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.
Events are “published” with a topic that indicates what they are about, and systems can “subscribe” to receive all events with relevant topics. 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.
Real-World Examples put the Business Benefits Into Perspective
Event-enabling IoT streams can provide benefits to retail customers and employees in-store, via customer service channels and even in warehousing.
Here are three use cases where the convergence of AI and IoT in retail, underpinned by event streaming, can make a real difference.
- Start in the store with personalization.
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.
- Don’t forget the call center: The beating heart of customer service.
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.
- Finish in the warehouse: Your employees are more important than ever.
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 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 AIoT Revolution is Here – are you Joining the Retail Bandwagon?
It’s already clear to see how the convergence of AI and IoT will transform retail operations today and in the future – especially when underpinned with an event-driven approach!
Retailers can selectively feed data to AI systems and allow them to operate in real time. The benefits will be felt widely, from the customers experiencing first-rate customer service, to the employees powering operations.
