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The retail sector has not been immune to the proliferation of Internet of Things (IoT)-enabled devices and sensors throughout stores, customer service channels, warehousing and the supply chain. Now, layering on artificial intelligence (AI) to better leverage this data, AIoT is emerging as pivotal for retailers.
In its IoT in Retail and Apparel thematic report, GlobalData sees the creep of AI into IoT products and services in retail is inevitable and already happening: “The key layers in the IoT value chain are physical, connectivity, data, apps and services. While these layers are logically discrete, large-scale IoT solutions are seeing considerable blurring of these logical boundaries.
“For example, while there will continue to be a clearly identifiable data layer towards the top of the stack, a growing proportion of the data processing occurs within and at the edge of the network. The accelerated development of generative AI (Gen-AI), particularly ChatGPT, has increased the relevance of AI across all IoT layers. Therefore, a growing number of IoT products and services incorporate AI into their capabilities, especially across customer-driven interfaces.”
A lot of early applications of AI in retail will likely focus on Gen-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.
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.
There are three use cases where the convergence of AI and IoT, underpinned by event streaming, is making a real difference to retailers and their customers.
The first focuses on enhancing every customer journey within the store. AIoT lets retailers 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. 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.
The second use case involves ensuring maximum success within the contact center. Modern customer contact centers now come with an AI copilot designed for better customer service. 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.
The third use case involves 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 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.
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 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. The key to unlocking these benefits lies in an event-driven approach — by selectively feeding relevant data to AI systems, retailers can implement real-time solutions to elevate to a new level of customer experience, empower employees and optimize operations.
Edward Funnekotter serves as chief architect and AI officer at Solace.
