It’s not often that two synergistic, paradigm-shifting technologies mature at the same time. Yet here we are, as both generative artificial intelligence (GenAI) and the ambient Internet of things (ambient IoT) are ascendant. These symbiotic advances are poised to reinvent industries, protect public health, and help fight climate change.
Let’s start with GenAI, which gets all the headlines (and investment) but is already experiencing growing pains. The large language models (LLMs) that underpin GenAI applications like ChatGPT and Microsoft Copilot learn by training mostly on publicly available data — news, information, social media — or licensed sources. Some datasets are combined or repurposed to create new training data; and they all need appropriate cleaning and analysis to filter out bias, “noise,” and low-quality information. Virtually all the data is human-generated, and while some is agreed-upon fact, much is subjective (and sometimes false), leading to GenAI “hallucinations” that ultimately undermine users’ trust in GenAI technology.
Plus, by some estimates, today’s GenAI could run out of training data as early as 2026. Then what? Actually, there’s plenty more untapped data to drive innovation, especially if tomorrow’s GenAI evolves into something different from the GenAI we think of today.
Today’s GenAI is frankly a consumer AI solution, even if consumers use it for work tasks, like creating presentations or summarizing online meetings. Tomorrow’s GenAI is an enterprise GenAI, focused on creating value for organizations in very specific areas, like improving healthcare outcomes, optimizing vaccine delivery, or tracing possible contamination through food chains. This enterprise-class GenAI requires enterprise LLMs – and new, trusted enterprise datasets now being created through ambient IoT.
Ambient IoT Data Augments LLMs
Here’s a good way to think about enterprise GenAI and the data required to enable it: If a supply chain manager wants to use today’s GenAI to optimize a supply chain, they may enter a “How do I?” prompt into an existing chatbot and receive informed answers based on existing LLMs.
With enterprise GenAI, augmented by ambient IoT data, the same supply chain manager can ask how to optimize their supply chain, based on data about their suppliers and the real-time movement and handling of their products and materials through their distribution channels. In other words, the finite universe of existing information about supply chains is augmented with near-infinite ambient IoT data about the actual products and conditions in those supply chains. And the manager isn’t just asking the LLM for information, they’re asking products themselves questions like: “Where are you now? What’s your condition? How can I get you from A to B more efficiently, sustainably, and cost-effectively?”
They can ask these questions of products in their supply chain because the ambient IoT knows the answers, based on vast amounts of data about trillions of everyday things.
Ambient IoT uses inexpensive, stamp-sized, self-powered compute devices (Wiliot calls them IoT Pixels) affixed to products, packaging, containers, crates, pallets, and more. These IoT Pixels communicate data through Bluetooth — automatically and without human intervention — to the cloud, where businesses can analyze information like location, temperature, humidity, and carbon footprint.
From Real-Time Product Data to Insights to GenAI
Through machine learning, an ambient IoT platform turns data into insights (“The pallet isn’t where it’s supposed to be.” “The case of produce was kept too warm for too long.”). Then, as ambient IoT merges with GenAI, businesses can integrate the resulting data and insights into new or existing LLMs to generate answers and fresh information, and to power enterprise-class GenAI that’s tailored to their specific industries, processes, and desired outcomes.
Most importantly, the data that ambient IoT generates is objective, and the insights safe from so-called hallucination. For example, a product — as sensed and traced through the ambient IoT — is demonstrably at a location (supplier, distribution center, retailer, etc.) or it isn’t. It’s being handled at a measurable temperature and humidity, or it isn’t. It is, in fact, taking the shortest route to a store or consumer, or it isn’t.
And based on objective data and insight, combined with unambiguous enterprise policy, it’s objectively where it needs to be, safe to consume or administer (in the case of pharmaceuticals), or meeting sustainability commitments by minimizing its carbon impact. When a supply chain manager asks an enterprise chatbot about the products they’re responsible for, they can trust the GenAI-generated answers.
Ambient IoT + GenAI = Talking to Products
In fact, when Wiliot developers applied their expertise in AI and machine learning to the combined potential of GenAI and ambient IoT, the central question was, “What if products could talk?” Could a grocer, for example, ask a chatbot which crate of milk is freshest? Or which boxes of produce are safe? Could another retailer ask which shipment of goods has the smallest carbon footprint, and then ask those goods to explain why?
By integrating the symbiotic technologies of ambient IoT and GenAI, they can. Wiliot demonstrated the ability with an enterprise chatbot for natural-language conversations with and about ambient IoT-connected products.
And eventually, businesses can make their enterprise LLMs available to consumers so individuals can talk to products. Using a smartphone app, a consumer can ask a grocery chatbot for a recipe of only the freshest ingredients in a store. Or ask a display of strawberries about the farm where they were grown. Or ask an article of clothing to explain its origin story and carbon impact.
Ambient IoT gives businesses and consumers visibility into everything around them. By combining data and insights from trillions of everyday things with GenAI LLMs, it’s possible to create more powerful, more knowledgeable, and more trustworthy chatbots. And to ask them literally anything.
