How IoT-Generative AI Convergence Will Reshape Business Innovation

Over the last decade, the Internet of Things (IoT) has grown to tens of billions of connected devices, from thermostats to vehicles and factories, continuously sending multi-modal data to the cloud. With this explosive data creation and collection underway, customers are seeking new ways to scale operations, gather insights and integrate data into their product development processes. 

One of the ways is entering the realm of generative artificial intelligence (AI), a disruptive technology that leverages large datasets to create large language models (LLMs) capable of powering natural language conversational assistants that intelligently interact with the user, write code and combine text and video data. 

While companies have mostly approached generative AI as a large upfront effort in training LLMs, we have only recently seen techniques like retrieval augmented generation (RAG) used for fine-tuning with proprietary data, the possibilities are boundless. By leveraging generative AI, companies can streamline processes, reduce costs and unlock new opportunities for innovation.

In this article, we will address why generative AI and IoT data are becoming more essential for business growth and decision-making, paving the way for a more intelligent, data-driven and hyper-personalized future.

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The Synergy Between Generative AI and IoT Data

To unlock the full potential of generative AI, customers need to find ways to continuously feed new data to LLMs. Historically, IoT has enabled the transfer of data from devices to the cloud for further processing and analytics derivation. With the advent of generative AI solutions at the edge, often referred to as Small Language Models (SLM), IoT is emerging as an enabler of compute tasks that can seamlessly harvest and transfer data between devices and the cloud. In cases where real-time responsiveness is crucial (for example, a robot taking action), an SLM can provide an immediate response while simultaneously using IoT to align status with an LLM counterpart.

An example of integration between IoT and generative AI can be found in connected vehicles, where automakers use IoT to connect millions of vehicles to the cloud, collecting diagnostics and supporting connected services. One such connected service is online search, which allows drivers and passengers to use voice commands or in-vehicle touchscreens to search the internet for information, directions, or points of interest. 

Vehicle in-cabin voice assistants have also been available for years, attempting to provide an easy-to-use speech interface for various vehicle functions and features. However, current voice assistants are limited in understanding natural language and complex conversational sequences, often frustrating drivers. Automakers are looking to leverage generative AI to create in-vehicle experiences similar to popular chat assistants. To do so, they need to rely on IoT technology to collect information from various vehicle sensors and systems, communicating with the LLM residing in both the vehicle and the cloud. During this handshake, automakers also need to depend on IoT technology to ensure security, privacy and flexibility while supporting new user experiences.

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We are just beginning to realize the possibilities at the intersection of IoT and generative AI. For example, IoT robotic devices, such as articulated arms and autonomous mobile robots, commonly found in manufacturing plants, can see and sense their surroundings. To date, most of these robots follow preset instructions with limited to no capability for improvement. With generative AI multi-models that continuously learn from new environments and user needs, IoT plays a key role in establishing a communication feedback loop that can enable collaborative action plans across robot fleets, improving efficiency and reliability.

The Future of Connected Intelligence

In summary, the convergence of IoT and generative AI is unlocking new possibilities for businesses across various industries. IoT enables the continuous flow of data from devices to the cloud, allowing for more intelligent and responsive systems capable of understanding natural language, adapting to complex scenarios and providing personalized experiences. This interaction is not only critical to enable efficiencies but also for realizing automation routines that are truly smart and independent in nature. As we move forward, we can expect to see more innovative applications of IoT and generative AI working together and unlocking new opportunities for innovation across various industries.