The use of generative AI for IoT (Internet of Things) offers the potential to extend the value of smart devices and systems. While IoT devices collect vast amounts of real-time data, traditional AI methods, such as machine learning, interpret the data to inform on conditions, predict trends, and automate responsive actions. Generative AI complements traditional approaches and further enhances the ability to interpret, predict, and optimize outcomes, enabling businesses to derive deeper insights and drive more intelligent automation.
This two part article explores how businesses leveraging IoT can unlock new efficiencies and value by integrating generative AI. Part One discusses the convergence of generative AI with IoT, and highlights some types of opportunities that are available. Part Two discusses some of the risks businesses face from this convergence. This blog is part of a continuing series of articles aimed at providing senior leaders and managers with a practical working knowledge of artificial intelligence.
What is Generative AI?
Generative AI is a type of artificial intelligence that goes beyond analyzing data—it creates entirely new and original content, such as text, images, audio, and even software code. Unlike traditional AI, which is designed to classify, predict, or optimize based on existing data, generative AI learns patterns from vast, broad knowledge sets and mimics human creativity to create original outputs.
This human-like intelligence capability makes it uniquely valuable for businesses in many ways. For example, generative AI can produce personalized marketing content, such as crafting tailored messages for different customer segments without the need for extensive human intervention. In product design, it can generate innovative concepts and prototypes to accelerate the ideation process. Customer support can be enhanced through AI-powered chatbots that understand and respond to complex queries in natural language, providing 24/7 assistance. Generative AI can support data analysis by performing complex queries and produce detailed reports and visualizations, helping executives make more informed decisions. Finally, it can also streamline business processes by automating the creation of documentation, presentations, and even software code generation.
Users interact with generative AI applications using natural language in a conversational manner. For example, users can get a report by making a request to the AI with “Please provide a summary status of yesterday’s activities.” In turn, the generative AI application responds dynamically in a conversational manner to refine and improve its outputs based on user feedback. This back and forth dynamic interaction and response makes business processes more adaptive and intelligent.
Generative AI is still an emerging technology that is evolving rapidly. However, it is poised for broader adoption as many business enterprises begin to recognize and understand its transformative potential to drive efficiency, creativity, and competitive advantage across their operations.
What is generative AI for IoT?
IoT devices and systems are one major source of operational data in a business. For example, the information provided by sensors range from the condition and performance of manufacturing equipment, the location of inventory and assets, and environmental conditions inside buildings. IoT data is generated on a continuous and real-time basis, in high volumes from thousands (and potentially millions) of sensors in the future. It comes in diverse data formats, including sensor readings, audio, video, and images. IoT data is both structured (sensor readings) and unstructured (audio, video, and images).
Traditional AI, such as machine learning, have mostly analyzed structured sensor data—such as temperature readings and other machine performance metrics—to generate insights from IoT data. For instance, machine learning models detect early signs of equipment failure based on relevant sensor measurements, such as temperature or vibration levels. That information is then used to predict when failure may occur.
Generative AI incorporates information from unstructured data sources, such as text from a machine operator’s logs and maintenance records, to complement IoT sensor data. While traditional AI may predict when equipment failures may occur, generative AI for IoT uses the information in the logs and maintenance records to fine tune the prediction. Further, generative AI may simulate different operational scenarios and generate optimized maintenance schedules, and suggest new operational strategies to extend equipment life and minimize unplanned downtime. This convergence of unstructured information with structured data significantly enhances the depth and accuracy of insights to create more relevant and actionable responses. The figure below shows an example of how the different types of AI could be applied in a manufacturing environment.
Generative AI for IoT opportunities
The convergence of unstructured information with structured data is transforming how businesses extract value from IoT ecosystems. By combining structured and unstructured data, generative AI for IoT brings a variety of new capabilities and intelligence to enhance processing and analysis of operational data. Furthermore, this convergence enables generative AI to transform IoT into a more intelligent, context-aware system.
Broadly speaking, the generative AI opportunity for IoT can be grouped into five categories, as shown in the figure below. Representative types of opportunities are briefly discussed below.
Opportunity: Summarize
Synthesize massive volumes of IoT data to create insights and reports
IoT generates high-volume data streams that are difficult to analyze manually. While a small set of this data is displayed on dashboards or acted upon by other systems immediately, much of it is stored unanalyzed in logs and cloud databases. Generative AI enables businesses to extract value from all of its IoT data. It automates the synthesis of these massive volumes of the IoT data, integrates with relevant unstructured data to provide summaries and insights in human-readable reports.
Example:
- Smart Manufacturing: AI generates daily reports operational anomalies, production inefficiencies and machine health from Industrial IoT (IIoT) sensor logs, operator logs and maintenance records.
- Healthcare IoT: AI summarizes patient vitals from various remote monitoring devices and hospital logs, reducing manual review time and assisting physicians in making faster decisions.
Opportunity: Simulate
Generate synthetic data for AI model training and testing
IoT applications require diverse training data, but real-world data collection is often limited or incomplete. For example, it is not possible to collect data on rare and unusual outlier conditions. It may be physically impossible to place a sensor at specific locations. In some cases, hazardous conditions limit or prevent the collection of real-world data. Generative AI can create synthetic IoT datasets to simulate these scenarios, which is used to broaden the training dataset and improve machine learning model responses.
Example:
- Traffic Management: AI generates realistic urban traffic patterns for optimizing IoT-driven smart city solutions.
- Healthcare IoT: AI produces synthetic patient vitals to test early warning systems for cardiac events.
Extend the performance of digital twins
A digital twin is a virtual model of a physical asset and environment. It mirrors the behavior, characteristics, and performance of its physical counterpart. IoT data helps build and refine digital twins and environmental models. Generative AI enables digital twins to continuously learn and adapt based on new data from IoT sensors, predictive simulations and “what-if” scenarios, ensuring they remain accurate representations of their physical counterparts. In addition, generative AI augments physical world IoT data with synthetic data to improve the fidelity and robustness of the digital twin.
Example:
- Smart Grids: AI simulates future power demand fluctuations based on IoT sensor data, assisting energy providers in outage prevention.
- Industrial IoT: AI predicts how machine failures will impact production lines, improving resilience planning.
Opportunity: Understand
Make IoT data analysis accessible to non-data scientists through natural language interactions
Extracting insights from the massive volumes of data collected by IoT devices and systems can be a complex undertaking. While some information can be obtained from pre-configured dashboards designed to answer specific questions, querying that data set to answer other questions is more difficult. This often requires a skilled data analyst to set up and perform the queries, a task that ranges from minutes for something simple to days for more complex questions. Generative AI enables non-data analysts and business users to ask natural-language questions, then performs the necessary data analyses using structured and unstructured data, and provide instant, AI-generated responses.
Example:
- Smart Buildings: A facility manager asks, “How did energy usage fluctuate this week?” AI analyzes HVAC and occupancy sensor data and provides an answer and a summarized report.
- Supply Chain IoT: A logistics manager queries, “Where are delays occurring in our cold chain transport?” AI analyzes GPS and temperature telemetry, generating a summary and the extent of the bottlenecks.
Diagnose and troubleshoot equipment problems
Many machines are equipped with a number of sensors to report on its operating condition. The data collected from the IoT sensors helps technicians service the equipment and troubleshoot any performance issues. However, some of these equipment are complex to service and repair, requiring highly experienced service technicians. Generative AI systems facilitate the maintenance and repair of equipment by combining sensor data and information from the operator logs and technical service manuals to quickly guide the technician to the cause of the problem and instructions for repair. This enables less experienced technicians to service complex equipment. In addition, it allows experienced technicians to increase their productivity and return the equipment to normal working condition faster.
Example:
- Smart Factory: Generative AI helps an onsite technician diagnose equipment problems faster by narrowing in on likely causes of the problem based on IoT data and the operating history of the equipment from operator logs. The generative AI system then presents the technicians with the key steps from the technical service manuals to make the repair.
- Industrial IoT: AI analyzes turbine performance in a power generation plant and predicts when it needs maintenance. The generative AI system reviews the operating logs of the turbine and the IoT sensor data to determine the cause of performance degradation. It simulates a number of operating scenarios to identify one that allows the turbine to operate at a level that minimizes the risk of performance degradation and premature failure before it can be taken offline for maintenance.
Opportunity: Innovate
Create new and innovative designs and plans
Context data, in combination with IoT data, can be used to simulate scenarios, generate predictions, and facilitate planning and design. IoT data provides measurements of the physical environment. This data is used by generative AI applications to complement design principles, planning guidelines and policies, to create new designs. Further, the generative AI application conducts a variety of simulations on the candidate designs to assess its performance, viability and compliance with requirements.
Example:
- Street design: Generative AI considers data from traffic sensors and cameras, pedestrian counters, noise sensors and air quality monitors to create simulations of a busy street corridor. It analyzes and predicts traffic patterns, pedestrian behaviors and experiences, public transit schedules, and air and noise pollution levels for a variety of scenarios. The generative AI application then produces a design that is prioritized and optimized for its requirements.
Opportunity: Autonomous execution
A form of generative AI, agentic AI, enhances the power of IoT by not only generating insights but also taking autonomous action based on those insights. Traditional IoT systems provide real-time monitoring and predictive analytics, but human intervention is often required to act on this information. With agentic AI, IoT-driven insights trigger automated workflows, resource allocation, and decision-making processes, reducing operational delays and improving efficiency. These AI-driven agents can execute complex, multi-step actions such as coordinating logistics, adjusting system parameters, or initiating procurement processes—all without direct human oversight. This capability transforms IoT from a passive data collection network into an intelligent, action-oriented system that improves uptime, reduces costs, and enhances service delivery.
Example:
- Predictive Maintenance: When IoT sensors detect early signs of wear in industrial machinery, agentic AI autonomously orders replacement parts and schedules a service technician based on predictive failure timelines, minimizing downtime.
- Energy Management: In smart buildings, AI agents analyze occupancy and weather data from IoT sensors to adjust heating, cooling, and lighting in real time, optimizing energy consumption while maintaining occupant comfort.
Conclusion
The fusion of generative AI and IoT represents a transformative opportunity for businesses, unlocking new levels of automation, insight generation, and decision-making. Businesses currently using IoT in its operations should thoughtfully examine and study how and where generative AI can improve and enhance existing operations. This article highlighted five representative examples of the value provided by generative AI for IoT. As AI technologies continue to evolve, early adopters who strategically integrate generative AI into IoT ecosystems will gain a competitive edge.
While the benefits provided by generative AI for IoT are significant, it also brings risks and challenges. Part Two of this article discusses these risks and challenges.
This is third in a series of blogs aimed at providing senior leaders and managers in mid-market organizations with a practical working knowledge of artificial intelligence (AI). If there are specific topics you wish for us to address in the future, please comment below.
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Video Interview: The generative AI opportunity for IoT
The generative AI opportunity for IoT Part Two
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