How AI is changing IoT

How AI is changing IoT

IoT has been steadily adopted in the business world over the past decade† Businesses are built or optimized using IoT devices and their data capabilities, ushering in a new era of business and consumer technology. Now the next wave is upon us as advances in AI and machine learning unleash the possibilities of IoT devices that leverage “artificial intelligence of things” or AIoT.

Consumers, businesses, economies and industries that adopt and invest in AIoT can harness its power and gain competitive advantages. IoT collects the data and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention.

Why IoT needs AI

IoT enables devices to communicate with each other and act on those insights† These devices are only as good as the data they provide. In order to be useful for decision-making, the data must be collected, stored, processed and analysed.

This creates a challenge for organizations. As IoT adoption on the rise, companies struggle to process the data efficiently and use it for real-world decision making and insights.

This is due to two problems: the cloud and data transport. The cloud cannot scale proportionally to handle all the data from IoT devices, and transporting data from the IoT devices to the cloud is limited in bandwidth. Regardless of the size and sophistication of the communications network, the sheer amount of data collected by IoT devices leads to latency and congestion.

Several IoT applications rely on fast, real-time decision making, such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instant decisions (just like a human). They cannot be limited by latency, unreliable connectivity, and low bandwidth.

Self-driving cars are far from the only IoT applications that rely on this rapid decision-making process. Manufacturing already includes IoT devices, and delays or latencies can affect processes or limit capabilities in an emergency.

In security, biometrics are often used to restrict or allow access to certain areas. Without fast data processing, there can be delays that affect speed and performance, not to mention the risks involved in emergency situations. These applications require ultra-low latency and high security. Therefore, the processing has to be done at the edge. Transferring data to the cloud and back just isn’t feasible.

Benefits of AIoT

Every day, IoT devices generate approximately one billion gigabytes of data. By 2025, the projection for IoT-connected devices worldwide is 42 billion† As the networks grow, so does the data.

As demands and expectations change, IoT is not enough. Data is increasing and creating more challenges than opportunities. The obstacles limit the insights and capabilities of all that data, but intelligent devices can change that and enable organizations to unlock the true potential of their organizational data.

With AI, IoT networks and devices can learn from past decisions, predict future activities, and continuously improve performance and decision-making capabilities. AI enables the devices to “think for themselves”, interpret data and make real-time decisions without the delays and congestion that occur with data transfers.

AIoT has a wide range of benefits for organizations and provides a powerful solution for intelligent automation.

Avoid Downtime

Some industries are hampered by downtime, such as the offshore oil and gas industry. Unexpected equipment outages can cost a fortune in downtime. To prevent that, AIoT can predict equipment failures in advance and schedule maintenance before equipment encounters serious problems.

Increase operational efficiency

AI processes the massive amounts of data coming into IoT devices and detects underlying patterns much more efficiently than humans can. AI with machine learning can improve this capability by predicting the operating conditions and adjustments needed for better results.

Enabling new and improved products and services

Natural language processing is constantly being improved, allowing devices and people to communicate more effectively. AIoT can improve new or existing products and services by: enabling better data processing and analysis

Improved risk management

Risk management is necessary to adapt to a rapidly changing market landscape. AI with IoT can use data to predict risk and prioritize the ideal response, improve worker safety, mitigate cyber threats, and minimize financial losses.

Key Industrial Applications for AIoT

AIoT is already revolutionizing many industries, including manufacturing, automotive and retail. Here are some common uses for AIoT in various industries.


Manufacturers use IoT to monitor equipment. AIoT goes one step further, combining the data insights from IoT devices with AI capabilities to provide predictive analytics. With AIoT, manufacturers can take a proactive role with warehouse inventory, maintenance, and production.

Robotics in manufacturing can significantly improve business operations. Robots are equipped with implanted sensors for data transfer and AI so that they can continuously learn from data and save time and reduce costs in the production process.

Sales and Marketing

Retail analytics takes data points from cameras and sensors to track customer movements and predict their behavior in a brick-and-mortar store, such as the time it takes to reach the checkout. This can be used to suggest staffing levels and make cashiers more productive, improving overall customer satisfaction.

Major retailers can use AIoT solutions to grow sales through customer insights. Data such as mobile user behavior and proximity detection provides valuable insights to deliver personalized marketing campaigns to customers as they shop, increasing traffic in physical locations.


AIoT has many applications in the automotive industry, including maintenance and recalls. AIoT can predict faulty or defective parts and can combine recall, warranty and safety authority data to identify parts that may need replacement and perform service checks to customers. Vehicles eventually gain a better reputation for reliability and the manufacturer gains customer trust and loyalty.

One of the best-known and perhaps most exciting applications for AIoT is autonomous vehicles. With AI that enables intelligence to IoTautonomous vehicles can predict the behavior of drivers and pedestrians in a variety of conditions to make driving safer and more efficient.


One of the main goals of quality health care is to extend it to all communities. Regardless of the size and sophistication of health care systems, physicians are under increasing time and work pressure and are devoting less time to patients. The challenge of providing high-quality care at an administrative burden is great.

Healthcare facilities also produce huge amounts of data and record large amounts of patient information, including imaging and test results. This information is valuable and necessary for quality patient care, but only if healthcare facilities can access it quickly to make diagnostic and treatment decisions.

IoT coupled with AI has numerous benefits over these hurdles, including improving diagnostic accuracy, enabling remote telemedicine and patient care, and reducing the administrative burden of tracking patient health in the facility. . And perhaps most importantly, AIoT can identify critical patients faster than humans by processing patient information so that patients are effectively triaged.

Prepare for the future with AIoT

AI and IoT is the perfect marriage of possibilities. AI enhances IoT through smart decision-making, and IoT facilitates AI capabilities through data sharing. Ultimately, the two combined will pave the way for a new era of solutions and experiences that transform businesses across many industries and create new opportunities.

Xavier Dupont is the senior director of the product line at Lantronix, a global provider of turnkey solutions and technical services for the Internet of Things (IoT). The goal of Xavier and Lantronix is ​​to enable IoT and their customers digital transformation by providing technology from detection to data collection and visualization.

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