How AI changes IoT

Over the last decade, IoT has been gaining ground in the business world. 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 with us, as advances in AI and machine learning reveal the capabilities of IoT devices using “artificial intelligence” or AIoT.

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

Why IoT needs AI

IoT allows devices to communicate with each other and act on these insights. These devices are as good as the data they provide. To be useful for decision making, data must be collected, stored, processed and analyzed.

This poses a challenge for organizations. As IoT adoption grows, businesses struggle to process data efficiently and use it to make real-world decisions and insights.

This is due to two problems: cloud and data transport. The cloud cannot be scaled proportionally to process all data coming from IoT devices, and the transport of data from IoT devices to the cloud is limited in bandwidth. Regardless of the size and complexity of the communication network, the huge amount of data collected by IoT devices leads to delays and congestion.

Several IoT applications rely on fast real-time decision making, such as autonomous vehicles. To be efficient and safe, autonomous vehicles must process data and make immediate decisions (just like humans). They cannot be limited by latency, unreliable connectivity and low bandwidth.

Standalone cars are far from the only IoT applications that rely on this quick decision making. Production already includes IoT devices, and delays or latency can affect processes or limit capabilities in the event of an emergency.

In the field of security, biometric data is 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 of emergencies. These applications require extremely low latency and high security. Therefore, the processing must be performed on the edge. Transferring data to the cloud and back is simply not viable.

Advantages of AIoT

Every day, IoT devices generate about one billion gigabytes of data. By 2025, the forecast for IoT-related devices worldwide is 42 billion. As networks grow, so does data.

As requirements and expectations change, IoT is not enough. Data is increasing, creating more challenges than opportunities. Obstacles limit the insights and capabilities of all this data, but smart devices can change that and allow organizations to unlock the true potential of their organizational data.

With AI, IoT, networks and devices can learn from past decisions, anticipate future activities, and continually improve productivity and decision-making capabilities. AI allows devices to “think for themselves”, interpret data and make real-time decisions without the delays and congestion that result from data transfer.

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

Avoid staying

Some industries are hampered by downtime, such as the offshore oil and gas industry. Unexpected damage to the equipment can cost a fortune during the stay. To prevent this, the AIoT can anticipate equipment damage in advance and plan maintenance before the equipment has serious problems.

Increasing operational efficiency

AI processes the vast amounts of data coming into IoT devices and detects basic patterns much more efficiently than humans can. AI with machine learning can improve this ability by predicting the operating conditions and modifications needed to improve results.

Enabling new and improved products and services

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

Improved risk management

Risk management is needed to adapt to the rapidly changing market landscape. AI with IoT can use data to predict risks and prioritize the ideal response, improve employee 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 AIoT applications in various industries.


Manufacturers use IoT to monitor equipment. Taking a step forward, AIoT combines data information from IoT devices with AI capabilities to offer predictable analysis. With AIoT, manufacturers can play a proactive role with inventory, maintenance and production.

Robotics in production can significantly improve operations. The robots are equipped with implanted sensors for data transmission and AI, so they can constantly learn from the data and save time and reduce costs in the production process.

Sales and marketing

Retail analysis takes data points from cameras and sensors to track customer movements and predict their behavior in a physical store, such as the time it takes to reach the payment line. This can be used to offer staff levels and make cashiers more productive, improving overall customer satisfaction.

Large retailers can use AIoT solutions to increase sales through customer insights. Data such as mobile-based consumer behavior and proximity detection offer valuable insights for providing personalized marketing campaigns to customers as they shop, increasing traffic in ordinary places.


AIoT has many applications in the automotive industry, including maintenance and downloads. AIoT can predict defective or defective parts and can combine data from withdrawals, warranties and safety agencies to see which parts may need to be replaced and provide customer service inspections. The vehicles end up with a better reputation for reliability and the manufacturer gains the trust and loyalty of customers.

One of the most famous and probably the most exciting applications for AIoT is autonomous vehicles. With IoT-enabled AI, autonomous vehicles can predict driver and pedestrian behavior in a variety of circumstances to make driving safer and more efficient.


One of the overarching goals of quality health care is to expand it to all communities. Regardless of the size and complexity of health care systems, physicians are under increasing pressure over time and workload and spend less time with patients. The challenge of providing high-quality healthcare against the administrative burden is serious.

Healthcare facilities also produce vast amounts of data and record large amounts of patient information, including images and test results. This information is valuable and necessary for quality patient care, but only if healthcare facilities have quick access to it to inform diagnostic and treatment decisions.

The Internet of Things, combined with AI, has many benefits for these barriers, including improving diagnostic accuracy, enabling telemedicine and remote patient care, and reducing the administrative burden of monitoring the health of patients in the facility. And perhaps most importantly, AIoT can identify critical patients faster than humans by processing patient information, ensuring that patients are sorted efficiently.

Prepare for the future with AIoT

AI and IoT is the perfect combination of capabilities. AI improves IoT through intelligent decision making, and IoT facilitates AI’s capabilities through data exchange. Ultimately, the two combined will pave the way for a new era of solutions and experiences that transform businesses into multiple industries, creating entirely new opportunities.

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

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