Here are the Top Applications of AIoT across various industries

Top Aiot applications depend deployment of Big Data & Cloud

Applications of AIoT successfully defines the new generation for technological achievements. Moreover, Artificial Intelligence and the Internet of Things make devices more accessible. Hence, this uncovers the immense potential of AIoT and its applications that help automate business processes.

AIoT technologies certainly depend on the deployment of Big Data and Cloud Architecture. Further, it helps generate, store, and process data for actionable insights to build AI strategies.

The general applications of AIoT have the potential to transform and improve businesses and their services. Moreover, it is critical to understand that the usage of AI and its technologies has grown by 270% in the last four years. Further, the AI development sector can value up to $118 billion by 2025.

Reports from 2020 also uncover that more than 30.73 billion devices were connected. As a result, the IoT infrastructure is predicted to connect 152,200 devices every minute by 2025.

Hence, in this article, we will learn more about AIot, its benefits, and applications across industries.

Applications of AIoT Across Industries

What is AIoT?

AIoT comprises two technological concepts, AI and IoT. Moreover, it refers to the augmentation of AI in IoT technologies and devices. Hence, this seamless integration between AI and IoT is now enhancing the growth of various industries.

Additionally, combining these advanced and transformative technologies enable businesses to predict changes and enhance the abilities of devices. Therefore, with AIoT, there is a significant improvement in communication and predictive capabilities. 

According to Tolga Tarhan, CTO at Rackspace Technology, “AI is utilized to deliver more capabilities for the connected device, by moving from purely programmed/algorithmic responses to dynamic decisions, empowered by machine learning.”  

The Main Aspects of building AIoT Solutions:

  • Data Collection: This aspect refers to the agenda of collecting data from various devices to help train the AI model. Hence, this phase helps process data to help AI detect and rectify errors as well as prevent biases.
  • Training: AI models often require advanced neural networks and various algorithms for training. Hence, it demands more robust processing capabilities in devices and equipment. Therefore, it is able to support and run parallel processes and analyze larger sets of data.
  • Inferencing: Lastly, this aspect deals with executing a trained AI model to generate inferences and predictive insights. Therefore, it enables AI models to improve and enhance efficiency in collecting, processing, and analyzing data.

What are the key AIoT Technologies?

It is critical to understand that there are key technologies that configure AIoT. Hence, here are the technologies that facilitate automation and machine intelligence.

  • Artificial Intelligence

Firstly, AI helps enhance IoT devices to be more accurate and efficient. It also processes data more logically by effectively utilizing intelligent sensors in IoT devices. Moreover, it enables the real-time processing of mission-critical and complex applications.

  • Hardware Accelerators 

Companies are certainly encouraging the advancements and capabilities of IoT technologies. Moreover, they are developing the devices to easily execute and perform AI, Deep Learning, and Machine Learning models. 

  • 5G Networks

5G is the next generation of wireless connectivity. It can also deliver an ultra-fast speed which is 100x faster than 4G/LTE. It also increases the number of connected devices by 100-fold. 5G networks will boost the applications of AIoT by making them more robust, mobile, reliable, and efficient.

  • Big Data

“Data is the new oil” is a quote we are all familiar with. Additionally, the usage of IoT increases the collection of data through devices and networks. Hence, with the explosion of data developers aim to train AIoT devices with more intelligent Deep Learning models. As a result, the devices will collect, filter, process, and analyze the data for accurate insights.

Here are the top Benefits of AIoT

Justin Richie, Data Science Director at Nerdery, states, “Robotics will greatly benefit from this as the IoT device management grows into more AI concepts. As devices begin to interact with humans and the experience grows in sophistication, AIoT will be much more involved.”

Therefore, here are the top benefits of AIoT and its applications in business:

  •  Amplifying the Scalability of IoT:

AIoT and its applications help expand the horizon for IoT and its devices. Moreover, it helps enhance existing processes and innovate new features. 

  • Improving Operational Efficiency:

Businesses often look for more efficiency from their resources for optimum results. Hence, AIoT enables to enhancement of resources and execute various operational tasks. Moreover, AIoT also provides insights to recognize patterns and comprehend malfunctions to prevent them.

  • Expanding New Horizons for the Business:

AIoT paves the way for new requirements and advancements in technology. Its solutions can also cater to multiple businesses and industries. Hence, it provides great opportunities for businesses to develop new products for various requirements.

  • Managing Risks:

Businesses often face the threat of risks while investing in or building new ventures. Although, there may be a few risks in long-term ventures as well. Hence, with AIoT businesses can identify loopholes and implement more secure protocols to prevent and tackle situations.

  • Monitoring Real-Time Actions:

With the help of AIoT Technologies, businesses can now monitor real-time tasks and processes. Hence, it helps keep continuous supervision on the devices, equipment, and software. As a result, it autonomously resolves any discrepancy or issue within the process.

Here are the Real-Life Applications of AIoT

  • Tesla’s Autonomous Cars

The development of autonomous cars has transformed the automobile industry. Moreover, the concept of these cars was only science fiction a few years ago. But now, the manufacturing of autonomous cars has become a reality with the help of AIoT.

For example, Tesla’s autonomous cars provide a variety of autopilot features to a driver.
It is also a hub of sensors, imaging devices, gadgets, cloud architecture, and data processing models. Hence, all these technologies work synonymously to help Tesla’s cars become autonomous.

Further, the car gathers data from various sources to determine road and weather conditions. Above all, Tesla encourages their, users, to drive the car often as it becomes more autonomous and accurate in driving decisions.

  • Boston Dynamics’ Spot

The manufacturing industry is always notorious for using the latest technology to ease their processes. Robots, AI, IoT, etc. are some of the latest ventures in Industry 4.0. Moreover, Robots in factories are now adapting to modern requirements and providing smarter solutions. Therefore, with the emergence of AIoT robots have now become more intelligent and sensitive towards various processes.

Boston Dynamics has introduced various robotic solutions, one such solution is Spot. it is an agile mobile robot that navigates through various terrains to capture data. Further, it is a great solution for more efficient and predictable execution of tasks.

  • ET City Brain

Applications of AIoT Technologies are making cities smarter and safer. Therefore, ET City Brain is one such real-world example of AIoT application. 

The city is the innovation of Alibaba Group. Moreover, the platform uses AIoT technologies and solutions to monitor traffic, road usage, accidents, etc.

Above all, this concept requires big data computing and neural networks to process large sets of data. As a result, it reduced the traffic in China by 15% with the help of AIoT.

  • Amazon Go

Amazon Go is a concept store by Amazon and it revolutionizes the retail industry. Moreover, it allows users to walk into stores, pick up an item and leave. There are no cashiers present in these stores, although they do have IoT devices that are capable of deducting money from the user’s e-wallet.

Additionally, with AIoT, when a user picks up an item it is added to their cart, but when they keep it back it is automatically deleted. Also, when the user leaves the system detects the items in their cart and processes the payment.

As a result, AIoT has made shopping easier and bringing new experiences to customers.


In conclusion, the applications of AIoT influence industries to integrate large-scale systems with various modern solutions. Moreover, it can combine machine learning with IoT and the system to develop “learning machines”.

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