IoT in mix with AI | HCLTech

IoT in mix with AI

April 26, 2022
Ashish Mishra


Ashish Mishra
Senior Technical Lead
April 26, 2022

At present, the business world is changing with the adoption of IoT (Internet of Things), where it captures a huge amount of data from multiple channels through multiple devices. Hence, because different kinds of data are getting stored from an “n” number of devices, collecting, analyzing, and processing this data becomes complex. Considering the fact that it requires a new technology to identify the full potential of IoT devices which is happening quite brilliantly with the emergence of artificial intelligence (AI).

As AI enters the world of IoT, the devices become more capable of learning from user interactions, service providers, and even any other devices which are part of that ecosystem. With AI, they adjust to fresh inputs or any other changes to achieve or fulfill the ultimate tasks without any manual effort.

With this union of AI and IoT, organizations will be able to achieve better decision-making with zero errors.

Currently both startups and big companies prefer AI to leverage the full potential of IoT. Leading organizations such as Oracle, Microsoft, Amazon, and Salesforce have already started this activity for their IoT applications.

Expansion of IoT into AIoT (Artificial Intelligence of Things)

At present, the practical existence of IoT can be seen in many areas. From home automation to self-driven cars, smart wearables to smart cities, and smarter robots to healthcare systems where AI and IoT are optimizing the entire system in tandem.

In fact, artificial intelligence and the Internet of things are the frontrunners in top technologies where companies are willing to invest more to increase efficiency and provide a competitive edge.

IoT in mix with AI

Difference between AI and IoT

IoT connects different devices/systems and accumulates the data collected from all of them. In contrast, AI is all about learning data and its automation by leveraging a wide range of statistical and computational techniques.

Benefits of AIoT

  • Refines automation of manual work where the system is able to make independent decisions which can increase productivity and efficacy
  • Understands and predicts the vast range of risks and automates for a prompt response
  • Develops new products or services and enhances the existing ones with faster product updates and releases
  • Helps in increasing IoT scalability
  • Helps in reducing/eliminating the unplanned downtime, which can prove to be too costly before its arrival

In addition to the above benefits, IoT platforms strengthened by AI can provide militant support to our private data and wouldn’t allow third parties to breach it. System-to-system interaction is facilitated by various companies to detect incoming threats and deliver automated responses to hackers. A generic example is the banking sector, where illicit activities in ATMs are traced by IoT sensors and conveyed immediately to law enforcement agencies.

A very important example that stamps the authority of what AI and IoT collectively bring to the table also take care of the civic decorum, and hence it also showcases the success of the technology. Smart traffic management, smart parking, smart waste management, smart policing, and smart governance are some innovations that evolved the smart city concept.

Accomplish great results with AIoT

For an organization to leverage the benefits of AIoT, the following implementation will augur well for successful deployment:

IoT in mix with AI
  • Real-time analytics: With the help of event stream processing, analysis of diverse datasets happen, followed by identification of most relevant incidents.
  • Leveraging application analytics: Based upon the use case of the technology, deployment of analytics happens for a specific purpose.
  • Merging of AI: To make AIoT successful, AI should no longer be deployed in isolation. It must work in close contact with machine learning, natural language e-processing, and computer vision to achieve optimum results.
  • Unification of the life cycle: To become successful in scaling, AIoT must have access to each possible dataset before sanitizing it for deeper insights.


With this union of AI and IoT, organizations will be able to achieve better decision-making with zero errors. AIoT solves a problem that existed during the birth of IoT, which was managing real-time data received from various connected devices at once. Hence, IoT devices are reaching new heights day by day.


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