Diversify into Artificial Intelligence of Things (AIoT)

Evolution of Internet of Things (IoT)

Combination of IoT and AI

IoT refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves. Internet-of-Things (IoT) channels internet connectivity, data processing, and analytics to the world of physical objects.

Ever since the instigation of Industry 4.0, Internet-of-Things (IoT) has shown exponential advancement in such a short period of time. Evolving from Internet-of-Things (IoT) to Industrial Internet-of-Things (IIoT), now technology has extended its capabilities towards Artificial Intelligence (AI), venturing into Artificial Intelligence of Things (AIoT).

With such expeditious advancements in technological breakthroughs, AIoT (Artificial Intelligence of Things) is redefining and reforming industrial sectors. Enlighting the way for intelligent task execution with real-time analysis while IoT channels the communication scale between devices. The combination of these two technologies makes each other’s applications more effective and powerful.

Sequential Steps of Artificial Intelligence of Things (AIoT)

AIoT Steps


Phase 1: Data Collection

Data collection is the primary phase, in which data is gathered via connected IoT devices.  A single device may incorporate with multiple sensors to manage different types of data. It is possible to connect various sensors to a single device for collecting different kinds of data.

Phase 2: Data Transmission and Storage

Due to the bulk of the data, it is processed further and then stored to the Cloud. Cloud storage minimizes the overall cost of storage, as organizations don’t need to spend a tremendous amount on hardware installation for data storage.

Phase 3: Data Processing

Data processing involves different phases like data extraction from the cloud, data cleaning and making it anomalies-free, data conversion into a standard format, and applying algorithms for deriving insights.

Phase 4: Data Prediction

After processing the data, the machine learning algorithms help in predicting future events. It is simpler to make predictions based on the results after the pertinent models have been created. For example, clustering models predict image patterns, anomaly detection models predict possible faults, and text-based models allow text classification and entity recognition.

Phase 5: Actionable Insights

After making the predictions, the eventual step for machines is to take action as per the generated insights. Insights and advanced dashboards assist in aligning business goals, fine-tuning processes, and creating future strategies. The data visualization tools like Tableau and Microsoft Power BI effectively visualize massive data having millions of data points. Data points and predictions help take real-time actions. For example, visual plotting is beneficial to create reports in detail.

Benefits of Artificial Intelligence of Things (AIoT)

Benefits of Artificial Intelligence of Things (AIoT)

Accelerate Innovation

Improve products and services by generating more value through IoT-generated data. Artificial Intelligence (AI) enables IoT devices to utilize data recollection to further analyze, learn and spool decisions without human input.

Boosting Operational Efficiency

Heuristics and patterns in data may be created, analyzed, and recognized using machine learning using AIoT-enabled applications. Swiftly offering operational insights, identifying and resolving complications, and automating manual procedures. Achieving the peak of operational efficiency with Artificial Intelligence of Things (AIoT).

Advanced Risk Management

Risk management within any industry is crucial. The capacity to predict dangers in certain futures, allowing the implementation of precautionary actions. Anticipate and manage potential obstacles in the future with the aid of AIoT technology.

High Scalability

Enabling more IoT devices to be connected to one another within an ecosystem thanks to AIoT, increasing scalability. Unlocking more potential optimization within current processes with Artificial Intelligence (AI), managing resource gateway to the specific components.

Internet-of-Things (IoT) Use Case Examples

Manufacturing and Production

Manufacturing Factory

Manufacturing industries are constantly under the compulsion of efficiency and productivity improvements while being cost-effective. With the advancement of Internet-of-Things (IoT), it has unraveled potential unprecedented disruption to the challenges. Manufacturing industries now have the ability to track data from multiple machines and pieces of equipment within the facility, exhibiting patterns and uncovering improvements courtesy to IoT technology.

The capabilities of Internet-of-Things (IoT) enable manufacturers to gain greater visibility and insights into their operations through the efficient utilization of data and the tighter integration of disparate systems. This opens up the possibility for manufacturers of moving away from simply selling products to becoming a provider of services and strengthening the relationship they have with the end users of their products.

The motivation for deploying Internet-of-Things (IoT) in manufacturing environments is, as always, money. As a result, products can be produced at a lower cost and sold for a higher profit. However, there are other benefits that are just as significant and can ultimately be equated to cost preservation. These include savings from maximizing worker productivity, minimizing energy consumption, saving costs, and extending machine life and line uptime through predictive maintenance.

Industrial manufacturing is primed for tremendous IoT innovation as businesses are engaged in the technology and the benefits it can offer. This innovation comes at a time in which industrial manufacturers are looking to transform their businesses for the digital era and the insights, flexibility, and automation IoT can provide them with will help them avoid flying blindly into this new era because of the highly granular, real-time insights IoT empowers them with.



As the world becomes more connected, the adoption of disruptive technologies within the healthcare industry became repulsive, ranging from automation to advanced machine learning. Technology is landing thorough impacts on how healthcare industry operates, diversifying the potential of lowering traditional costs while escalating productivity levels became possible.

The question is “How”.

Regardless in terms of patients’ diagnosis, equipment, or medical drugs manufacturing. The solution Internet of Things (IoT) and Artificial Intelligence (AI) provide within the healthcare industry includes configured connected sensors to aid the protocol or processes within the industry. Through data-driven approach, collecting real-time data from a variety of sources, particularly equipment. Real-time continuous data transmission could be provided via the transport layer. Healthcare could easily manage predictive loss   AIoT ventures new dimensions for monitoring processes, improving experiences in both patients and healthcare sectors.



Managing a facility is a task that encompasses a broad variety of day-to-day responsibilities, whether it’s healthcare, manufacturing, financial, or any different industry. Keeping track of assets, and ensuring customer satisfaction and safety reach its peak, all while generating revenue, is challenging.

The Internet of Things (IoT) has the potential to transform facility restraints into substantial breakthroughs. Unlocking more efficiency in resource utilization and productivity through the capacity to unite all the sectors of the facility to track and monitor multiple operations simultaneously. Driving new insights with the data-driven solutions provided by Internet-of-Things (IoT) technology such as sensors, embedded beacons, ID trackers, etc. Offering a wide range of automation opportunities, reducing obstruction in communication and human errors.

In a nutshell, facilities were once driven by human oversight, but with the difference of the Internet of Things (IoT). The plethora of IoT solutions incorporated within facilities offers a unique perspective on how facilities are operated. Thus, resulting in terms of operations, maintenance, supply chain perspectives, compliance, and optimization, soaring towards new heights.

One perfect example would be the invocation of Covid-19, where strict SOPs were established. Click here to look at how IoT assisted in the pandemic.

Glad to See Your Interest in Knowing More

Undoubtedly, the amalgamation of AI and IoT, unfolding AIoT is gaining much traction as businesses are leaning towards digitalization. As these technologies’ capabilities continue to develop, they are currently being used in every industry where information and problem-solving may improve results for all stakeholders. AI and IoT are two distinct areas, but evolution has led organizations to integrate both technologies as a single unified. Redefining Industry 4.0 and industrial revolution via the convergence of AI and IoT. Let’s continue with more use-case examples.

Renewable Energy


renewable energy - solar panels and wind turbine sensorsIt’s not understated that Renewable Energy will drive futuristic transformation. This adoption has continuously prevailed at an unprecedented rate, unlocking smart energy solutions. Utilizing modern-day technology, the integration of IoT and AI in renewable energy segment is enabling its expansion to a great extent. Mitigating challenges that are limiting the acceptance of renewables.

AIoT sensors located on renewable energy assets can regularly monitor and analyze the volumes of data across them. The sensors can effectively manage congestion on the transmission line and ensure that all power grids are operating optimally.

As a result, the energy sector can transition from a dated centralized system to distributed, smart, and integrated network. An essential key when it comes to deploying renewable energy sources, particularly in regard to supplying energy at a local level and optimizing the grid. AIoT will aid the solar energy sector / the solar power industry to effectively manage, monitor, automate, control, and improve the efficiency of its renewable energy assets. Certainly, with a good energy management system (EMS) in place. Artificial Intelligence of Things (AIoT) will be an essential tool to achieve Net Zero Carbon.

You Might be Wondering What is Net Zero Carbon

Net Zero Carbon refers to the overall balance between greenhouse gas emissions produced and greenhouse gas emissions taken out of the atmosphere.

Therefore, when we talk about the energy from the Sun that makes its way to Earth. It can have trouble finding its way back out to space. Some of this energy is absorbed and released by greenhouse gases as a result of the greenhouse effect.

To avoid a climate catastrophe, emissions of greenhouse gas must be as low as possible. In other words, achieving “real zero”. Now energy industries are heading toward low-carbon electricity. Parting ways with traditional fossil fuels.



The application of AIoT within the retail industry is flourishing. In terms of customer behaviors, reducing theft, and streamlining shopping experiences. AIoT has enabled several potential solutions to improve the retail industry’s overall processes and operations.

Let’s dive into some examples, shall we?

Incorporating AI and IoT makes video surveillance for security purposes a lot more efficient and smart. Contrary to conventional Video Management Systems (VMS), which demand human operators to monitor video feeds.

This means that the video content depends on limited attentiveness, subjective judgment, and splotchy reaction times. AIoT, leveraging machine learning algorithms, analyzes the video feeds in real-time, detects objects, and objectively recognizes people and events.

Strategically and logically placed sensors across a store can detect and transmit various types of data over wired or wireless connections. IoT devices normally send all of their data to an in-store gateway device, which aggregates it and sends it to a server or the cloud. This data is then queried by the relevant business application tool to analyze the structured information to eventually generate insights for further action.


Agriculture Livestock

The emerging technology has also established immense potential in revolutionizing the shape of agriculture. Not limited to sensors, image processing, cloud computing, and artificial intelligence (AI). AIoT has made agricultural production, management, and marketing more autonomous and efficient.

Let’s discuss the application of AIoT in the Agriculture Industry:

Precision Agriculture

The goal of precision agriculture is to analyze the data, generated via sensors, to react accordingly. It helps farmers to generate data through the help of sensors and analyzes that information to take strategic actions. There are numerous precision farming techniques like irrigation management, livestock management, vehicle tracking, etc which play the role of increasing efficiency and effectiveness. With the help of Precision farming, farmers can also analyze soil conditions and other related parameters to increase operational efficiency.

Data Analytics

With the real-time status of the crops captured from sensors,  the capability to analyze weather conditions, livestock conditions, and crop conditions became possible.

Utilizing predictive analytics, farmers can get insight following the analytical trend to predict upcoming weather conditions and harvesting of crops. AIoT has enabled agriculture sectors to maintain the quality of crops and fertility, enhancing product value and volume.

Although there might be barriers such as cost factors and the adaptation to the traditional agricultural practice. The applications of AIoT will become the driving force for smart agriculture. With seamless end-to-end intelligent operations and improved business process execution. Production gets processed faster and reaches logistics in the fastest time possible.


Technology is growing and undeniably permeating every aspect of modern life. Although AIoT can analyze telemetry data from a vast quantity of connected devices in real-time, there are certain basic problems with this technology that need to be resolved. Those issues are mostly on the software side, both in the operating system and the application code, and the proper resource allocation and segmentation. Despite this, AIoT architectures are vital in industrial settings capable of envisaging equipment failures and shutting down damaged machinery before an accident occurs, eventually driving industry 4.0 innovations in years to come. As the technology matures and more vendors begin to compete, solutions will become more refined. Technology by itself is meaningless unless the experience created with it has value.








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