Artificial Intelligence is frequently paired with other technologies-cloud, data analytics, and, recently, the Internet of Things (IoT). The latter merger paves the way for a new trend-AIoT. How can AI improve the performance of IoT devices? When does it make business and operational sense to integrate AI and IoT? What should you keep in mind while making the decision about the AIoT applications? Read on to discover.

What is AIoT is and how does it work?

AIoT combines the functions of AI algorithms, such as data processing and analytics, and IoT systems, such as data collection via sensors and actuators.

The combination of AI and IoT creates connected systems that gather, process, and act upon real-time data.

Artificial Intelligence and Machine Learning enhance the capabilities of IoT devices by enabling them to gather, process, and analyze data more intelligently, leading to improved automation, efficiency, and decision-making. IoT, in turn, increases the value of AI by facilitating connectivity, signaling, and seamless data exchange. Together, they create a connected system that captures the data, processes it in real-time or near-real-time, and performs algorithm-based actions and predictions.

An AIoT system consists of the following layers.

AIoT systems include swarm intelligence, cloud, Digital Twin, edge device, on-device AI, and connectivity layers.

  • Edge devices. They include the physical devices equipped with sensors or actuators that collect data from the environment or interact with it and the components necessary to perform on-device AI computation.
  • On-device AI. These algorithms can run close to or directly on the device hardware to make more intelligent decisions based on the device's use and user behavior. For example, it can extract specific data from video streams and process it before sending the results to the cloud (e.g., instead of sending the whole traffic stream, the algorithms send only the video clips of the cars that violated traffic rules.)
  • Connectivity layer. It includes communication protocols and networks (Wi-Fi, cellular networks, or other wireless technologies) enabling IoT devices to transmit data to the cloud.
  • Cloud layer. System-wide AI computation is performed in the cloud. The cloud is also a home for the physical device's virtual copy (i.e., digital twin), which represents the digital environment of the physical asset. Through the digital twin API, AI algorithms can analyze data from sensors, IoT devices, and other sources, and interact with the real-world assets based on the results of data analysis.
  • Swarm intelligence. In AIoT, swarm intelligence algorithms enable distributed decision-making, adaptive learning, and robustness across a specific set (swarm) of assets and environments. These algorithms empower interconnected IoT devices to collaborate, share information, and coordinate actions autonomously, improving complex systems' efficiency, scalability, and resilience.

Appropriately integrated AIoT systems can enhance various industries, such as healthcare, manufacturing, smart homes, and smart cities. Let's review what makes a combination of AI and IoT an interesting topic to explore.

Learn what the future holds! 6 hottest IoT trends in 2024: using the power of connected devices

Why should you consider AI integration into IoT devices?

The task of AI in IoT applications boils down to a few core functions:

  • Process large data sets from IoT devices;
  • Identify specific patterns/objects;
  • Compare patterns to predict further scenarios;
  • Suggest (and perform) the next steps, supervised or not.

From a business standpoint, combining AI and IoT can improve device performance and enhance data analytics. However, it's not the full list of reasons AIoT can be beneficial. Let's explore the less obvious ones in more detail.

The benefits of AIoT include Advanced analytics and large-scale data collection, predictive maintenance, anomaly detection, and others.

Advanced analytics and large-scale data collection

AIoT technology can process large volumes of data generated by IoT devices in real-time, identifying valuable insights and patterns that humans may overlook. AI components can process complex data from the IoT sensors and filter it on the edge before sending the results to the cloud to uncover valuable patterns, trends, and correlations. By analyzing data more effectively, businesses can optimize operations, understand the environments in-depth, and make better data-driven decisions.

Predictive maintenance and anomaly detection

AIoT systems detect anomalies by leveraging AI algorithms to analyze data collected from IoT devices. They can identify abnormal patterns or deviations from expected behavior by processing large volumes of sensor data in real-time. This approach reduces downtime, minimizes costly repairs, and extends the lifespan of assets. As a result, it helps prevent potential failures, optimize operations, and improve overall system reliability and performance.

Enhanced continuous monitoring

AI-powered IoT devices are capable of sending a stream of real-time data for further processing by AI algorithms. The latter can analyze the patterns and detect anomalies perpetually, making it a great solution for products and solutions where continuous monitoring is critical (e.g., medical devices, safety cameras, etc.).

Better understanding of data context

AI algorithms process data considering various factors, such as time, location, user behavior, etc. It helps Natural Language Processing (NLP) systems to respond to human interactions, adapt based on them, and provide contextually relevant services. It also sets a foundation for personalizing the user experience.

Personalized experiences

IoT devices with AI can personalize user experiences by analyzing user behavior and preferences. For example, smart home devices can adjust settings based on individual preferences, while wearable health trackers can provide personalized fitness recommendations. It can help businesses increase customer satisfaction rate and improve loyalty.

Read more: List of 15 IoT service providers to consider for your next project

Automation and efficiency

AI-enabled IoT devices can make autonomous decisions based on the analyzed data. It includes automating repetitive tasks and processes, adjusting device settings, triggering specific actions, or dynamically adapting to current conditions. Automation improves efficiency, reduces errors, lowers operational costs for businesses, makes systems more agile and responsive, and decreases latency in decision-making.

Improved security

AI algorithms can identify and address security threats in IoT networks by analyzing patterns and detecting anomalies. Adaptive security measures increase the system's ability to respond to emerging threats and vulnerabilities. It allows businesses to maintain the security of their IoT environments and keep IoT data confidential.

Real-time network optimization

On-device AI in IoT gadgets processes data in real-time and minimizes the amount of the information which needs to be transmitted over the network. This decreases latency and lowers cloud costs. In addition, AI enables dynamic bandwidth allocation based on the real traffic conditions and application requirements.

Overall, integrating AIoT technology enables businesses to maximize their IoT investments, driving innovation, competitiveness, and value creation. The number of use cases grows as the AIoT technology develops. It can potentially perform more tasks and will be used across more industries. Let's review a few that already benefit from AIoT applications.

Explore more: AI in the automotive industry: Fueling a smarter, safer driving experience

Top 7 industries adopting AIoT applications

The combination of AI and IoT is best for industries that deal with an extensive network of sensor-based devices and data sets obtained from them. Here are a few examples of AIoT applications worthy of investment.

Manufacturing, logistics and supply chain, healthcare, retail, Smart home, Smart city, and agriculture industries benefit from AIoT applications.


Modern manufacturing machinery is usually equipped with IoT sensors that collect the engine's performance data. With AIoT technology, the information from the sensors is analyzed, and depending on the vibration characteristics, the AI component predicts the nature of this behavior and suggests what should be done to keep the optimal performance. It is excellent for predictive maintenance, leading to safer working conditions, uninterrupted performance, longer equipment lifecycle, and reduced downtime. In addition, AIoT helps improve quality control on the production line.

Example: Sentinel, a pharmaceutical plant monitoring system by IMA Pharma, uses AI to analyze data from sensors along the production line to ensure the quality and efficiency of the drug production. Based on the sensors' data, AI identifies and optimizes underperforming components and keeps the machines running efficiently.

Logistics and supply chain

From the transfer fleet and autonomous warehouse delivery vehicles to scanners and beacons, IoT devices within this industry transmit plenty of data. Powered with AI, it can be used for tracking, analytics, predictive maintenance, autonomous driving, and so on, providing visibility into the logistics operations and strengthening partnerships with vendors.

Example: Amazon uses over 750,000 autonomous mobile robots that help warehouse staff with heavy-lifting, delivery, and package handling. Other examples include using AI-powered IoT devices like cameras, RFID sensors, beacon technology, and other tools to help track the flow of goods and locate the products within the warehouse and during transportation. AI algorithms can then calculate the estimated time of arrival (ETA) and predict possible delays based on traffic conditions.


The combination of AI and IoT in healthcare settings helps hospitals efficiently provide remote patient care and decrease the load on the facilities. In addition, AI can be applied during clinical trials to pre-process the data from the sensors used by the large pool of target and control groups.

Example: Smart wearable technologies allow doctors to monitor patients remotely. Sensors gather the vital signs, such as heart rate, blood pressure, and glucose levels in real-time. Then the AI algorithms analyze them to help doctors detect issues early, create customized treatment plans, and improve patient outcomes.


IoT sensors detect movement and customer flow in the building, while AI algorithms process the data and provide insights into traffic patterns and product preferences. This data helps understand customer behavior better, prevent stockouts, and improve customer analytics to boost sales. Moreover, AIoT helps retailers provide personalized shopping experiences by using geographical data and shopping preferences.

Example: IoT sensors detect movement and customer flow in the building, while AI algorithms process the data and provide insights into traffic patterns and product preferences. This data helps understand customer behavior better, prevent stockouts, and improve customer analytics to boost sales.

Smart home

The smart home ecosystem includes smart thermostats, locks, security cameras, energy, heating, lighting, and entertainment. AI algorithms process data gathered from these devices and can provide context-specific recommendations for each user. This allows homeowners to use utilities efficiently, create a personalized living environment, and reach sustainability goals.

Example: LifeSmart provides a wide range of AI-powered IoT tools for smart homes, connecting the new and existing smart appliances and allowing customers to control them on the smartphones. In addition, they offer an AI builder framework to deploy AI on smart devices, edge gateway and cloud so that AI algorithms can process data and user behavior and act upon it autonomously..

Smart city

Smart city is another area where AIoT applications can improve the well-being of citizens, urban infrastructure planning, and future city development. Except for traffic management mentioned above, IoT devices with AI components can monitor energy consumption patterns, predict demand fluctuations, and dynamically adjust energy distribution. AI-powered surveillance cameras and sensors can detect suspicious activities, monitor crowd density, and alert authorities to potential security threats in real-time, enhancing public safety and security.

Example: Bosch's INTEOX camera platform with integrated Traffic Detector, a video analysis function based on neural networks, detects and locates vehicles under any traffic situations and lighting conditions and sends the data to the cloud for further AI evaluation. Later, the algorithms optimize traffic lights in the affected areas, helping eliminate traffic jams and detect accidents.


According to recent Continental research, over 27% of surveyed farmers use drones to analyze land from the air. These devices capture the images of the crops as they are and send them to the dashboard for further evaluation. However, AI can take this further.

Example: AIoT-powered drones can capture images of crops at every growth stage, identify whether the plants are healthy or have diseases, and suggest the next step to harvesting the maximal yield. In addition, these drones can be used for targeted crop treatment, irrigation monitoring and management, soil health analysis, and more.

AIoT applications help businesses from various industries leverage the data from IoT devices to make better decisions, automate processes, and improve customer experience. While this technology can improve business outcomes, there are a few things to consider before proceeding.

Explore the topic: Industrial IoT: best practices and key use cases

Challenges of the AIoT adoption

Businesses with high-risk factors and strict regulations, such as healthcare, aviation, and manufacturing, should consider the possible challenges of merging AI with IoT. Let's review a few.

N-iX will help you deal with the challenges of the AIoT adoption, such as AI unpredictability, strict regulations, and increased computation costs.

  • AI unpredictability. AI algorithms can be unpredictable due to their complexity and the inherent uncertainty in real-world data. In safety-critical applications, such as autonomous driving vehicles (ADVs) or medical devices, unpredictable AI algorithms can pose risks to human safety if they make erroneous decisions or fail to respond appropriately to changing conditions.
  • Strict regulations. Even though AI-based IoT could theoretically benefit many devices in healthcare, automotive, aviation, and other industries, regulations like the EU's AI Act define the conditions for implementing AI. For example, AIoT technology can be used for hospital ward management and surveillance. Still, it's strictly regulated if used in implanted devices or other tools that pose significant risks to fundamental rights, health, or safety. The AIoT applications that use or misconfiguration pose the mentioned risks and must obtain approval from the regulators before selling their devices.
  • Increased computation costs. Integrating AI with IoT solutions means increased data to analyze and the need for more bandwidth. It leads to growing cloud and hardware budgets and possible redesign of the existing IT infrastructure.

You should also keep in mind that adopting AI is unlikely to add any functional value to the product if your solution isn't supposed to process a large cluster of data or doesn't need the results from such a process for further actions.

N-iX verdict regarding AIoT application: Consult first

AIoT technology can improve the business's ability to analyze data in real-time, enhance predictive maintenance, and save repair costs-these are just a few possible benefits.

Since the market of AI-powered IoT shows a steady growth dynamic with an expected $83.6B by 2027, according to Business Wire, more businesses will explore the feasibility of its application in their operations. If you decide to add an AI component to your IoT ecosystem, we recommend getting expert advice from IoT specialists and requesting consultation with AI engineers.

First, it will help you better understand the capabilities of your current infrastructure. Second, they can determine what should be changed to ensure successful adoption. Third, they may find other ways of getting the results you opt for. Not every IoT device requires AI integration to deliver the expected outcomes, and you may save a lot of resources by not following the trend.

However, there are cases where the investment in AIoT applications is justified, and you need a skilled and experienced tech team to integrate the technology into your organization.

How can N-iX help you adopt AIoT?

With over 21 years of experience providing exceptional software services to global enterprises, we have the resources and skills to consult, develop, and deploy AIoT technology into your organization. More than 160 global leaders from manufacturing, retail, healthcare, automotive, finance, logistics, agritech, and other industries trust their software innovation with our experienced engineers.

We have strong expertise in technologies that enable AIoT, such as cloud computing, Big Data, Machine Learning, data analytics, computer vision, generative AI, PRA, embedded, and cybersecurity. This ensures we can help your business adopt the best-fit solution that is effective for your specific problem and suggest innovations that are not only trendy but feasible.

If you need a consultation on AIoT technology or a team to work on an AIoT project, N-iX is ready to become your reliable tech partner. Contact us, and let's discuss what your business can achieve when you merge AI with IoT!


  1. Smart Home System & Solutions Provider - LifeSmart
  2. Improve production efficiency with Sentinel - IMA Digital
  3. Global Artificial Intelligence of Things Solutions Market Report 2022 - Business Wire

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N-iX Staff
Mykhaylo Kohut
Solution Architect, Embedded & IoT Practice

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