AIoT: Beyond the Hype, What's the Reality?
AIoT is booming - hailed as the next revolution in connected devices. But is it always necessary? At 1NCE, we focus on real-world business impact rather than hype. While AIoT can unlock powerful efficiencies, many companies achieve their goals with IoT alone. The key is knowing when AI (Artificial Intelligence) adds value and when it’s just an expensive add-on.
According to Transforma Insights, the number of AIoT connections will grow from 1.4 billion to 9.1 billion by 2033. The reason? Businesses want smarter, more autonomous systems. However, AIoT is only valuable when it drives cost savings or increases revenue – not when it’s implemented for the sake of technology. Just like IoT, AI is a technological concept, and it should only be applied where it makes sense and where real business problems can be solved. Let’s dive deeper into this topic.
The Power of IoT without AI
Not every IoT solution requires AI. In many cases, sensors, real-time monitoring and alerts provide all the insights businesses need – without the added complexity of AI. This configuration is effective when the objective is to track and monitor fundamental metrics in real time and when complex algorithms are not needed for decision-making. Here are some examples:
Asset Tracking: IoT sensors are used in logistics and inventory management to track the position and condition of assets like vehicles, shipments, and equipment. The data from these sensors is sent to a centralized/cloud system, which shows the asset's present condition. This data is adequate for observation and, if necessary, human intervention. Without the use of AI, alerts can be triggered if any conditions deviate from configured thresholds.
Environmental Monitoring: IoT is effective in scenarios of monitoring environmental conditions, such as in agriculture, environmental science, or building management systems. Sensors collect data on temperature, humidity, air quality, and other factors. If a sensor detects an issue, an alert is sent. This process doesn't require AI and helps make informed decisions based on predefined conditions and thresholds.
Basic Predictive Maintenance: IoT may gather the signals and send alerts when values exceed predetermined thresholds in industrial use cases, where device failures are predictable based on basic data points, such temperature or vibration levels. In these situations, IoT's simplicity—focusing on data collection and fundamental alerts—provides the required functionality without AI, being a cost-efficient and effective option.
In these cases, AI doesn’t just add complexity—it’s unnecessary. IoT’s simplicity and cost-effectiveness often make it the best choice for tracking, monitoring, and automation without overengineering the solution. Businesses should resist the temptation to add AI just because it’s trending and instead focus on getting the fundamentals right—that’s where 1NCE ensures reliable, global IoT software & connectivity.
“AI is only as good as the data it learns from. Without reliable, uninterrupted connectivity, AI is just an expensive experiment. At 1NCE, we make AIoT work by ensuring seamless, global data transmission—so businesses can trust their AI to deliver real intelligence, not guesswork.” — Fabian Kochem, Head of Global Product Strategy at 1NCE
When AI Makes IoT Truly Smart
But what about when IoT alone isn’t enough? AI comes into play when businesses need more than just data collection—when they need predictive insights, automation, and intelligent decision-making. This is especially true in data-intensive applications, such as real-time video analysis or large-scale fleet monitoring, where AI can reveal hidden patterns and drive automation.
From a technical point of view, there’s two main approaches in AIoT: Edge AI, where processing happens directly on the device, and cloud-based AI, where data analysis occurs remotely in the cloud. In Edge AI devices, like those with emerging AI-optimized Systems-on-Chips (SoCs), real-time decisions can be made locally, reducing latency and dependency on cloud infrastructure. On the other hand, cloud-based AI uses scalable, 3rd-party resources to perform complex analyses on large datasets. In cloud-based AI, IoT sensors can remain relatively simple, as they only need to collect and transmit data, leaving the complex analysis to be performed in the cloud. Some IoT applications combine both approaches—using Edge AI for immediate processing and data filtering, and cloud AI for deeper insights and more sophisticated tasks.
There are industries and applications where AI adds considerable value by taking data from IoT devices and making detailed predictions, optimizing operations, or providing real-time decision support.
Autonomous Systems Applications: These are prime examples of how computer vision and Deep Learning AI technologies can be applied in AIoT Drones and self-driving cars, for example, mostly rely on AI to process data from a range of IoT sensors, such as GPS, LIDAR, and cameras. These systems must constantly analyze their surroundings and make decisions about path optimization, obstacle avoidance, and navigation. While sensors provide the data, AI enables the system to autonomously respond to that data in real time. In critical applications such as self-driving cars, local data processing ensures that safety mechanisms—like emergency stop processes—are triggered instantly, without the delays inherent in transmitting data to remote locations.
Predictive Maintenance with AI-Driven Insights: In industrial environments with sophisticated machinery, AI analyzes data from IoT sensors and detects patterns that people may miss. Instead of purely sending alerts when thresholds are breached, Machine Learning (ML) algorithms are applied to analyze historical data and predict potential failures or inefficiencies before they take place. This reduces the need for remote data transmission and ensures that necessary diagnostics happen locally.
Smart Factory Optimization: In manufacturing, IoT devices collect information like device health, production speeds, and inventory levels. AI processes this data to find inefficiencies, optimize supply chain, and adjust production schedules. By ML analysis of data at a higher resolution and closer to the source, AI can enable deeper actionable insights. This prevents delays from data transmission to remote locations and extends to predictive maintenance and pre-emptive action on machinery.
Healthcare Monitoring and Diagnostics: AI in healthcare relies on IoT devices like wearables or medical sensors that monitor vital signs and other patient data. AI analyzes this data, recognizing patterns that can help predict health issues, diagnose conditions, and alert healthcare providers to emergencies. The AI’s Machine Learning abilities to analyze and correlate vast amounts of data in real-time helps to improve patient outcomes, where IoT alone would offer raw, unprocessed data.
CCTV for Security: Deep Learning-based AI algorithms can support activity recognition analyses of high-resolution video feeds locally, eliminating the need to send large amounts of data to remote locations for processing. Video feed analysis becomes faster and more detailed. For instance, AIoT-enabled CCTV cameras can monitor low-traffic areas, only transmitting small amounts of data when specific activities are detected. This minimizes bandwidth costs and reduces the need for constant human surveillance.
HVAC Systems: HVAC systems powered with Machine Learning and IoT can operate more efficiently by monitoring environmental conditions in real-time. Adjustments are made without delays. In case of a wide-area connection failure, these systems continue to function autonomously, minimizing the risk of service disruptions. On the other hand, traditional IoT systems depend on cloud connectivity, which may introduce delays and service interruptions.
The use cases clearly show that there’s a lot of value in AIoT – and more will be discovered., But AIoT is only as good as its foundation—reliable, global data transmission. That’s where 1NCE comes in. We ensure IoT data reaches AI-powered cloud platforms efficiently and securely, so businesses can focus on actionable intelligence rather than connectivity headaches.
ROI and Impact: Is AI or IoT Adding Value?
Businesses must consider whether integrating AI is worth the additional expense and complexity as they assess AIoT solutions. For AI to provide real value, it must solve problems that IoT alone cannot address. Without complex data patterns or the need for predictive analysis, an AIoT solution may be unnecessary and may be just a distraction rather than a solution.
The key to successful AIoT implementation lies in focusing on measurable outcomes. Businesses should consider the following metrics:
Cost Savings. Reduced downtime (e.g., predictive maintenance preventing costly equipment failures), optimized resource utilization (e.g., precision agriculture reducing water and fertilizer use), and improved operational efficiency.
Increased Revenue. New product and service offerings (e.g., personalized air quality recommendations), improved customer satisfaction (e.g., smart parking reducing driver frustration), and increased market share.
Improved Safety. Reduced workplace accidents (e.g., AIoT systems detecting unsafe conditions), improved environmental monitoring (e.g., early detection of pollution), and enhanced security (e.g., AIoT-powered surveillance systems).
Summary
The decision to integrate AIoT should be based on the specific needs and challenges of the industry or application. In some cases, IoT alone may be sufficient to meet business objectives by providing real-time monitoring and basic alerts. However, in industries requiring deeper insights, predictions, and automated decision-making, AI adds significant value.
Rather than implementing AI for the sake of novelty, businesses should focus on whether AI truly solves complex problems or simply adds unnecessary layers of complexity itself. By targeting applications where AI delivers clear and measurable benefits, companies can ensure that their AIoT solutions are not just trendy but truly impactful. At 1NCE, we ensure the accurate delivery of data from IoT devices to the cloud, where it can be processed and analyzed by AI-powered cloud solutions, such as those offered by manufacturers or AI cloud platforms.