Data quality
The risks of poor data quality are amplified in AIoT environments.
Data quality is a pillar of governance and refers to its accuracy, consistency, completeness, and timeliness. Poor data quality can lead to erroneous conclusions, biased models, and wrong decisions.
Therefore, it is necessary to implement rule validation, error detection, and dashboards, ensuring that detailed metadata such as origin, context, timestamp, confidence score, version, and processing lineage are maintained.
Data governance, with engineering techniques and attribute contextualisation, is a fundamental practice that ensures that AIoT system data is both usable and reliable.
Security
The exponential increase in connected devices means that the attack surface increases in equal proportion. Therefore, cybersecurity must be an element that is worked on from the conception of the project.
At the physical layer, the protection of sensors and actuators involves root-of-trust in hardware, zero-trust, secure initialisation mechanisms, breach detection, secure OTA (Over-the-Air) updates and firmware validation to prevent the execution of unauthorised code.
The inclusion of a Secure Element, a password safe within the device, also provides additional security by ensuring the privacy and irrefutability of the data source.
At the network layer, the use of authentication and encryption protocols (e.g., TLS and DTLS), VPNs, and intrusion detection systems is essential to protect data in transit.
At the processing and application layers, robust identity and access management (IAM), secure APIs, and hardened cloud environments are essential.
Edge computing provides additional protection by decreasing the volume of data in transit, thereby reducing exposure. In addition, AI models must be protected with integrity checks, continuous monitoring, and secure model update channels.
Ethics
With the autonomy of AIoT systems, ethical design is necessary to ensure that these technologies benefit society without causing harm.
Principles such as fairness, which requires AI models to avoid biases that could lead to discriminatory outcomes, and transparency, which ensures that AIoT systems provide explanations so that users and regulators understand how and why certain outcomes occur.
In addition, the ethical design of AIoT must take into account social and environmental impacts, promoting sustainability, data sovereignty and respect for user autonomy.
Practical applications
Some possibilities can bring significant benefits to a wide range of sectors. By way of example, I will mention just a few of the demands for AIoT systems that I have encountered most often:
Predictive maintenance
Predictive maintenance is the natural evolution of asset management. Instead of following fixed schedules or reacting only when a failure occurs, we now listen to the machine itself. IoT sensors capture data in real time: temperature, vibration, noise, pressure, tension. Connectivity brings this information to AI models, which analyse patterns, detect anomalies and learn continuously.
As a result, we can anticipate failures before they occur. In a smart factory, for example, the system can identify the first signs of wear in an engine or a lack of lubrication in assembly line equipment. This allows the team to act at the right time, neither too early nor too late, resulting in fewer unplanned shutdowns and greater efficiency in the use of resources.
The opportunities extend to dams, with fibre optic sensors, oil rigs with IIoT (Industrial Internet of Things) devices, smart buildings, vehicles and much more.
Over time, each operating cycle feeds new data into the system. Learning accumulates, models become more accurate and operation becomes more reliable.
Computer vision
Computer vision brings human perception into machines, allowing cameras and visual sensors to become the digital eyes of the operation. Much more than just recording images, computer vision is capable of understanding scenarios in real time: identifying objects, recognising patterns, measuring distances, monitoring flows and even detecting unexpected behaviour.
Images captured in factories, warehouses, shops, cities or hospitals are processed locally at the edge or sent to the cloud, where AI models apply classification, anomaly detection and predictive analysis. As a result, we have machines that not only see, but understand what they see.
In manufacturing, computer vision can detect millimetre-sized flaws in parts that are still on the production line. In logistics, it automatically monitors stock, reducing human error. In urban security, it identifies risky situations and triggers immediate alerts. In healthcare, it tracks patients in real time, recognising critical movements or vital signs. In retail, it understands consumer behaviour and provides insights to increase conversion and enhance the customer experience.
Over time, each new image analysed enriches the models, making them even more accurate.
Asset tracking
Asset tracking provides real-time visibility into an organisation’s physical assets, from vehicles and inventory to high-value tools and equipment. With IoT-enabled tags, such as RFID, GPS or BLE, it is possible to track the location, movement and status of these assets in factories, distribution centres or throughout the logistics chain.
The greatest value is obtained when AI comes into play, which not only shows where the asset is located, but also reveals how it is being used, documenting its entire journey, anticipating bottlenecks, reducing the risk of loss or theft, and extending its life cycle.
In the logistics sector, for example, AIoT systems can monitor the condition of perishable cargo in transit, predict delivery times, optimise routes and automatically alert to delays or temperature variations that could compromise quality.
Robotics and automation
Robotics and automation are undergoing a decisive transformation with the fusion of AI and IoT. Whereas robots were previously limited to rigid behaviours and structured environments, today they are capable of perceiving, deciding and interacting with people and machines in dynamic scenarios.
In traditional automation, robots were limited to assembly lines, repeating tasks mechanically and requiring frequent manual reprogramming. Now, with integrated IoT sensors and advanced algorithms, robots can operate in complex and unstructured environments.
A robotic arm in a factory, for example, can recognise different components using computer vision, adjust its gripping force in real time based on tactile feedback, and work safely alongside humans using proximity and motion sensors.
AMI
AMI (Advanced Metering Infrastructure) is the backbone of digitalisation in the utilities sector. It is an ecosystem that connects smart meters, secure communication networks and management platforms, enabling real-time measurement, monitoring and control of water, energy or gas consumption.
More than just automatic readings, AMI creates a continuous flow of data between the field and the operations centre. Smart meters record consumption and technical parameters, connectivity ensures the reliable transmission of this information, and management systems analyse the data to identify fraud, forecast demand and guide strategic decisions.
Over time, AMI evolves from a metering infrastructure to an intelligent management platform, integrating into AI models that enable fault prediction, dynamic load balancing and even energy efficiency strategies on an urban scale. It is the basis for transforming traditional utilities into digital operators of essential resources.
Digital twins
Digital twins are virtual representations of physical assets, systems, or processes that are continuously updated based on data collected by IoT sensors. These digital models evolve in sync with the real world, learning from historical data and AI analytics to simulate scenarios, predict failures, and optimise performance throughout the entire lifecycle.
In a factory, for example, the digital twin of a production line can display energy consumption, product flow and machine conditions in real time, allowing immediate adjustments to be made without interrupting operations. Artificial intelligence extends this capability by detecting anomalies, predicting bottlenecks and even recommending or executing automatic actions to maintain efficiency.
In practice, digital twins represent the physical world in a digital environment, offering total visibility and coordination capabilities in complex systems such as energy, sanitation, transport and infrastructure. In this way, they cease to be mere descriptive models and become living systems, capable of guiding strategic decisions and enabling continuous improvements on a large scale.
The future of AIoT
I believe that the next step in the development of AIoT systems will no longer be just a question of device volume, but an increasingly intelligent use of the technologies that enable these possibilities and those yet to come.
6G, quantum computing and on-device learning, for example, are concepts that will undoubtedly bring about revolutions that we cannot yet imagine.
All these possibilities must be approached with responsibility and determination, in order to create value for society as a whole, without exception.
Artificial Intelligence of Things is no longer just a trend; the future lies in what we will be able to create with it.
