AI and IoT: Why Artificial Intelligence Needs the Internet of Things to Have Real Impact

https://www.cloud.studio/wp-content/uploads/2026/03/Cloud-Studio-IoT-Cadena-Ser.mp3

Artificial intelligence without AI and IoT combined is like a brain without a nervous system: intelligent, but blind. It can imagine. It can assume. It can… hallucinate. But it cannot feel what is happening in the real world.

This is not a casual metaphor. It is the reality facing thousands of companies that have invested millions in AI projects that ultimately fail. The reason? They are feeding sophisticated algorithms with static data, outdated PDF reports, and Excel spreadsheets that reflect the past, not the present.

When Joaquín Cervera, CEO of Cloud Studio IoTITermIoT (Internet of Things)The IoT (Internet of Things) is the network of physical objects with sensors, software and connectivity that collect and exchange data and act autonomously.View profile, was invited to Hoy Por Hoy Madrid leaded by Marta González Novo, from Cadena SER (Spain’s leading radio network) to discuss the future of technology, he brought a clear message: “Without IoT infrastructure behind it, AI is an expensive guessing machine”. Because without real-time data, artificial intelligence is making decisions blindly. And in the business world, that is not intelligence. It is unnecessary risk.

What Exactly Is the Internet of Things and Why It Matters for AI

The concept sounds abstract until we bring it to concrete ground. AI and IoT work together when devices and sensors capture data from the physical environment and send it to the cloud, where servers centralize all that information and transform it into actionable knowledge.

Joaquín Cervera explained it with precision during the interview: “We are talking about devices, sensors that take data from our environment and send it to a cloud, to a server, and that server centralizes all this, all this data, and transforms it into information that then allows, through different tools, to make data-based decisions”. This continuous flow of real-time information from the physical world creates the foundation for meaningful artificial intelligence applications that can understand and respond to actual conditions rather than theoretical scenarios.

The magic happens in that bridge between the physical and digital worlds. From smart streetlights that adjust their intensity based on pedestrian traffic, to waste containers that report their fill level to optimize collection routes. Air quality, noise levels in cities, soil moisture in agricultural fields, building energy consumption — everything becomes real-time data.

For modern smart cities, this convergence is fundamental. A smart citySTermSmart cityA smart city uses IoT sensors and data to manage urban infrastructure more efficiently and sustainably: traffic, lighting, waste and water.View profile is not built only with algorithms in the cloud: it needs thousands of sensors distributed across its streets, buildings, and infrastructures continuously capturing information about traffic, pollution, energy consumption, and security. These sensors provide the raw material that transforms abstract predictions into actionable urban management decisions, enabling real-time responses to changing conditions like traffic congestion, air quality spikes, or energy demand fluctuations.

But here is the critical point: this data is useless without intelligence to process it. And conversely, artificial intelligence is useless without fresh, real, and continuous data to analyze. It is a mandatory symbiosis that defines the success of any industrial digital transformation project.

The Brain and Nervous System Analogy: Why AI Needs IoT

During the conversation on Cadena SER, Cervera used an analogy that summarizes decades of technological complexity into an instantly comprehensible image: AI is the brain, IoT is the nervous system. This biological comparison effectively communicates the core challenge and opportunity facing organizations today: the disconnect between analytical capabilities and operational reality.

The biological nervous system exists for one essential function: to carry information from the body to the brain. Peripheral nerves capture stimuli — temperature, pressure, pain, position — and transmit them to the central system for processing. Without that constant flow of sensory information, the brightest brain in the world would be isolated, unable to interact with reality.

In the digital world, exactly the same thing happens. IoT sensors are the peripheral nerves that capture what is happening in the physical world. IoT transports that data through networks, creating the continuous information flow that artificial intelligence requires to function effectively. And AI can analyze it, identify patterns, predict behaviors, and recommend actions.

This symbiotic relationship explains why so many artificial intelligence projects in industrial companies fail: they are trying to build a brain without connecting it to the body it must govern. Without the constant flow of data provided by a well-deployed sensor infrastructure, even the most advanced AI models end up generating theoretical insights without practical application. The disconnect between analytical potential and operational capability represents one of the biggest challenges facing organizations today in their digital transformation journeys.

The key is to understand that AI and IoT are not independent technologies that can be adopted separately. They are components of the same digital nervous system that, working together, allows organizations to perceive, process, and react to their operational environment with speed and precision impossible to achieve with traditional methods.

Real AI and IoT Cases: How They Transform Entire Industries

Theory is convincing, but practical cases are what demonstrate the real value of combining artificial intelligence and internet of things. During the Cadena SER interview, Cervera shared several concrete projects where this integration is generating measurable impact in sectors as diverse as education, industrial safety, and agricultureAIndustryAgricultureView profile.

Climate Monitoring in 120 Schools in the Canary Islands

One of the projects Cloud Studio IoT is deeply proud of is the deployment of 240 air quality sensors in 120 schools distributed across the 7 Canary Islands, completed in just 18 days. A complex logistics operation that required coordinating ferries, technical teams, and precise installation schedules across multiple islands. This achievement demonstrates our operational capabilities in geographically challenging environments and our commitment to educational quality improvements through technology deployment.

The objective: to mitigate the impact of haze (calima) coming from the Sahara and understand how it affects educational centers. But the next step, as Cervera explained, is to take that data to an artificial intelligence engine to determine in advance what is happening and which schools need priority intervention.

This project, documented in our climate monitoring case study in schools, demonstrates how the combination of distributed sensors and intelligent analysis can protect the health of thousands of students. The ability to predict haze episodes before they occur allows educational authorities to make proactive decisions about ventilation, outdoor activities, and protection protocols.

Early Fire Detection with Thermography

In the United States, Cloud Studio IoT co-created an early fire detection solution using thermal cameras. These cameras report anomalous temperatures in real-time, allowing the identification of high probabilities of fire before it occurs.

In summers where news of forest fires is devastating, this combination of technologies is not just operational efficiency. It is prevention of irreversible damage, protection of lives, and conservation of ecosystems. AI algorithms analyze historical and real-time thermal patterns to distinguish between normal conditions and risk situations, generating automatic alerts when anomalies are detected.

Irrigation Optimization in Precision Agriculture

In the agricultural sector, soil and leaf moisture sensors allow determining exactly how much water each zone of a field needs at any given moment. As Cervera explained: “Today basically you irrigate, you open the tap, you irrigate everything and then you cut, and suddenly we don’t know where we are applying water at the correct levels and where we are not”.

Water is a critical resource, especially in Spain where scarcity is a growing reality. The combination of IoT sensors with AI algorithms allows drastically optimizing consumption, applying resources only where and when they are needed. Precision agriculture solutions that integrate both technologies are demonstrating savings of up to 30% in water consumption while maintaining or improving crop yields.

The Triple Impact of AI and IoT on Business Transformation

When AI and IoT are implemented correctly, they do not generate just operational efficiency. Cervera highlighted in the interview the triple impact these technologies produce: social, economic, and environmental. This triple perspective is what differentiates tactical implementations from transformative strategies.

The social impact manifests in the transformation of daily work. The operator who previously had to physically travel to an oil well or to check if a streetlight was working can now monitor everything from a control center. They only approach the site when the system has detected a failure, and they do so knowing exactly what spare part to bring, what component to replace, and how long the repair will take.

This change is not just convenience: it is a fundamental improvement in quality of work life and worker safety. Inspection tasks in hazardous environments can be performed remotely. Interventions become planned operations instead of unpredictable emergencies.

The economic impact is direct and measurable: reduction of operational costs, optimization of resources, prevention of costly failures. In projects like the gas consumption measurement system in Mexico — with 80,000 meters monitored in real-time — companies bill without delays and eliminate manual operator rounds. The ROI of these implementations typically recovers in less than 18 months.

The environmental impact is perhaps the most significant in the long term. Optimization of water consumption in agriculture, reduction of emissions through smart lighting, prevention of forest fires, efficient urban waste management. These integrated technologies are essential tools for business sustainability and compliance with increasingly demanding ESG objectives.

Security and Privacy: Protecting Data from the Physical World

An inevitable question arises when we talk about thousands of sensors continuously collecting data: how is all that information protected? The Cadena SER host raised exactly this concern about privacy, control, and security in an increasingly connected world.

Cervera explained that there are different levels of protection in a well-designed AI and IoT architecture: from the encryption of the sensor itself at the moment of capture, through the encryption of communications during transmission, to the security applied on the servers where data is centralized.

Different protocols use different levels of encryption, and the choice depends on the criticality of the application. An air quality monitoring system in schools has different requirements than critical energy or water infrastructure. Security in IoT is not an afterthought: it is a fundamental part of design from the first sensor.

Best practices include device authentication through certificates, end-to-end encryption of communications, network segmentation to isolate IoT devices from critical corporate systems, and regular security audits. Data privacy is also critical: collected information must be anonymized when possible and stored complying with regulations like the General Data Protection Regulation (GDPR).

According to a report by the European Union Agency for Cybersecurity (ENISA), IoT devices represent one of the fastest-growing attack vectors, making it essential to adopt robust security standards from the design phase. The implementation of protocols like MQTT with TLS and the use of hardware with Secure Elements are practices recommended by international bodies such as NIST to protect critical infrastructure.

Madrid as an Innovation Hub: The Ecosystem Making Change Possible

During the interview, the conversation turned to the innovation ecosystem in Madrid. Cervera did not hesitate in his assessment: “For me today they are leaders, basically they are leaders. They are setting trends, there is so much innovation here, and the truth is that they are being protagonists”.

Cloud Studio IoT is part of Madrid Innovation, the Madrid City Council’s brand to promote innovation in the city, and is located at Puerta Innovación, in Puerta de Toledo. This belonging to an ecosystem of startups, researchers, and technology companies is not accidental: it was a deliberate search.

“We went looking for them. We saw the reach they had and the tools they provided us, and the truth is that they ended up far exceeding our expectations”, Cervera explained about how they came to be part of this initiative.

This ecosystem is the breeding ground where ideas like the integration of these advanced technologies can mature, be tested, and scale effectively. When a startup with nine years of experience building IoT infrastructure receives the showcase of an interview on Cadena SER, it is a sign that the sector is reaching the maturity necessary for massive impact.

Madrid is positioning itself as a European reference in applied technology, with public initiatives that support innovative companies and a business fabric receptive to adopting new technologies. According to the European Innovation Scoreboard 2024, Spain has significantly improved its innovation position, particularly in technology adoption by SMEs and digital transformation acceleration.

For companies seeking technology partners, this ecosystem offers access to validated providers, specialized talent, and demonstrable success cases. The collaboration between technology startups, research centers, and public administrations is accelerating knowledge transfer from laboratory to market.

The Future: Where AI and IoT Integration Is Heading

The interview on Cadena SER was not just a review of past achievements. Cervera made clear where this technology is evolving: towards anticipation and proactive prediction.

Once you have historical data from sensors distributed in the field, the next logical step is to apply artificial intelligence models to predict what will happen. Not just detect that a school has poor air quality now, but predict which schools will have it tomorrow based on weather patterns. Not just report that a motor is heating up, but predict when it will fail to intervene before it happens.

This is the true promise of AI and IoT: the step from reaction to prediction. From responding to problems to preventing them. From optimizing existing processes to redesigning how entire industries function. Predictive maintenancePUse casePredictive maintenanceView profile, anticipatory resource management, proactive infrastructure planning — all this is possible when you combine sensors that capture the present with algorithms that learn from the past to anticipate the future.

Companies that understand this transition — that AI needs IoT as its sensory nervous system — will have sustainable competitive advantage. Those that continue investing in “brains” disconnected from the “body” of their operations will continue depending on expensive machines that guess instead of intelligent systems that know.

Meet the Cloud Studio IoT AI Copilot — AIoT, In Production

This is no longer theory for us. We are shipping the AI Copilot, the agentic and conversational layer that turns Cloud Studio IoT's Gear platform into an AIoT system you can talk to. Currently in beta with a curated group of customers, it does four things from day one:

• Conversational query over your telemetry: ask anything in plain English (or Spanish) and the Copilot fetches the right data, scoped to your permissions and tenant. No SQL, no PromQL.
• Generative dashboards and SCADA views: describe what you need ("compare energy KPIs across the East region") and the Copilot opens a pre-filled draft.
• Agentic actions on devices: with the appropriate permission, the Copilot dispatches commands, acknowledges alerts, or triggers automations — every action audit-trailed and bounded by a per-device-type allow-list.
• Alert investigation in natural language: pick any alert and ask "why did this fire?". The Copilot summarises the conditions, the telemetry trail, and the most likely root causes, citing the endpoints involved.

Full capabilities, prompt examples, permissions, and roadmap (voice mode, multi-step workflows): see the AI Copilot documentation. If you operate an IoT fleet and want to see the Copilot on your own data, request a demo.

Conclusion: The Symbiosis That Defines the Business Future

Cloud Studio IoT’s participation in Cadena SER left a clear message: these technologies are not separate components that companies can adopt at their convenience. They are components of the same integrated system that, separated, lose most of their value and effectiveness.

Artificial intelligence without data from the physical world is potential without execution. IoT without intelligence to process its data is information without action. Together, they form a virtuous cycle where sensors feed algorithms, and algorithms generate decisions that improve the physical world that sensors capture.

For CTOs, technical directors, and system integrators evaluating digital transformation projects, the fundamental question should not be “do we invest in AI or IoT?”. The correct question is: “how do we connect the digital brain with the physical nervous system of our operation?”.

The smart manufacturing solutions we integrate at Cloud Studio IoT demonstrate that this convergence is not futurism: it is present. From LoRaWAN connectivity for IoT to real-time analysis platforms, the tools exist. What is needed is strategic vision to implement them coherently.

Because at the end of the day, as Cervera said closing the interview: “What it’s about is improving the world”. And that is only possible when artificial intelligence can truly feel what is happening in the real world.

Keep reading

What Is Industrial AI? A Practical Guide for 2026 Plants · Industrial AI Prompts: 12 Working Examples for Plant Engineers and Integrators · industrial-iot-solutions-use-cases · mantenimiento-predictivo-iot-ia · industrial-ai-software · what-is-an-llm-in-industrial-iot · rag-in-industrial-iot · agentic-ai-for-industrial-operations · ai-copilot-vs-scada-hmi · ai-copilot-for-iot-platforms-buyers-guide