Long associated with fridges that text you when you run out of milk and watches that count the number of steps you take, the Internet of Things has changed almost beyond recognition since the term was popularised a decade ago.
Describing networks of connected devices that collect, exchange, and act on data, often without human intervention, IoT has grown from a single internet-connected toaster, demonstrated at the Interop conference in 1990, to today’s billions of digital nervous systems linking the physical and virtual worlds.
For one thing, the sheer number of smart devices has grown astronomically. According to IoT Analytics, the number of connected IoT devices increased from 13.4 billion in 2015 to nearly 20 billion in 2025, and is expected to double again to almost 40 billion by 2030.
Moreover, the range of ‘things’ being connected has expanded dramatically. No longer limited to consumer gadgets or simple sensors, IoT devices in 2026 include autonomous vehicles, humanoid robots, industrial machinery, and even entire infrastructures, from energy grids and transport networks to global supply chains.
Thirdly, and perhaps most significantly, as AI is integrated into IoT networks, they are becoming more intelligent, able to see, sense, learn, and respond in real time.
“AI is gaining eyes, ears, hands, and legs, i.e. the ability to perceive the physical world, reason with it, and take action,” says Kenta Yasukawa, CTO of Soracom. “And IoT connectivity is becoming the spine and nervous system that ties it all together: seamless, global, always-on. This is not about AI hype or IoT hype converging. It is about a technological capability finally emerging that can deliver on promises both fields have been making for a decade.”
Autonomous vehicles, industrial robots, and other IoT systems can now learn collectively, continuously refining AI models in near real time. “By using camera devices as the eyes and ears of AI, a great variety of use cases that were previously impossible will become possible,” Yasukawa adds.
The result is the emergence of the Artificial Intelligence of Things, or AIoT, in which systems identify patterns and trends, deliver actionable insights, make predictions, and then automate actions.
“As AI develops and becomes increasingly sophisticated, a significant trend is the development and improvement of predictive AI, which can now forecast outcomes with high accuracy,” says Nik Kairinos, CEO of Fountech AI. “Predictive AI can now use data to map potential outcomes and assign a likelihood score to each.”
“This development has the potential to transform how organisations across multiple sectors operate, shifting decision-making from reactive to proactive. In industrial settings, combining predictive AI with IoT has a wide range of applications. It can make operations safer by analysing data from IoT sensors, identifying signals that something may be about to go wrong, such as a faulty manufacturing process or an emerging safety risk, and even initiating action to prevent it.
“It can improve efficiency by identifying new approaches that save resources or accelerate processes. It can uncover patterns and relationships that were not previously anticipated and identify optimal outcomes before new approaches are even tested. And, of course, it can enhance decision-making by providing richer, more reliable information, propelling innovation with greater insight and confidence.”
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