Long focused on data collection, the IoT is evolving under the impetus of AI towards ambient intelligence capable of understanding, anticipating and optimizing operations in real time.
A major evolution is underway in many sectors: the Internet of Things (IoT) traditional is transforming, driven by field data and data capture solutions that create new forms of intelligence thanks to artificial intelligence (AI).
Some are now talking about AI-based IoT. However, a more accurate expression would be that of ambient intelligence. If IoT based on AI designates the means, ambient intelligence represents the end: a concrete value, directly exploitable by field teams in their daily lives.
But what does ambient intelligence actually mean? In short, it’s about digitizing physical environments, workflows, equipment and inventory, then transforming this data into key insights, trends, predictions, alerts and actionable recommendations for the field and operations.
Ambient intelligence brings together the physical layer, the data layer, an AI-driven analysis layer, as well as an execution layer associated with an appropriate level of automation. And above all, it operates in real time, like a living system reflecting precisely what is happening at a given moment.
Thanks to multi-sensor layers and AI, it becomes possible to create an adaptive and real-time representation of operations, making physical spaces and field teams sensitive to their context. This comprehensive approach helps connect the field, understand the entire environment, ensure visibility of every piece of equipment, and support intelligent automation in many industrial environments.
The essential link: distributed intelligence at the edge
A key driver of ambient intelligence is sensing technologies, software, real-time edge processing, and AI.
Physical environments feed AI models with varied data. This relies on the integration of multiple detection technologies to build a dynamic understanding of environments and workflows, orchestrated by modern software platforms.
This notably includes RFID, where advanced real-time location solutions use fixed and flexible systems to provide extremely precise location. These technologies can also integrate computer vision, AI-driven voice commands, endpoint usage data, geolocation, 2D and 3D machine vision, and barcodes for fundamental identification data.
If the multiplication of sensors generates exponential growth in data (images, audio, location or performance), multimodal AI models precisely exploit this wealth of information to understand environments and automate workflows thanks to multimodal intelligence.
Processing this data at the edge brings the physical world, data and execution closer together, enabling latency-free workflows, essential in high-speed industrial environments or in areas where connectivity remains intermittent.
Modern mobile devices and machine vision equipment incorporating neural processing units enable data to be processed locally to accelerate inference, reduce cloud costs, and increase security and privacy. Field employees equipped with these terminals receive usable information directly on the screen, or via a headset, exactly when they need it.
Towards augmented and collective intelligence (IAC)
If IoT is evolving through AI towards a form of ambient intelligence, AI itself also continues to evolve. In addition to computer vision, deep learning and machine vision, AI agents (or digital workers) capable of operating in intelligent environments are now being added.
This combination between human collaborators and agents can be described as a form of augmented and collective intelligence (IAC), where ambient intelligence constitutes the information base on which the agents operate. This approach strengthens field capabilities and paves the way for autonomous operations capable of simultaneously improving productivity and profitability.
IAC can be understood as a distributed network of terminals or a swarm of connected and specialized agents, rather than a single omniscient model. It also combines several approaches to AI (generative and algorithmic) in order to respond to complex problems. Finally, it is based on human augmentation: employees bring their business expertise and their understanding of the field, while AI amplifies these capabilities through decision support.
From a technical point of view, ambient intelligence consists of transforming raw data from multiple sensors into an operational context that can be used in real time, thanks to the combination of adapted connectivity, multimodal sensors and AI.
On an industrial scale, it supports a vision where field operations become digitalized, automated and intelligent. And on a human scale, it aims to build environments where field employees have the visibility and anticipation necessary to improve daily work.
