World industry is undergoing the largest technological shift since electrification. Digital technologies have ceased to be merely a tool for targeted optimisation and have become the foundation on which modern manufacturing is built. According to estimates by the analytical company MarketsandMarkets, the industrial artificial intelligence market will grow from $34 billion in 2025 to $155 billion by 2030, demonstrating a compound annual growth rate (CAGR) of 35%. The digital twin market is developing even faster: from $21 billion to $150 billion over the same period (a CAGR of almost 48%). As NVIDIA CEO Jensen Huang noted, “digital twins are turning from passive simulations into the active intelligence of the physical world.”
Digital Twins: From Simulation to Managing Reality
A digital twin is a virtual copy of a physical object or process: a factory, a conveyor line or an entire oilfield. Such a model is updated in real time using data from sensors, making it possible to simulate scenarios, predict equipment wear and make management decisions without halting production.
In 2026, Siemens and NVIDIA presented the Digital Twin Composer platform and the Industrial AI Operating System — a unified environment for creating digital twins at every stage of a product's life cycle: from design to logistics.
A striking example of scaling this technology is BMW's Virtual Factory project. It is a digital copy of the entire production network, combining data from 30 plants around the world into a single model. This approach makes it possible to test the operation of the global supply and assembly chain virtually. Thanks to this, the time required to verify new production scenarios has been reduced from four weeks to three days, which allows plants to reconfigure faster for new models and market demands.
Computer Vision: Factories Learn to “See” Better Than Humans
Artificial intelligence and computer vision are transforming quality control from a routine procedure into an intelligent system. Modern AI agents at factories achieve defect detection accuracy above 99%, whereas for a human operator this figure is around 80%. According to estimates by AgentMarketCap, the market for such solutions will grow from $6,4 billion in 2026 to $12 billion by 2032.
For example, Toyota has implemented the Invisible AI computer vision platform at its plants in North America. The system monitors the sequence of operations on assembly lines, records the slightest deviations and warns of possible errors before they lead to defects. According to the company, this has made it possible to reduce the number of defects in the early stages of assembly by tens of percent.
Unmanned Machinery and Autonomous Systems
Autonomous machinery has ceased to be an exotic novelty for the mining industry and has become its standard. In Australia's Pilbara region, where the largest iron ore deposits are concentrated, more than 1 thousand autonomous dump trucks operate in total. This is the world's largest testing ground for unmanned quarry machinery.
Rio Tinto is integrating digital twins of its mines with this fleet. By 2025, these machines had transported 5 billion tonnes of rock around the clock, without drivers and without incidents related to staff fatigue. Moreover, Rio Tinto has built the world's first fully autonomous long-distance railway, 1,7 thousand km in length. Ore trains travel from the quarries to the ports without drivers, controlled by a centralised system.
At the same time, maintenance logistics is changing thanks to 3D printing. Instead of waiting months for a part (while it is manufactured and delivered across the ocean), it can be printed right at the production site in a couple of days. This is critically important for remote industrial facilities, where equipment downtime is extremely costly.
The Energy Challenge for Artificial Intelligence
The development of AI requires colossal computing power, and therefore an enormous amount of electricity. Data centres for AI have already entered the top five largest energy consumers in the world. According to the International Energy Agency (IEA), in 2026 the electricity consumption of global data centres will exceed 1 thousand TWh, comparable to the energy consumption of all of Japan.
The shortage and high cost of energy are spurring the search for unconventional solutions. The American company Crusoe Energy builds modular data centres right at oil fields, using associated gas that was previously flared. Instead of being emitted into the atmosphere, the gas is converted into electricity for servers. This approach reduces the carbon footprint and provides cheap energy for AI computations, attracting hundreds of millions of dollars in investment from major funds and cloud providers.
Similar environmentally friendly initiatives are being implemented around the world: in Norway, data centres use surplus energy from hydroelectric plants, in Iceland — geothermal energy, and in the UAE and Saudi Arabia large solar farms are being built to power computing clusters. The idea is to move computations to where energy is cheaper and more accessible.
Conclusion
The digital transformation of industry has moved from the experimental stage to the stage of a global industrial standard. Companies that successfully integrate digital twins, computer vision and autonomous systems gain a strategic advantage: they adapt faster to market changes, reduce operating costs and minimise the impact of the human factor. The future of global manufacturing has already arrived, and its foundation is a seamless symbiosis of the physical world and advanced digital technologies.
