Embodied AI

Both, in different places. Industrial robotics is established at scale, and humanoids are beginning to enter bounded production work. BMW reports that Figure 02 supported production associated with more than 30,000 X3 vehicles before the next phase moved into logistics. The long-range market projections remain projections. My read is that the direction of travel is clearer than the timing, which is a case for early, bounded learning rather than a large commitment made on the strength of a forecast.

A map, not a strategy. Where machines already act physically in the operation and its supply chain, which of them carry AI, who can intervene and what happens on failure, what the insurance and liability position is ahead of the EU’s December 2026 product liability regime, and one or two bounded pilots that generate learning rather than headlines. If management cannot describe the organisation’s physical AI surface, that is the finding.

Resist filing it under technology. The exposure is operational and legal before it is technical, so the natural home is the executive who owns physical operations, with the risk or audit committee sighted on liability, insurance, and safety, and the full Board owning any material capital commitment. Where a Chief AI Officer exists, they coordinate; they do not absorb the accountability.

Exposure tracks how much of the organisation’s value chain depends on physical delivery. Manufacturing, logistics, construction, healthcare, and energy sit closest to the change. One horizon is statutory: the EU’s revised product-liability regime applies to relevant products placed on the market or put into service from 9 December 2026. The second is operational. Pilot economics should be refreshed as capability, cost, insurance terms, and the availability of suitable systems change, rather than inherited from an assessment made several years earlier.

In my view the larger market is domestic, and it is the one the projections struggle to price. The World Health Organization projects the global over-60 population doubling to 2.1 billion by 2050, with the over-80s tripling to 426 million, against care workforces that already cannot meet demand. A machine that helps someone stay in their own home, safely and with dignity, answers a need that workforce planning alone will not close. The factory case is about cost; the care case is about demand with nowhere else to go, and demand of that kind tends to find its technology.

They are one of the more mature forms of embodied AI, although conventional industrial robotics is further established operationally. Wayve and Uber have announced plans for Level 4 public-road trials in London under the Department for Transport’s accelerated pilot framework, subject to government and Transport for London approval. The UK intends to complete implementation of the Automated Vehicles Act in the second half of 2027, establishing the wider authorisation and liability regime. Many Boards may encounter this first through fleets and logistics, but the application I would also watch is independence: self-driving vehicles could restore mobility to people who can no longer drive.

It moves AI into the delivering dimension of work, the physical work the first wave barely touched, which I wrote about in The Great Remaking. BMW describes its current humanoid deployments as augmentation of physically demanding and repetitive tasks. That is a documented example, not yet a labour-market pattern. The workforce questions nevertheless arrive early: consultation, reskilling, safety representation, and the redesign of operating procedures. They belong on the agenda before a machine reaches the site, not after.

China accounted for 54% of global industrial-robot installations in 2024, according to the IFR’s World Robotics 2025 report. That is evidence of concentration in deployment and manufacturing, not a complete map of the global robotics supply chain. For an organisation planning a physical AI capability, the Board question is where the machines, critical components, software, and models come from, what alternatives exist, and how support and data flows would be affected by trade restrictions or geopolitical disruption. The answer will differ by sector, but the dependency belongs in the capital discussion from the start.