Reflecting on the findings of Capgemini’s recent report, Dr Diane Berry, Capgemini’s Engineering Science Leader, explains the changes needed to enable the UK to fully and confidently deploy physical AI.
Some technologies explode onto the market, while others advance steadily as organizations, regulations, and operating models catch up. Physical artificial intelligence (AI), at least in the UK, is firmly in the latter category. Robotics has a long history of high expectations, so it’s no surprise that many organizations are cautious about adoption.
But while adoption may be measured, potential impact never is. By combining AI, robotics, and advanced sensing, physical AI will transform the way industries operate, enabling machines to perceive, reason, and act autonomously in the physical world. From critical infrastructure and manufacturing to energy and logistics, it has the potential to reimagine productivity, resilience, and safety at scale.
Research suggests that UK executives are ready to look beyond the hype and recognize its strategic importance. According to a recent report from Capgemini, nearly two-thirds (67%) say physical AI will be a key driver of industry competitiveness.
However, this recognition has not yet led to full-scale efforts. 65% of UK executives now rate physical AI as a high priority for the next three to five years, but the path to scale remains cautious. The UK’s approach is cautious and gradual, especially compared to fast-moving markets such as Japan, South Korea, China and the US, where physical AI is already embedded as a core part of their industrial strategy.
In contrast, the UK is progressing through structured exploration and early deployment, with a focus on building trust, confidence and long-term viability rather than acceleration.
Building a foundation for scaling up
UK organizations balance opportunity and responsibility, particularly in safety-critical and regulated environments. Physical AI is not a plug-and-play technology. It requires tight integration across data, operations, infrastructure, and workforce design. This reality naturally favors a more gradual adoption curve.
Traditional operating models continue to shape the pace and nature of change. Historically, large-scale transformation in the UK has often prioritized stability, efficiency and risk mitigation over rapid experimentation and iteration. While these models have been effective in the past, they can make it difficult to build the practical operational capabilities needed to scale emerging technologies like physical AI at pace.
Without strong internal understanding and collaborative, long-term partnerships focused on building capabilities, scaling becomes more difficult. Physical AI requires not only technology but also skills, governance, and continuity of operational ownership.
The need for long-term efforts
There are also broader behavioral challenges when it comes to investing. Physical AI requires a sustained, multi-year effort. However, UK organizations are often more cautious with investors pausing or withdrawing funding as soon as short-term pressures arise.
This stop-start pattern is fundamentally inconsistent with technology maturing through iteration, learning, and cumulative capability building. A 1-3 year planning cycle will not allow you to realize the full value of physical AI. What is needed instead is a shift to long-term capacity investing, based on a clear vision, stable funding, and an acceptance that ROI will evolve over time rather than immediately.
Where physical AI comes first
It’s a great opportunity for those who are ready to stay the course. Unlike agent AI, which is introduced into organizations through high-volume, low-risk digital tasks, physical AI is often deployed first in the highest-risk locations. This is ideal for high-risk, hazardous or safety-critical environments, such as “non-human-suitable” tasks where continuity, accuracy, and risk mitigation are paramount. At nuclear power plants, for example, robots are already able to perform tasks that are too dangerous for humans, such as decommissioning and remote control in high-radiation areas.
In sectors such as energy, infrastructure, manufacturing, and logistics, physical AI is already increasing resiliency, reducing downtime, enhancing safety, and providing a stable 24/7 operational backbone for critical infrastructure during times of labor shortages.
From exploration to action
Encouragingly, the foundations are being laid. The UK organizations featured in our report are actively working on physical AI, moving from exploration to piloting and early adoption. Almost half (47%) expect humanoid robots to work alongside human employees by 2030, demonstrating growing confidence in human-robot collaboration, albeit a longer timeline than some global peers.
This is not about rushing towards humanoids or replacing them. It’s about convergence. Robotics, automation, and advanced sensing are not new. What is changing is its intelligence. Physical AI allows these systems to learn, adapt, and operate with far greater autonomy, transforming robots from tools to collaborators.
If the confidence expressed by British executives is any indication, there is intent. The next step is to translate that intention into consistent action. When that change occurs, opportunities move decisively beyond cautious exploration into resilient, trusted, and globally competitive forms of scale.
This article will also be published in the quarterly magazine issue 26.
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