Plugging the uncertainty gap in physical AI

My colleagues have already explored the technical pillars underpinning physical AI, from whole body control to human–robot interaction, and how these capabilities can work in embodied and humanoid robots and other intelligent robotic systems. These capabilities are essential to making physical AI work, but they are only part of the picture. Technical progress alone doesn’t answer the questions organisations are now asking: Can we trust physical AI? How do we deploy it responsibly? And how do we manage risk without blocking innovation?

Many organisations are keen to add AI to their pipelines, but hesitate when it comes to deployment. In their minds, they cannot yet be sure if new technology like physical AI is safe, secure or viable because there is little historic data or real-world evidence to lean on – yet.

This is what we call the ‘uncertainty gap’: the space between the pace of innovation and trust in the system. This uncertainty is where concerns tend to arise, and where hypothetical worst-case scenarios sit alongside more probable, low-impact risks. Without clear data, people imagine extremes: catastrophic failures, loss of control or systems behaving unpredictably.

But this lack of real-world evidence does not make the technology unethical or unsafe by default – it simply means that traditional approaches to validation and trust are harder to apply. Physical AI introduces new risks, not unknowable ones. As with any new technology, it’s impossible to fully eliminate this uncertainty, but we can help you prepare and engineer for it – and so mitigate risk.

This is the essence of our AI assurance policy. By reasoning forward, we anticipate what could go wrong, what is theoretically possible and what is realistically likely, identifying risks to understand both their likelihood and impact, and ultimately decide explicitly what level of uncertainty is acceptable for the use case. This lays the groundwork for organisations to responsibly move towards deployment, even as the technology continues to evolve.

How AI assurance leads to a first mover advantage

Some may still feel it is safer to wait and see how others fare with new technology before exploring it themselves. This cautious approach reduces immediate risk, but it introduces risk of a different kind: falling behind.

When it comes to deep tech like physical AI, it is the bold first movers who see the greatest gains. They set the pace of the market with cutting edge technology and unique services while others scramble to catch up. These industry leaders recognise the operational benefits, strategic advantage and long-term value of embracing bold new technology – and have the vision to see that the potential reward outweighs the risk.

For those who want to seize that first mover advantage with physical AI, building robust and responsible AI assurance from the outset is essential. Being first without an AI risk management framework is risky – ethically, reputationally and commercially. But being first with AI assurance in place allows organisations to confidently demonstrate responsibility, build trust and contribute evidence that helps shape emerging standards and regulation.

This is of increasing importance as AI regulation begins to gain momentum. While AI law has historically lagged behind technology, recent events show that regulation can become reactive when public concern grows. Organisations that have already adopted a risk-based, ethical approach from the start of the project are far less likely to be disrupted by sudden regulatory change and have a clearer path to long-term success.

What does AI assurance look like in practice?

Physical AI assurance is not a single test, framework or checklist. It is a process of building an evidence base drawing on both quantitative and qualitative inputs.

On the quantitative side, this includes sim2real performance data, lab testing, benchmarks and operational metrics. These show what the system can do, how reliably it does it and where its limitations lie.

But data alone is not enough. Assurance also relies heavily on qualitative evidence such as expert judgement and feedback from people who work with or around the system. Context is everything: how an intelligent robot behaves and what risks it introduces depends entirely on where it is used, who it interacts with and what it is designed to do. An intelligent robot using fine manipulation capabilities in a controlled warehouse environment requires a very different assurance approach from a humanoid robot operating alongside the public. Physical AI is inherently socio-technical – the risks do not sit purely in the model or the hardware; they emerge through interaction with people, processes and environments.

This is why AI assurance always starts with context of use. We need to understand:

• where the system will operate
• who will interact with it
• how it will be used
• and what outcomes are acceptable or unacceptable

In some cases, that context is tightly defined. In others, organisations deliberately want flexibility, allowing systems to be tested, adapted and extended into new scenarios. This requires a clear understanding of risk tolerance – if you want a system to evolve organically, you must be comfortable with a higher level of uncertainty and have appropriate guardrails in place. Defining those risks and guardrails is a key part of where we can help your deployment strategy as we develop physical AI assurance for your specific use case.

This means looking across multiple risk domains at once, including technical risk, data risk, legal and regulatory risk, ethical risk, human risk and business risk, alongside cross-cutting issues such as security. The value lies in understanding how these risks interact, rather than treating them in isolation.

It is also important here to be clear about the distinction between assurance and safety. Safety is a formal discipline governed by specific laws, standards and testing regimes. Assurance does not replace safety assessment. Instead, it identifies where safety considerations apply, ensures they are addressed appropriately and places them within a broader understanding of risk.

In short, AI assurance turns uncertainty into something that can be actively managed rather than simply avoided. It gives leaders a clear view of what a physical AI system can do, where its limits lie and what risks are being taken – consciously and deliberately – in a given context and use case. With this understanding in place, the question shifts from whether physical AI can be trusted to how and when it should be deployed.

Turning uncertainty into informed innovation

Physical AI does not need blind optimism or blanket caution. It needs structured thinking, honest conversations about risk and AI assurance that is grounded in how these systems actually behave in the real world. Here, CC can be your guide.

It’s clear to us that physical AI is entering a new phase – one that moves beyond the lab and hype and into real-world deployment. With this shift comes uncertainty, but those who harness AI assurance to manage uncertainty will move faster, not slower, towards deployment for an incredible market advantage in the long-term.

Reach out to continue the conversation about AI assurance to build trust into your AI strategy.