Artificial intelligence (AI) is moving beyond screens and into systems that can move, touch and act in the physical world through robots, machines, vehicles and infrastructure. One market forecast estimates that the Physical AI market will grow from roughly US$7 billion in market value in 2026 to more than US$430 billion by 2030. If verified, that would show how quickly capital and attention are shifting. For the Middle East, where strategically important industries are built around physical assets and complex operations, the opportunity is difficult to ignore.

Oil and gas remains a cornerstone of Gulf economies, while logistics is becoming increasingly important as the region positions itself as a global trade and transportation hub. These are precisely the environments where physical AI could have significant impact, from autonomous inspection and maintenance to intelligent logistics, robotics and increasingly automated industrial operations.

The opportunity is considerable, but so is the question that comes with it. As AI moves from generating information to making decisions and taking action, how should organisations govern systems that can act at machine speed and increasingly affect the physical world?

The real risk is not rogue AI

Popular narratives about AI swing between utopia and apocalypse. Robots will save us, or robots will destroy us. Both miss the point. The real threat is not rogue AI or sentient machines, but ungoverned autonomy.

Agents operating at machine speed, connected to imperfect data and deployed through operating models never designed for physical consequences, are the real risk. Imagine a logistics operation running hundreds of autonomous machines. A routine update introduces a subtle data mismatch, several systems begin operating outside approved parameters and one enters an area occupied by people.

There is no malice. There is no sentience. There is simply autonomy operating at speed, connected to stale or inaccurate data, without effective guardrails. Enterprises have seen this failure pattern for decades with software, where rapid deployment, siloed ownership and governance bolted on after something breaks, have become the norm. Physical AI simply makes those familiar gaps consequential.

Five reasons physical AI changes the governance equation

Physical AI does not add one new risk. It amplifies existing governance gaps simultaneously.

First is kinetic agency. AI does not simply recommend an action. It moves, touches and changes the physical world, while many existing risk frameworks still assume a predominantly digital impact. Second is speed of action. Autonomous systems can make decisions at machine speed, while approval workflows and oversight processes remain designed around human timescales. Third is data dependency. Physical AI relies on real-time sensor data combined with enterprise information. Data quality is no longer simply a prerequisite for good analysis. It becomes an operational control. Fourth is self-optimisation. As systems adapt how they achieve objectives, traditional change-management processes can struggle to keep pace. Finally, there is the expanded attack surface. Prompt injection in AI-enabled systems, sensor spoofing and adversarial inputs introduce new vulnerabilities into systems that can have consequences beyond the digital environment.

The catastrophic scenario therefore isn’t the dramatic robot rebellion. It is mundane, predictable wrongness at scale, from misread information and incorrect temperatures, to routing collisions or flawed decisions. Each is trivial in isolation, but multiplied across thousands of agents and millions of decisions, minor issues can become regulatory, reputational or operational events.

The same question applies to digital agents

Physical AI governance and the region’s broader ambitions for agentic AI reinforce each other rather than compete for attention. Getting physical AI governance right builds the muscle, and the trust, needed for agentic AI, more broadly, so organisations that invest here put themselves ahead on both fronts at once.

The governance principles required when AI can control a machine share many of the same foundations as those required when an AI agent can act on behalf of a citizen, customer or employee.

Across the Middle East, governments and businesses are exploring AI systems that can increasingly act on behalf of people. The UAE’s growing focus on agentic AI in government services is one example of a future where AI agents could navigate processes, make decisions and execute tasks with a degree of autonomy. These systems may not have wheels, motors or robotic arms, but the underlying governance question is remarkably similar.

What authority does an agent have? What data can it access? What decisions can it make? Who is accountable when it gets something wrong? How quickly can its actions be detected, stopped or reversed? These questions matter whether an AI agent is directing a machine in a warehouse or helping to deliver a government service.

When governance failures occur in digital systems, the impact can already be financial, operational or reputational. When those systems are connected to critical services and interact directly with the public, the consequences can become much broader. The principle is therefore bigger than physical AI. As AI becomes more autonomous, governance has to evolve alongside it.

Two futures. Same technology.

The Middle East has an opportunity to approach this differently. Rather than waiting for autonomy to become widespread and then bolting governance on after something goes wrong, organisations can begin thinking about governance at the same time they think about autonomy. That is particularly important in a region where governments and businesses are making ambitious bets on AI and moving quickly to turn those ambitions into operational reality.

One path leads to autonomous systems being deployed quickly, with unclear identities and boundaries, fragmented ownership, compliance added after deployment and human oversight operating at machine speed. Data quality issues, security vulnerabilities and small operational errors compound until the organisation can no longer intervene effectively.

The other looks very different. Autonomy is developed with clear accountability, defined boundaries, governed access to data, appropriate testing and continuous oversight. The technology is the same, but the operating model around it changes what that technology can safely achieve.

The Middle East is already thinking seriously about what AI can do for its economies, governments and industries. The next question is how seriously it thinks about what AI should be allowed to do. The recommendation is simple: before autonomous systems are scaled, organisations should define ownership, data controls, testing standards, escalation routes and clear stop mechanisms. Until then, the issue is not only AI safety. It is governance maturity.

By Mohamed El Yahya, Managing Partner, Global Infrastructure Services (GIS), Middle East & Africa at DXC Technology

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