Physical AI connects digital intelligence with real-world operations, turning products, environments and workflows into an integrated network of value engines that reshape how enterprises create and capture value
For decades, AI lived mostly inside screens and software. The world moved from early deterministic machine learning and special purpose industrial robots to perception systems, including computer vision, speech and natural language, and then to the breakthrough of Generative AI, which can create new content, code and conversations at superhuman speed, with Agentic AI adding reasoning, memory and autonomous decision-making.
Most of this innovation has remained trapped in the digital world, touching only a slice of enterprise value. Physical AI changes that. By combining Perception AI, Generative AI and Agentic AI with robotics and connected machines, we are now embedding intelligence directly into products, assets and operations. Therefore, there is no doubt that the Physical AI market is positioned for exponential growth.
From deterministic automation to Physical AI
In manufacturing plants, warehouses and transport networks, deterministic AI and special-purpose robots have worked for years, repeating specific tasks that are unsafe, unsuitable or uneconomic for humans. They boosted productivity but remained narrow, brittle and costly to re-task.
What has changed is that Perception AI can now sense the environment, Generative and Agentic AI can reason and decide in context, and robotics can execute those decisions in the physical world. Physical AI is this convergence in action. Intelligent agents that can sense, decide and act in real time across factories, warehouses, hospitals, cities and energy systems, turning AI from a purely digital capability into a driver of physical outcomes at scale.
Why Physical AI is now enterprise-ready
More than 95% of what we consume are still physical, such as goods, infrastructure, energy, healthcare, mobility and built environments. Embedding intelligence into this physical fabric is a profound disruption, and several forces over the last 12-18 months have made it both urgent and feasible.
Firstly, the need. Supply chain shocks, geopolitical risk and a renewed focus on in-country production have exposed the fragility of globally distributed, labour-intensive operations. At the same time, many economies face acute labour shortages in manufacturing, logistics and field operations. Competitive advantage increasingly depends on the level of autonomy in your core operations; autonomous plants, warehouses, ports and energy assets are becoming a strategic imperative, not a nice-to-have.
Secondly, the technology has crossed a threshold. Perception AI has matured, while Generative and Agentic AI dramatically reduce the need for hand-crafted algorithms and task-specific models. Instead of building and maintaining thousands of narrow models for each workflow, we can orchestrate a smaller number of powerful foundation models with domain-specific fine-tuning. From GPUs to simulation tools and robot platforms, platform players such as NVIDIA, for example, are building full stacks explicitly architected for Physical AI and robotics.
Thirdly, simulation has changed the economics and risk of Physical AI. It’s difficult to safely beta test an autonomous vehicle, refinery robot or surgical assistant purely in the real world. The cost, safety risk and regulatory friction are simply too high. With high-fidelity simulation, we can train and stress-test agents in virtual replicas of plants, cities, vehicles and devices, then transfer those policies into real robots and assets at a fraction of the cost and risk. This is one reason Gartner analysts now list AI entering the physical world as a top strategic technology trend for 2026.
Why AI pilots stall and how to move to proof-of-value
Despite this promise, many organisations are stuck in what I call the POC trap. Pilots are often misunderstood, misdesigned and disconnected from business outcomes, so they never earn the right to scale.
The first problem is that teams try to prove what is already proven. They run POCs to check whether a camera can read a label, a LiDAR sensor can provide depth, or a robot arm can move on command. These are solved problems, so you inevitably conclude there is no case to scale and expend energy without learning anything useful about value.
Additionally, pilots are rarely framed as proof-of-value. A Physical AI pilot should be anchored in a specific outcome. For example, reducing quality defects by 60%, eliminating a class of safety incidents or increasing throughput on a line by 20%. The design should start from that outcome and ask, ‘If we deploy these agents and robotics on this workflow for 7-14 days, what measurable change should we see?’
Finally, fear of missing out drives vanity experiments. Leaders rush to announce that they have run 100 AI pilots or deployed dozens of bots, but the real goal subtly becomes pilot count, not business impact. This creates a theatre of experimentation that burns time and credibility without building the foundations for scale. To escape this, you must redefine your pilots explicitly as proof-of-value, not proof-of-concept, and design them backwards from outcomes.
De-risking and scaling responsibly
A common question I hear is, ‘How do we implement AI without risk or errors?’ Aiming for a completely risk-free or error-free implementation is utopian. The right comparison is not against perfection, but against today’s human-only baseline. Human centric operations are not devoid of mistakes, errors and consequences. In contrast, with the augmentation of AI, we get a tremendous boost in capability and value at a much-reduced risk.
We must think in terms of risk thresholds and impact. A misrouted question in a customer service chatbot has a very different risk profile from a misclassified pedestrian in an autonomous driving system. For each process and industry, we must assess which errors are tolerable, which are not, and what safeguards are needed to reduce both the probability and impact of failure.
Practically, I recommend layered architectures where recommendation, review and decision are separated into different agents, each with its own rules and thresholds, so errors get caught across layers rather than at a single point. Human-in-the-loop remains essential for oversight, with hands-on control until accuracy and robustness reach thresholds.
Responsible AI needs to be built in from the design stage, with explicit guardrails for safety, fairness and bias, and simulation should be treated as a first-class safety mechanism for Physical AI deployments.
What AI-native enterprises really look like
AI-native is a fashionable phrase, and many organisations interpret it as AI-first. I disagree with that framing. Every organisation has a core mission: to design safer, more sustainable vehicles, to keep a telecoms network resilient, to provide reliable energy or to deliver excellent healthcare.
In my view, an AI-native enterprise is one that puts AI at the centre of how it pursues its core mission, whether that’s in its products, assets, processes and services, rather than treating AI as a side project or bolt-on. It uses Physical AI to re-imagine how products are designed, built and serviced, how networks self-heal, how energy systems are monitored and optimized and how clinicians are assisted in diagnosis and treatment.
On the bottom line, Physical AI’s benefits are clear: higher productivity, less waste, fewer defects and more autonomous operations across plants, warehouses, ports, rigs and clinical environments. On the top line, the impact is equally powerful but often underestimated. Simulation and AI-assisted design mean you can bring offerings to market earlier and closer to customer needs, increasing revenue and market share, while intelligent products that can self-monitor, self-heal and communicate their own service needs unlock new as-a-service and outcome-based revenue models.
A five-step roadmap to scale Physical AI
To move from pilots to profit, organisations need more than isolated use cases. Instead, they need a clear, practical roadmap. While every enterprise has its own context, I see five steps that apply broadly.
One, reimagine products, assets, services and processes end-to-end. AI can’t simply be added on top of existing workflows. The real competitive advantage will come from re-versioning entire business operations with AI and robotics at the core, while deliberately consuming innovation from outside your four walls.
Two, start with the end in mind and define outcomes before pilots. Avoid pilots that merely prove technical feasibility that the market has already proven. Begin with clear outcome targets. For example, a defined productivity uplift, a reduction in safety incidents or a measurable improvement in service uptime and design your Physical AI pilots to test whether those outcomes are achievable.
Three, create a journey map and align resources. Physical AI touches IT, OT, engineering, operations, safety, compliance and HR. A journey map should sequence capabilities, clarify dependencies and define how humans curate data, govern AI behaviour and ensure safe operation with your talent, partners and platforms.
Four, make simulation a core capability, not an afterthought. Before you deploy robots, autonomous systems or AI-driven control changes into live environments, test them in virtual twins of your plants, cities or assets. Simulation lets you try many scenarios at low cost, uncovering optimal configurations and avoiding expensive missteps in the real world.
Five, embrace disruption and ride the wave because speed matters. In this transition, there is no easy strategy. The gap between leaders and others will be structural and profound. Winners will be those who embrace disruption early, move fast and deploy Physical AI purposefully at scale, while keeping a clear line of sight to safety, ethics and outcomes.
From value chains to value engines
Looking ahead, I expect Physical AI to reshape not just individual factories, hospitals or cities, but the very structure of enterprises. Traditional linear value chains and rigid functional silos will give way to value engines based on networks of intelligent assets, AI agents and people working fluidly across boundaries towards shared outcomes, such as customer satisfaction, safety, sustainability and profitability.
There will be workforce disruption, as there always is with major technological shifts, but history shows that as productivity rises, human demand and ambition rise even faster. New categories of work will emerge around designing, orchestrating and governing these value engines, and I am personally optimistic that, in the long term, net employment and overall prosperity will grow.
We are orchestrating AI for the good of humanity: safer workplaces, more resilient infrastructure, faster medical breakthroughs, more sustainable industries and richer experiences for customers. Enterprises that move beyond pilot theatre, embrace Physical AI as a core capability and follow a disciplined, outcome-driven roadmap will define what it truly means to be AI-native in the years ahead.
