Richard Clough is the EY Global Chief Data Officer, mobilizing the AI Ready Data Strategy and AI Ready Data Governance.
Every large organization now has an AI strategy, but almost all of it is aimed at AI that lives on a screen, not the AI that will drive vehicles, operate machinery and move goods through warehouses.
To capitalize on physical AI, enterprises need what many still lack: data architecture that supports real-time decisions in physical environments, trusted simulation data at scale and governance robust enough for machines operating in the real world. These requirements differ fundamentally from those underpinning most existing data strategies, and the gap is widening faster than leaders realize.
This article examines where that gap exists, why a continuous governance flywheel is the only model that scales safely, and three concrete moves leaders can make now to close it before the market forces them to.
Why Digital AI Strategy Doesn’t Transfer To Physical AI
Many enterprises separate operational systems that capture activity from their analytical systems that turn data into dashboards, forecasts and models. The resulting latency between analysis and action was usually acceptable because AI-assisted decisions were removed from immediate physical consequences.
Physical AI collapses that distance. A warehouse robot sharing a floor with people cannot wait for tomorrow’s data; it needs continuous flows to make decisions as conditions change. The old hand-off between operational and analytical data must therefore become a closed loop, with sensing, inference and execution happening in near real time.
Physical AI also demands a more comprehensive data foundation, one that combines traditional digital data with physical data such as layout, spatial geometry and location. When we train a chatbot, we feed it historical records, including text, transactions and spreadsheets. Physical AI needs to understand how a warehouse layout change affects the safe operating path of every autonomous vehicle on the floor, or how a packaging line should adjust its grip pressure when switching between product types mid-shift.
Spreadsheets cannot teach these dynamics, and real-world training may be dangerous, impractical or impossible. Simulation creates a viable path, enabling millions of variations overnight instead of decades of trial and error. Yet each variation must be physically accurate, visually realistic and properly labeled, creating petabyte-scale datasets beyond the capacity of most enterprise infrastructure.
Why Physical AI Needs A Data Flywheel
If physical AI demands continuous sensing, learning and action, then governance cannot be split across siloed data and systems. It has to run as a loop, reinforcing trust with every cycle.
Synthetic data must be packaged properly so teams can find and trust it, cleared against compliance rules and validated before deployment into a training pipeline. What makes this a flywheel is the returning feedback: where the system failed, where real conditions diverged from simulation and where behavior drifted. Those findings feed back into the data layer, improving the next simulations and tightening governance.
Each rotation makes the next one better. Better lineage improves simulation quality, which produces better model behavior, which generates better outcomes and richer feedback, strengthening governance by exposing the gaps previous cycles missed.
When the flywheel is weak, the opposite compounds. Poor provenance leads to weak training assumptions, brittle behavior, eroded trust, more friction and slower deployment, with each turn deepening the problem rather than correcting it.
Three Next Steps For Leaders
For some companies, robotics programs already carry real urgency and substantial potential value. Because the required data foundations take time to build, waiting until a use case becomes pressing means building under pressure.
1. Rebuild Your Data Architecture For Real-Time Action, Not Just Analysis
A real-time dashboard is not real-time AI. Data must move quickly enough to close the loop between sensing, inference and action, with immediate inference at the edge and model updates, simulation and fleet-wide learning in centralized systems.
Start by mapping current capabilities against the edge-to-cloud requirements of one realistic physical AI use case.
2. Learn To Use Simulation Before You Need It At Scale
Most organizations have never built or managed synthetic datasets. They need the compute and storage for petabyte-scale data, tools for physically accurate variations and provenance standards covering origin, parameters, validation and intended use. When a robot acts, leaders must be able to explain why and trace the synthetic data behind the decision.
Start with a small simulation pilot for one relevant use case to expose gaps in infrastructure, governance and skills before the stakes rise.
3. Make Trust The Principle, Not Just The Aspiration
Physical AI systems share space with people, so trust must be built through continuous validation, feedback and correction—not a one-time compliance review. Each synthetic-data training cycle should include checkpoints where specialists confirm that simulated conditions match reality and learned behaviors are safe. A named individual or working group should own sign-off on training-data quality for every physical AI initiative.
The Question To Take Back To Your Leadership Team
Many boards have asked their technology leaders whether the organization is ready for AI. Very few have asked the harder version of that question: If our AI needs to operate in the physical world, making decisions in real time alongside people and machines, do we have the data architecture, the simulation capability and the governance flywheel to make that safe?
That’s a question worth raising at your next leadership meeting. Organizations that answer it honestly now will be positioned to deploy physical AI responsibly when the market demands it. Those that do not will be forced to retrofit trust into systems that should have had it from the start.
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