Maurizio Galardo, Chief Technologist at AVEVA, explains how Physical AI goes beyond software to interact with and optimize real-world industrial systems, reshaping automation, human-machine collaboration, and operational performance.
- How do you define Physical AI, and how is it fundamentally different from today’s software-only or generative AI systems?
Physical AI refers to AI systems that are not only capable of reasoning and learning, but also of interacting with and influencing physical assets, processes, and environments. Unlike software-only or generative AI, Physical AI must operate within the constraints of physics, safety, time, and reliability.
In industrial contexts, this means moving from AI that explains or predicts, to AI that supports or executes decisions in real operations, where outcomes have real-world consequences for safety, availability, energy efficiency, and sustainability.
- Is Physical AI best understood as robotics with AI, or AI that happens to have a physical embodiment?
The most accurate view is Physical AI as part of a closed-loop cyber-physical system. Robotics is one expression, but in industry the “physical embodiment” is often a process, a production line, a utility network, or an energy system.
What matters is not the form factor, but the continuous loop between sensing, simulation, intelligence, and action, anchored in engineering context and operational reality.
- What are the biggest technical challenges in moving AI from virtual environments into unpredictable physical worlds?
The key challenges are:
- Variability and drift in real operations compared to training conditions
- Safety, trust, and governance, especially in regulated and mission-critical environments
- Real-time integration with existing control systems, data infrastructure, and OT cybersecurity
In practice, success depends less on individual models and more on system architecture, lifecycle management, and the ability to ground AI decisions in engineering constraints and operational knowledge.
- How important are simulation, digital twins, and synthetic data in training Physical AI systems?
They are foundational. Digital twins provide the engineering backbone that allows AI to understand how a physical system is designed, how it should behave, and what constraints apply.
Simulation and synthetic data enable training and validation across normal, abnormal, and rare scenarios—many of which cannot be safely or economically observed in live operations.
Over the next months, we’ll see digital twins increasingly used not just for visualization or what-if analysis, but as active learning environments for industrial AI.
- Which industries will feel the impact of Physical AI first?
Industries with high asset intensity, strong instrumentation, and clear operational KPIs will see impact first—manufacturing, energy, infrastructure (including datacenter), and utilities in particular.
In these sectors, Physical AI can deliver near-term value by improving operational stability, energy efficiency, predictive insight, and decision consistency, especially in complex, distributed operations common in the Middle East.
- How does Physical AI change traditional automation and industrial robotics strategies?
Traditional automation excels at repeatability but struggles with change. Physical AI introduces adaptability and context awareness, allowing systems to respond to variability while respecting operational limits.
Strategically, this shifts industrial automation from static logic toward continuously learning systems, where performance is optimized over time using operational data, digital twins, and AI-driven insights—without compromising safety or control.
- What role do edge computing and real-time inference play in making Physical AI practical?
They are essential. Many Physical AI use cases require low-latency, deterministic response, and continued operation even with limited connectivity.
Edge computing enables real-time inference close to the asset, while cloud platforms—such as industrial cloud environments—support fleet-level learning, model lifecycle management, and cross-site optimization. The practical architectures emerging now are hybrid by design.
- How will Physical AI redefine the relationship between human workers and intelligent machines?
Physical AI will increasingly act as an operational copilot—providing early warnings, recommendations, and scenario evaluation—while humans retain authority over high-risk decisions.
Rather than replacing expertise, it helps scale best practices, reduce cognitive load, and make complex systems more transparent and manageable, especially for less experienced operators.
- Is Physical AI more likely to augment human capability or replace certain roles entirely?
In the 12–24 month horizon, augmentation is clearly the dominant model. Physical AI will automate specific, bounded tasks, but its greatest value comes from amplifying human decision-making, improving consistency, and enabling safer, more efficient operations.
Over time, roles will evolve, with greater emphasis on supervision, optimization, and system stewardship rather than manual intervention.
