The rapid evolution of artificial intelligence is no longer confined to software environments. Today, AI is moving into the physical world—powering robots, industrial machines, sensors, and autonomous systems. This shift, often referred to as Physical AI, is transforming industries at an unprecedented pace. However, it is also raising complex governance challenges that organizations can no longer ignore.
As AI systems begin to interact directly with real-world environments, the stakes increase significantly. It is no longer just about whether an AI model can generate accurate outputs. The real question is: how do we test, monitor, and control these systems when their decisions translate into physical actions?
The Rise of Physical AI Across Industries
Physical AI encompasses a broad category of technologies, including robotics, edge computing, and autonomous machines. These systems combine artificial intelligence with physical capabilities, enabling them to sense, decide, and act in real-world environments.
The growth of industrial robotics highlights the scale of this transformation. According to the International Federation of Robotics:
- 542,000 industrial robots were installed worldwide in 2024
- This figure is more than double the annual installations recorded a decade earlier
- Installations are expected to reach 575,000 units in 2025
- The number could surpass 700,000 units by 2028
This rapid expansion demonstrates how deeply embedded automation has become in modern industries.
At the same time, market analysts are expanding the definition of Physical AI to include a wider range of intelligent systems. Grand View Research estimates that:
- The global Physical AI market will reach $81.64 billion in 2025
- It could grow to $960.38 billion by 2033
While these projections vary depending on how vendors define “intelligence,” they reflect the massive economic potential of this emerging sector.
From Digital Output to Physical Action
One of the most critical differences between traditional AI and Physical AI lies in how decisions are executed.
In software-only systems, AI outputs remain digital—recommendations, predictions, or automated responses. In Physical AI, those outputs become actions. A model’s decision can:
- Move a robotic arm
- Trigger industrial machinery
- Adjust infrastructure systems
- Respond to sensor data in real time
This shift fundamentally changes the governance landscape.
Why Governance Becomes More Complex
When AI interacts with physical systems, errors can have immediate and tangible consequences. A faulty decision could:
- Damage equipment
- Disrupt operations
- Compromise safety
- Put human lives at risk
As a result, governance is no longer just about compliance—it becomes a core part of system design.
Google DeepMind’s Robotics Push
A leading example of Physical AI development comes from Google DeepMind. The organization has been actively working on integrating advanced AI models into robotics.
In March 2025, it introduced:
- Gemini Robotics
- Gemini Robotics-ER
Both models are built on Gemini 2.0 and are designed specifically for robotics and embodied AI applications.
What These Models Do
- Gemini Robotics: A vision-language-action model that can directly control robots
- Gemini Robotics-ER: Focuses on reasoning, spatial understanding, and task planning
These systems enable robots to:
- Interpret natural-language instructions
- Identify objects in their environment
- Plan and execute multi-step actions
- Evaluate whether tasks are completed successfully
For example, robots powered by these models can perform tasks such as folding paper, packing items, or handling unfamiliar objects.
The Core Capabilities of Physical AI
According to Google DeepMind, effective robotic systems require three essential capabilities:
1. Generality
The ability to operate in unfamiliar environments and handle new objects.
2. Interactivity
The capacity to respond to human input and adapt to changing conditions.
3. Dexterity
The precision needed to perform complex physical tasks.
These capabilities go beyond traditional AI functions and require a combination of perception, reasoning, and control.
The Importance of Success Detection
In Physical AI, success detection is a critical component.
Unlike software systems, where outcomes can be easily measured, physical systems must determine whether a task has been completed correctly. This involves:
- Evaluating environmental feedback
- Deciding whether to retry a task
- Determining when to stop
Without accurate success detection, systems risk repeating errors or failing silently.
Advancements in Embodied AI Models
In April 2026, Google DeepMind introduced an updated model:
- Gemini Robotics-ER 1.6
This version enhances capabilities such as:
- Spatial reasoning
- Task planning
- Intermediate decision-making
- Autonomous retry mechanisms
The model is available in preview through the Gemini API, enabling developers to integrate these capabilities into real-world applications.
Developer Ecosystem
Tools like Google AI Studio and the Gemini API allow developers to:
- Test AI models
- Build applications
- Integrate AI into robotics systems
This ecosystem brings AI development closer to real-world deployment.
Governance Moves Into System Design
As AI systems gain the ability to act autonomously, governance must be embedded directly into their architecture.
Key Governance Controls
Organizations must define:
- What data AI systems can access
- Which tools they can use
- What actions require human approval
- How activities are logged and audited
These controls ensure that AI operates within safe and predictable boundaries.
Insights from Industry Research
Research from McKinsey & Company highlights a significant gap in enterprise AI readiness:
- Only about one-third of organizations report mature governance frameworks
- Many systems are already operating with increasing autonomy
This mismatch creates risks as AI systems become more powerful.
Safety in Physical AI Systems
In robotics, safety extends beyond data and algorithms to include physical behavior.
Layers of Safety
According to Google DeepMind, safety operates at multiple levels:
- Low-level controls: Collision avoidance, force limits, stability
- High-level reasoning: Determining whether an action is safe in context
Both layers must work together to prevent accidents.
The Role of Safety Datasets
To improve safety evaluation, Google DeepMind introduced:
- ASIMOV
This dataset is designed to test whether AI systems can:
- Understand safety instructions
- Avoid unsafe actions
- Operate responsibly in physical environments
Such tools are essential for validating AI behavior before deployment.
Governance Frameworks for Physical AI
Organizations are increasingly relying on established frameworks to manage AI risks, including:
- NIST AI Risk Management Framework
- ISO/IEC 42001
These frameworks provide guidelines for:
- Risk assessment
- Accountability
- Lifecycle management
However, Physical AI requires extending these frameworks to include hardware and environmental factors.
Industry Collaboration and Testing
Google DeepMind has partnered with several robotics companies to advance embodied AI, including:
- Apptronik
- Agile Robots
- Agility Robotics
- Boston Dynamics
- Enchanted Tools
These collaborations enable real-world testing and validation of AI systems.
Real-World Use Cases
One example includes robotics tasks such as instrument reading, which require:
- Visual interpretation
- Contextual understanding
- Accurate decision-making
These applications demonstrate the practical potential of Physical AI.
Applications Across Industries
Physical AI is already being applied in various sectors:
Manufacturing
Automating production lines and improving efficiency.
Logistics and Warehousing
Optimizing inventory management and delivery systems.
Industrial Inspection
Monitoring equipment and detecting anomalies.
Facilities Management
Managing infrastructure and environmental conditions.
In each case, governance plays a critical role in ensuring safe and effective operation.
The Core Governance Question
As Physical AI continues to evolve, organizations must answer a fundamental question:
How do we define and enforce limits on autonomous systems before allowing them to act independently?
This includes:
- Setting safety boundaries
- Establishing escalation protocols
- Ensuring accountability
Without clear answers, the risks associated with Physical AI could outweigh its benefits.
Looking Ahead: The Future of Physical AI Governance
The growth of Physical AI is inevitable, driven by advancements in technology and increasing demand for automation. However, its success will depend on how effectively organizations address governance challenges.
Key Priorities for the Future
- Embedding safety into system design
- Developing robust testing frameworks
- Aligning AI behavior with real-world constraints
- Ensuring transparency and accountability
Conclusion
Physical AI represents the next frontier in artificial intelligence, bringing digital intelligence into the real world. While this shift unlocks enormous potential, it also introduces complex governance challenges that cannot be ignored.
From industrial robots to advanced AI models like Gemini Robotics-ER 1.6, the technology is evolving rapidly. But without strong governance, these systems risk becoming unpredictable and unsafe.
As organizations adopt Physical AI, the focus must shift from capability to control. The future of autonomous systems will not be defined solely by what they can do—but by how safely, reliably, and responsibly they can do it.
Read Also:
- OpenAI Missing Internal Targets
- SAP on Enterprise AI Governance
- Google Turns Agentic AI Governance Into a Product
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