Physical AI

Physical AI is rapidly moving beyond research labs and controlled environments into enterprise operations. From manufacturing and logistics to healthcare, energy and critical infrastructure, organisations are beginning to deploy autonomous systems capable of understanding, reasoning and acting in the physical world. Explore the technological breakthroughs driving this transition, where investment is accelerating, and what business leaders should expect over the coming years.

Discussion points include:

• Why Physical AI has reached an enterprise tipping point.
• The industries seeing the fastest adoption.
• The technologies making large-scale deployment possible.
• The commercial opportunities and barriers ahead.

While many organisations have demonstrated promising pilots, few have successfully deployed Physical AI at enterprise scale. This panel explores the operational, organisational and technical challenges of moving from experimentation to production.

Key discussion points:

• Building the business case for Physical AI.
• Integrating autonomous systems into existing operations.
• Workforce readiness and organisational change.
• Measuring ROI and long-term value.

Agentic AI is beginning to transform scientific discovery by autonomously designing experiments, analysing results and accelerating innovation across pharmaceuticals, materials science and advanced manufacturing. Discover how organisations are combining foundation models with laboratory automation to reshape research and development.

Large Vision-Language-Action models are redefining what autonomous systems can achieve. Explore how advances in multimodal reasoning, embodied intelligence and world models are enabling robots to understand complex environments, generalise across tasks and safely interact with the physical world.

We’ll explore:

• Vision-Language-Action models.
• World models and reasoning.
• General-purpose robotics.
• Embodied intelligence.
• The road towards more capable autonomous systems.

Successful Physical AI systems depend on far more than increasingly powerful foundation models. Discover how organisations are using simulation, synthetic data and digital twins to train, validate and continuously improve autonomous systems before deployment.

Topics include:

• Digital twins.
• Synthetic data generation.
• Simulation environments.
• Model evaluation.
• Bridging the simulation-to-reality gap.

As AI moves from the digital world into vehicles, robots and other physical systems, a new technology stack is emerging. What will it take to build intelligent machines capable of understanding, adapting to and acting reliably in the real world?

• Foundation models for the physical world – how general-purpose models are changing robotics and autonomous systems
• Vision-language-action & embodied intelligence – connecting perception, reasoning and physical action
• Data, simulation & world models – how machines learn about environments before and during deployment
• The generalisation challenge – moving beyond predefined environments, routes and tasks
• Evaluation, safety & reliability – proving autonomous systems can handle the unpredictability of the real world
• Build vs buy – what should enterprises own and where will shared platforms and models emerge?
• Different bodies, shared intelligence – autonomous vehicles, humanoids and specialised robots: where different embodiments make sense
• From pilots to deployment at scale – what needs to change to move Physical AI into widespread real-world operation

Remote Robotics Operations at Scale – Safety, Autonomy & Industrial Deployment

• How robotics, AI, and remote operations are transforming inspection and monitoring in energy infrastructure
• Building trusted autonomous systems that operate reliably in harsh, safety-critical environments

Physical AI requires a new generation of infrastructure spanning edge AI, high-performance compute, intelligent sensors, networking and real-time connectivity. Explore the technology stack enabling autonomous systems to operate reliably and securely across industrial environments.

Key themes include:

• Edge AI and distributed compute.
• Networking and connectivity.
• Sensors and perception.
• Orchestration platforms.
• Scalable infrastructure.