Form pilot to scale – When AI meets robotics
Explore how AI-powered robots tackle variable tasks in real-world environments.
You are currently viewing a placeholder content from HubSpot. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.
More Information
Physical AI & Agentic AI explained.
What is Physical AI and how does it work?
Physical AI enables machines to perceive and act in the physical world. It uses sensors and AI to recognize objects, interpret their environment, determine actions and execute movements in real time. Unlike conventional robotics, Physical AI can adapt when conditions change, for example, when an object has a different shape, weight or position than expected.
What is Agentic AI and what role does it play in autonomous systems?
Agentic AI provides the planning and decision-making intelligence behind autonomous systems. It manages complex workflows, sets priorities, plans tasks across multiple steps and reacts when reality deviates from the original plan. In combination with Physical AI, it determines what needs to happen while the physical system executes the required actions.
How do Agentic AI and Physical AI work together?
Agentic AI plans and decides; Physical AI perceives and acts. A continuously updated world model connects the two. Physical AI uses sensor data to understand the current environment and execute actions, while Agentic AI compares the results with the plan and replans when necessary. This feedback loop enables autonomous systems to respond to real-world variability.
How can Physical AI and Agentic AI create business value in manufacturing and logistics?
The combination makes physical tasks economically viable that are too variable for rigid automation and too costly, strenuous or dangerous for people. In manufacturing, autonomous systems can adapt to changing product variants and tolerances. In logistics, they can dynamically plan orders, identify and handle different items, and replan when priorities or conditions change. Early warehouse deployments show significant performance gains and payback periods of under two years for AMR-based automation.
What are the main challenges of implementing AI-powered robotics and industrial automation?
The biggest challenges are not purely technical. Companies need robust data integration, suitable foundation models, reliable connections between the Agentic AI layer and the technical infrastructure, and a resilient ecosystem of hardware, models and systems expertise. Governance, safety certification, liability and compliance with the EU Machinery Regulation and EU AI Act are also critical. Finally, successful adoption requires training, acceptance and change management.
How can companies scale autonomous robots from pilot projects to real-world operations?
Scaling requires a structured path from use-case discovery and validation to operational deployment. Companies should first identify the right use cases, test them under real operating conditions and resolve technology and integration risks before moving into production. A strong partner ecosystem and the right data and infrastructure foundations are essential for scaling autonomous robots reliably and compliantly.
Explore your use case
Discover where Physical and Agentic AI can create value in your operations.
Autonomous robotics is already delivering measurable value.
From potential to business value
Autonomous robotics is moving from isolated pilots to real-world applications. When Physical AI and Agentic AI work together, robots can adapt to changing conditions, handle complex tasks and create measurable value at scale.
1bn+
Picks completed across more than 40 DHL sites in its long-standing collaboration with Locus Robotics.
<2 yrs
Payback period for flexible AMR-based warehouse automation in most cases, according to industry analyses.
