What CSCOs Need to Know for Supply Chain Strategy

While current physical AI applications already create value, data collection within manufacturing sites and warehouses remains an underinvested  opportunity for CSCOs. Manufacturing operations should be viewed as proprietary data assets that can become a source of competitive advantage as AI adoption accelerates. According to Gartner Director Analyst Tess Frenzel, “Collecting this proprietary data will help supply chains unlock more advanced intelligent simulation and digital twins, while also positioning the supply chain for more successful deployments of future autonomous robots.”

Potential risks:

• Hardware limitations: Advanced sensors, actuators and battery constraints can make physical AI systems costly to deploy and scale.

• Data collection and storage costs: Physical AI generates large volumes of data, requiring a clear storage strategy to balance performance and long-term costs.

Physical AI will make robotics more flexible and adaptable. Traditional robots were often limited to a single task and difficult to repurpose when business needs changed. Physical AI can enable robots to learn new tasks through software updates rather than hardware replacements. Advances in AI-enabled tactile systems will also allow robots to understand touch, texture, shape and force in addition to visual inputs. This will expand the range of warehouse activities that can be automated, including complex picking and packing tasks. As a result, organizations can improve operational agility while getting more value from their robotics investments.

Physical AI could enable smaller, highly autonomous facilities that reduce the need for traditional production footprints. As automation expands, network design decisions may shift from labor availability to customer proximity, which will help organizations improve responsiveness and resilience. This could also reduce transportation requirements and support sustainability goals. At the same time, advances in world models and digital twins will allow companies to test scenarios, evaluate investments and optimize operations with greater confidence. By improving the ability to simulate future outcomes, physical AI can accelerate innovation and decision making. While these changes are still emerging, leading CSCOs are already assessing their potential impact on future supply chain strategies.

Potential risks: 

• Commercial viability challenges: High upfront costs and uncertain returns can slow investment in smaller, autonomous facilities.

• Computational costs and model limitations: Advanced world models and digital twins require significant computing power and may struggle to predict outcomes in highly uncertain scenarios.