Scale at Speed™
English (Global)
French (Canada)
Abstract
Robotics is emerging as the next wave of AI-driven transformation, with growing adoption across manufacturing, healthcare, logistics, and autonomous systems. Unlike traditional AI, robotics operates at the intersection of physical and digital ecosystems. This creates distinct challenges for enterprises in capturing realistic scenarios, managing complex environments, and building scalable operations that support continuous learning.
By investing early in scalable data ecosystems, strong governance, and experienced partners, enterprises can reduce development costs, improve robot performance, and scale robotics initiatives more effectively.
Key Insights
Robotics Requires High-Quality Data to Perform Human-Like Activities
Enterprises need to build a strong foundation of diverse, use-case specific data for effective training. These inputs typically combine digital information, physical-world observations, video streams, sensor signals, and environmental context to support accurate robotic behavior and decision-making.
Data Value in Robotics Depends on Context and Synchronization
Our experience across real-world robotics programs reveals three critical phases that shape data quality and training readiness. These are data collection, data onboarding, and data operations. Enterprises prepare the data they collect for training to result in a continuous feedback loop.
Future-Ready In Robotics Depends On Scalable Data Systems
Our robotics data readiness framework involves understanding scenarios, designing the collection strategy, preparing the data ecosystem, and more. By establishing a system that supports strong data handling and governance, we help enterprises build a robust data foundation for robotics.
