This article, originating from Universal Robots (UR), highlights the increasing role of Artificial Intelligence in physical automation, particularly within robotics, by predicting trends such as industry-specific AI and a new data economy influencing physical AI by 2026. This suggests a future where AI and machine learning are further integrated into robotic systems, enabling greater autonomy, adaptability, and problem-solving capabilities in physical environments.
In manufacturing & industrial, logistics & supply chain, and transportation & automotive, this means smarter robots that can optimize processes, predict failures, and adapt to changing conditions, leading to reduced operational costs, increased throughput, and improved safety. Furthermore, the shift to a data economy creates opportunities for new revenue streams based on the data generated and processed by these robots.
For businesses, the shift towards physical AI implies a need to upskill the workforce to manage and maintain increasingly sophisticated robotic systems. The emphasis on industry-specific AI suggests a greater opportunity to tailor automation solutions to specific workflows, improving efficiency and reducing downtime. Furthermore, the growth of a data economy around robots allows operators to fine-tune their processes and improve performance by leveraging real-time data and predictive analytics.