SuperAI 2025 Explores the Exciting Potential for AI in Robotics, but Can the Hype Translate into Actual Value?

The difference between traditional automation and embodied Artificial Intelligence (AI) lies in how machines engage with the world around them. While conventional robots are designed to execute pre-programmed, repetitive tasks, embodied AI systems use sensory inputs to perceive their surroundings, make decisions, and act in real time. This evolution in robotics was showcased prominently at the SuperAI 2025 conference in Singapore, bringing attention to the technology’s current maturity and the aspirational trajectory of future AI development.

As one of the premier global gatherings for AI in Asia-Pacific, SuperAI provided a platform for technology vendors to demonstrate their latest achievements and breakthroughs. Leading AI robotics companies, such as Auki from Hong Kong, China's Unitree Robotics, and Singapore's Weston Robot, were all present at the show to exhibit their latest AI-enabled robotic canines, drones, and vehicles that are capable of remarkable sophistication in responding to sound and touch. While still early in the development cycle, these demonstrations showed how AI and robotic technology could work synergistically to unlock new value, such as 1) increased autonomy of robotic operations, 2) enabling higher complexity tasks to be fulfilled, and 3) improving the safety of human-robotic collaborative operations.

The ability of embodied AI systems to perceive and interpret their environment and actively respond will be a breakthrough for productivity and efficiency in many industries. In manufacturing and industrial operations, sensor-driven robots are being developed to fill critical labor gaps and increase productivity on factory floors. These robots increasingly evolve from simple programmed routines to adaptive and responsive actions based on environmental cues. For example, Boston Dynamics’ Spot robot follows a technician around Hyundai’s car assembly process and functions as an inspection tool through computer vision and autonomous adaptability to environmental cues. In logistics and supply chain management, combining AI with physical robotics can enhance operational efficiency and result in measurable cost savings at ports, warehouses, and other transport hubs. This approach extends beyond traditional conveyor belts and sorting machines to include Autonomous Mobile Robots (AMRs) capable of navigating complex environments and working alongside human supervisors for collaborative picking tasks.

However, despite the high potential of robotics, several critical barriers will continue to hinder widespread adoption. Some of these limitations include:

• Supply Chain Vulnerabilities: With many key robotic components and assemblies concentrated in East Asia, the lack of diversification in production could risk potential disruptions from various factors, such as natural disasters. Growing geopolitical tensions like tariffs could also disrupt access to key raw materials like semiconductors and other metals, which could increase costs.
• Unprepared Technical Infrastructure: Foundational AI models require extensive, task-specific fine-tuning training data that can take months or years to develop. In addition, the high complexity of real-world environments with vast amounts of variables will require the processing of large amounts of sensory information in real time, which will, in turn, demand significant computational resources.
• Economic Barriers to Entry: Initial costs remain prohibitive for many organizations, and sophisticated robots require high Capital Expenditure (CAPEX) to start implementation. Mny enterprises are struggling to develop compelling use cases that can deliver a clear Return on Investment (ROI) timeline.
• Workforce Adaptation and Integration Complexities: Successful implementation requires significant retraining strategies and cultural change management. Integrating and deploying robotics on work floors could take more than a year. This could be a substantial barrier for firms primarily looking at short-term ROIs.
• Physical Capabilities: Currently, robotics innovation cannot replicate the complexity and precise movements of human features, like hands, and it is also severely limited by remote power sources and battery life.

Despite the challenges, ABI Research foresees that AI-based robotics will become a critical enabler of new industrial and commercial use cases in the future, with the growing adoption of robotic solutions driving sales revenue (of robotic solutions) to approximately US$164 billion by 2030, representing a Compound Annual Growth Rate (CAGR) of 21.3% from 2024. Technology maturity is also reaching an inflection point in several other key adjacent areas, such as improvements to battery technology and robotic maneuverability, helping to overcome issues with limited operational durations and physical dexterity.

Commercial and industrial players looking to adopt or integrate robotics should strategically align their roadmap to identify key use cases and how ROI can be achieved. Focusing on high-value applications that solve key problems in workflows, such as the automation of hazardous tasks, augmenting labor shortages, or improving quality in precision-critical operations, should be the first step in identifying the role of embodied AI within the enterprise’s business processes. This can be achieved by conducting operational assessments to understand current pain points and determine where robotic solutions can provide immediate value, while minimizing implementation complexity.

Second, it is essential to invest in data infrastructure, which is critical to effective AI implementation. Building strong data pipelines through sensory node collection can increase the speed of robot deployments by simulating realistic environments before real-world deployments, significantly reducing training time and risks.

Lastly, upskilling the workforce through training and change management is another key to successful robotic-human collaboration. As robotics’ use case continues to drive adoption, workers must continually upskill and redefine their value on the factory floor, evolving into a human-oversight supervisory role capable of handling multiple aspects of the industrial process.