Yinghao Song is the Head of North America and Product Lead at AGIBOT, a global pioneer in embodied AI and full-spectrum robotic systems. Melding an advanced technical acumen with elite commercial strategy—holding an MBA from the Kellogg School of Management at Northwestern University—Yinghao orchestrates AGIBOT’s international Go-to-Market (GTM) execution, product positioning, and developer ecosystem architecture. At the forefront of the hardware industrialization wave, he focuses on accelerating the commercial deployment of full-size humanoids, advanced quadrupeds, and dexterous hands across industrial and research sectors, bridging the gap between cutting-edge intelligence and enterprise-grade ROI scaling.
While generative AI has redefined the digital world, the true frontier lies in the physical realm—translating embodied AI into scalable, commercial reality. For global enterprises and scaling startups alike, the bottleneck is no longer just algorithm training, but the systemic deployment of hardware capable of execution in complex human environments. In this session, Yinghao Song, Head of North America & Product Lead at AGIBOT, will break down the strategic blueprint for taking advanced robotics from pilot projects to global Go-to-Market (GTM) execution. Drawing from AGIBOT’s rapid scaling velocity, the discussion will dissect the unique investment logic driving the humanoid and quadruped sectors, the core supply chain dynamics required to achieve mass-production inflection points, and the precise ROI frameworks enterprises demand before integration. Furthermore, Yinghao will address the critical "software-hardware gap," explaining how modular software stacks and open SDKs empower partners to deploy full-size humanoids and highly dexterous hands across automotive manufacturing, logistics, and unstructured research environments. Attendees will walk away with actionable insights on identifying high-value robotics use cases, building sustainable developer ecosystems, and mastering the cross-border scaling strategies necessary to win the 2026 embodied AI landscape.
