LEAP 2026 conference takes AI into the physical world: computing hardware and embodied robots undergo field tests
The second day of the “LEAP 2026” conference in Riyadh, held alongside the “Deep Fest” event specializing in deep technologies, marked a pivotal turning point in the trajectory of AI deployment. Discussions and technical presentations moved beyond theoretical promises and software capability demos on screens, shifting directly to operating mechanisms in real-world physical environments, across four core pillars: embodied robots, intelligent prosthetic limbs, complex computing infrastructure, and national-level institutional integration.
In the embodied robotics and interactive systems sector, the global companies present demonstrated a focus on moving inference algorithms from virtual environments to the real world. Abel Ding, Chief Operating Officer for the Middle East, Asia and Pacific region at the Chinese company “Agibot”, explained that AI-enabled embodied robots represent the next stage of technological interaction after the keyboard and mobile phone revolutions. The company’s technical vision relies on integrating sensory perception, cognitive capabilities, and motor action into a unified system, allowing a robot to perform its tasks within everyday facilities with natural fluidity. This approach aligns with what Chang Min, Vice President at the Chinese company “Unentry”, noted, confirming that current industrial focus is on making advanced robots more accessible and practically scalable, viewing the Saudi market as one of the top destinations ready to absorb the new generation of smart robots.
The shift to real-world operation cannot be completed without computing platforms that withstand daily load and keep pace with the intensity of data flow.In this context, the American company “Supermicro” unveiled advanced computing environments designed to match accelerating workloads, with Mohamed Zahri, the company’s regional director, noting that scaling applications across facilities requires a flexible architecture capable of handling inference operations without bottlenecks. This direction is supported by direct regional investments aimed at building local capacity and training talent to operate modern data centers in line with Vision 2030 targets.
At the level of precise health applications, the American company “Saionic” introduced a vital artificial limb that relies on continuous learning from user movements. Dr. Adel Akhtar, the company’s chief executive officer, explained that the technology aims to make the prosthetic an integrated physical extension, revealing that motion data collected from users is leveraged in a parallel pathway to train robots and equip them with fine-grained movement skills, underscoring the convergence of health and robotic control sciences.
This technological maturity coincided with a governmental operational achievement announced by the Saudi Data and AI Authority (SDAIA) for the “Tawakkalna” platform, where 62 government entities were honored for fully automating the integration of their services to meet the 2026 targets. The platform presently serves over 36 million users, processing more than 85 million transactions daily, providing access to over 1,700 government services after consolidating 926 services into a single digital interface, reflecting the scale of the operational infrastructure managed by the national digital system.
This shift from experimentation to field deployment reshapes the technical priorities of teams and organizations in the region.The chief technology officer in companies, banks, and utility agencies across the Gulf, Egypt, and the Levant is no longer concerned solely with launching internal conversational models; they must now redesign computing architectures to accommodate physical automation systems and integrate services with comprehensive APIs. The next challenge will not be proving model efficiency, but ensuring hardware resilience in handling millions of concurrent transactions and preparing human staff to work alongside robots and smart equipment in the field.