Blab AI
AI Moves from Lab to Supply Chains, but Practical Hurdles Remain
At the fourth China International Supply Chain Expo (CISCE) in Beijing, artificial intelligence stepped out of the laboratory and into the heart of industry. From 22 to 26 June 2026, the event showcased how AI is reshaping manufacturing, logistics, and health services, yet it also highlighted the stubborn obstacles that keep many firms from realizing the full promise of the technology.
CGTN reporter Dai Kaiyi moderated a panel that brought together executives and experts who agreed on one point: AI has matured, but the “last mile” of deployment remains difficult. The discussion underscored the benefits that AI can deliver in real‑world settings—improved efficiency, better decision‑making, and higher quality—while also cataloguing the barriers that prevent widespread adoption. Data silos, platform incompatibility, and high deployment costs were the most frequently cited themes. Nevertheless, the conversation suggested a gradual easing of these challenges as companies invest in digital infrastructure.
A compelling illustration of this trend came from Schneider Electric’s overhaul of a 30‑year‑old factory in Shanghai. XIA XUEYING, Vice President of Corporate Affairs & Sustainable Development for Schneider Electric China, explained that the company’s AI initiatives span research, manufacturing, and operations. Machine‑learning models have sharpened product performance, while AI‑driven production scheduling now lets the plant accommodate a broader product mix and smaller batch sizes with greater efficiency. Schneider Electric, a French multinational that reported €38.15 billion in revenue in fiscal 2024, has long positioned itself as a leader in energy technology and industrial automation.
In the logistics arena, French cosmetics giant L’Oréal leveraged AI to streamline its supply chain. LAN ZHENZHEN, President of Public Affairs for L’Oréal North Asia and China, described the company’s Suzhou Smart Fulfillment Center, which opened in 2024. The center can process up to 7,000 parcels per hour, and during peak shopping events it sorts and fulfills 99 percent of orders within 48 hours. AI‑powered solutions, combined with collaboration between Chinese and French technology partners, have reportedly delivered strong performance across all stages of the beauty supply chain.
The healthcare sector also turned to AI to knit together disparate data sources. HUANG HUI, Chief Omics Consultant and Genomics Researcher at BGI Genomics, noted that the company’s new AI platform aggregates multiple types of health data. The system helps users understand health risks and manage conditions more effectively. BGI Genomics, a Chinese genomics firm that went public in 2017, has a history of large‑scale sequencing projects and has recently expanded into precision health management.
Despite these successes, the panel acknowledged that AI has not yet fully bridged the gap between demonstration and sustained value. Data often remains confined to separate systems, and many businesses cite deployment costs as a deterrent. Yet the presence of companies such as Schneider Electric, L’Oréal, and BGI Genomics at the expo signals a gradual shift toward practical AI applications.
CISCE organizers, which attract exhibitors from 85 countries, framed the event around themes such as advanced manufacturing, clean energy, and digital technology. The expo’s focus on collaboration and integration reflects a broader industry trend toward end‑to‑end AI solutions that can operate across diverse platforms.
In sum, AI is increasingly moving beyond laboratory prototypes into operational environments, but the transition is uneven. Companies that have invested early in digital infrastructure and data integration are reaping tangible benefits, while others still contend with technical and financial barriers. The next steps for the industry will involve scaling AI solutions, reducing costs, and ensuring that data can flow freely across legacy and modern systems.
CGTN reporter Dai Kaiyi moderated a panel that brought together executives and experts who agreed on one point: AI has matured, but the “last mile” of deployment remains difficult. The discussion underscored the benefits that AI can deliver in real‑world settings—improved efficiency, better decision‑making, and higher quality—while also cataloguing the barriers that prevent widespread adoption. Data silos, platform incompatibility, and high deployment costs were the most frequently cited themes. Nevertheless, the conversation suggested a gradual easing of these challenges as companies invest in digital infrastructure.
A compelling illustration of this trend came from Schneider Electric’s overhaul of a 30‑year‑old factory in Shanghai. XIA XUEYING, Vice President of Corporate Affairs & Sustainable Development for Schneider Electric China, explained that the company’s AI initiatives span research, manufacturing, and operations. Machine‑learning models have sharpened product performance, while AI‑driven production scheduling now lets the plant accommodate a broader product mix and smaller batch sizes with greater efficiency. Schneider Electric, a French multinational that reported €38.15 billion in revenue in fiscal 2024, has long positioned itself as a leader in energy technology and industrial automation.
In the logistics arena, French cosmetics giant L’Oréal leveraged AI to streamline its supply chain. LAN ZHENZHEN, President of Public Affairs for L’Oréal North Asia and China, described the company’s Suzhou Smart Fulfillment Center, which opened in 2024. The center can process up to 7,000 parcels per hour, and during peak shopping events it sorts and fulfills 99 percent of orders within 48 hours. AI‑powered solutions, combined with collaboration between Chinese and French technology partners, have reportedly delivered strong performance across all stages of the beauty supply chain.
The healthcare sector also turned to AI to knit together disparate data sources. HUANG HUI, Chief Omics Consultant and Genomics Researcher at BGI Genomics, noted that the company’s new AI platform aggregates multiple types of health data. The system helps users understand health risks and manage conditions more effectively. BGI Genomics, a Chinese genomics firm that went public in 2017, has a history of large‑scale sequencing projects and has recently expanded into precision health management.
Despite these successes, the panel acknowledged that AI has not yet fully bridged the gap between demonstration and sustained value. Data often remains confined to separate systems, and many businesses cite deployment costs as a deterrent. Yet the presence of companies such as Schneider Electric, L’Oréal, and BGI Genomics at the expo signals a gradual shift toward practical AI applications.
CISCE organizers, which attract exhibitors from 85 countries, framed the event around themes such as advanced manufacturing, clean energy, and digital technology. The expo’s focus on collaboration and integration reflects a broader industry trend toward end‑to‑end AI solutions that can operate across diverse platforms.
In sum, AI is increasingly moving beyond laboratory prototypes into operational environments, but the transition is uneven. Companies that have invested early in digital infrastructure and data integration are reaping tangible benefits, while others still contend with technical and financial barriers. The next steps for the industry will involve scaling AI solutions, reducing costs, and ensuring that data can flow freely across legacy and modern systems.
