From October 20 to 22, along the banks of Shanghai's Huangpu River, the 2026 Sustainable Global Leaders Conference will return. Nobel and Turing Award laureates will take the stage to discuss the future and trends of technology, and the internet-famous professor who "rescues" people from anxiety and involution—Michael Macfarlane—will engage in a live dialogue with Xiang Biao!
Limited spots available, first come first served. Let us join together for a sustainable future! [Click to Register] Recently, the Provincial Department of Economy and Information Technology announced the second batch of Hubei Province's 2026 artificial intelligence typical application scenarios. Eleven application scenarios from our city were successfully selected, tying for second place across the province.
In terms of categories, the selected scenarios from our city cover multiple fields including AI plus industrial manufacturing and AI plus education, involving smart factories, AI visual inspection, process parameter optimization, safety management, drone inspections, intelligent production and auxiliary testing, among other directions. These scenarios collectively demonstrate the results of deep integration between artificial intelligence and manufacturing, and also showcase innovative exploration in education, industrial services, and other fields.
In terms of geography, Shashi District accounts for 8 of the selected scenarios, coming respectively from Jianghan New Materials, Jiuling Technology, Saliente, Sairui Energy (2 scenarios), Hongsheng Technology, Hongye Knitting, and Jingzhou Mobile. This marks another concentrated breakthrough for Shashi District in the field of AI-empowered manufacturing, following the addition of 3 provincial-level artificial intelligence typical application scenarios in 2025. Let us take a look at Shashi District's specific practices in AI-empowered manufacturing.
Using policy services as support to tackle transformation challenges for small and medium-sized enterprises
Building an ecosystem featuring "government guidance, enterprise leadership, and service provider collaboration," and through a combination of measures including policy rewards and subsidies, digital infrastructure, and digital-intelligent transformation services, continuously lowering the threshold for transformation for small and medium-sized enterprises, providing institutional guarantees and resource support for district-wide digital-intelligent transformation. In recent years, Shashi District has thoroughly implemented the national "AI Plus" initiative and Hubei Province's "AI Plus Manufacturing" special action plan, using application scenario development as a key lever and continuously using "intelligent transformation and digital transition" as a powerful driver to help enterprises improve intelligent application scenario development, accumulating over 20 million yuan in policy funds secured from provincial, municipal, and district levels to support enterprise digital-intelligent transformation.
Using scenario needs as the driving force to precisely advance AI application implementation
Starting from the real pain points of enterprises, specific needs in production, quality inspection, supply chains, and other areas are transformed into implementable and quantifiable AI application lists. Through demand-oriented technology matching, artificial intelligence technology is ensured to find concrete footholds, enterprises can intuitively perceive transformation benefits, and a closed-loop upgrade chain of "demand—application—benefit" is formed. Jingzhou Sairui Energy Technology Co., Ltd. focuses on petroleum equipment manufacturing, targeting core pain points such as inspection precision and safety control. Relying on 5G network infrastructure and integrating industrial IoT, machine vision, and artificial intelligence technologies, it has built an integrated smart factory scenario centered on AI intelligent auxiliary testing and supported by production safety intelligent management and control. After the project is completed, the inspection time per product has been reduced from approximately 60 seconds to approximately 20 seconds, with inspection efficiency improved by about 67%; the missed detection rate has dropped from 4.2% to below 0.3%, with the false detection rate controlled within 1%; hazard identification accuracy reaches over 95%, with average warning response time of 5 minutes or less; the transactional workload of frontline management personnel has been reduced by 40%, and production and operational costs are expected to decrease by approximately 15%.
Using leading enterprises as demonstration models to accelerate collaborative transformation across the industrial chain
Giving play to the demonstration and driving role of industry-leading enterprises, promoting upstream and downstream small and medium-sized enterprises to simultaneously implement digital-intelligent transformation through standardized solution output and platform sharing. Relying on the mature experience of industry benchmark enterprises, forming replicable transformation paths, and expanding the breakthrough achievements of a single enterprise into cluster-style upgrades across the entire industrial chain. Jingzhou Hongye Knitting Garment Co., Ltd., addressing problems in traditional data analysis such as data silos, delayed response, and high labor costs, has built an AI data analysis system that automatically connects with MES, ERP, and major e-commerce platforms, establishes a unified data warehouse, and achieves standardized cleaning and integration of multi-source data. The system features trend analysis, product performance analysis, user behavior analysis, inventory optimization suggestions, anomaly detection, and other functions, and automatically generates visualized dashboards and intelligent reports. It is expected to improve data analysis efficiency by over 80%, shorten report generation time from hours to minutes, increase inventory turnover rate by 15% to 20%, achieve prediction accuracy of over 85%, and reduce the data processing workload of operational personnel by 60%. This scenario has been recognized as a technology-leading "chain-network collaboration" typical case in the textile and apparel industry, with strong value for industry-wide promotion.
Next, the Jingzhou Municipal Bureau of Economy and Information Technology will take this selection as an opportunity to fully leverage the demonstration and driving role of typical scenarios, increase efforts to publicize and promote outstanding cases, continue to explore more high-value artificial intelligence application scenarios, promote the deep integration of artificial intelligence technology with the real economy, and support the high-quality development of our city's new industrialization.
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