Qwen Office Deployed at Changan Automobile, Used Across R&D, Production, Supply, Sales, and Service

No AI, No Changan

QwenWork News

Recently, Changan Automobile has officially introduced QwenWork across multiple business scenarios including R&D, manufacturing, supply chain, sales, and service.

As one of the three major state-owned enterprises in China's automotive industry, Changan Automobile is actively embracing AI technology trends, championing the slogan "No AI, No Changan." Previously, Changan successfully launched industry-leading products such as Tianshu Linghang and Tianshu Cabin. This year, the company has further brought the wave of AI innovation deep into its organizational structure, covering all business areas.

Liang Guangzhong, Assistant to the President and Head of Transformation & Efficiency at Changan Automobile, stated: "At Changan, intelligent agents like QwenWork are not just tools for enhancing individual productivity—they are key to building an AI-native organization. By embedding AI capabilities into the core of business processes, they drive a transformation in the overall operational model, making AI an indispensable infrastructure for the enterprise."

To this end, Changan has mandated that all employees use AI agents. QwenWork is now deeply integrated into frontline staff workflows, generating numerous innovative use cases across R&D, production, supply chain, sales, and service.

In R&D, Mao Haiyun, an electrical systems integration test engineer, used QwenWork to develop two business applications: a wiring harness selection calculator and a lighting effect simulator. Wiring harness selection involves calculating thousands of parameters per vehicle model—work that previously took senior engineers about two days manually. Now, results are generated within five minutes after uploading the parameter sheet. The lighting effect simulator renders lighting designs directly on 3D car models, replacing the previous practice of verbal descriptions or building physical demos through lengthy development cycles. This has reduced consensus-building time from several weeks to just one meeting.

In manufacturing, Guo Ziya, a quality system management engineer with 13 years of experience who had never written code before this year, began using AI and developed a QR-code-to-Chinese converter via QwenWork, solving the problem of production-line scanners failing to recognize Chinese characters. The tool is now widely deployed across Chang'an's Hebei facilities. Other internal staff have built a micro-application for checking vehicle documents—including certificates of conformity, CoC certificates, and environmental lists—automating comparisons across dozens of parameters. Where manual checks once took over ten minutes per vehicle, the process now takes only one to two minutes: users simply take a photo, upload it, and the system automatically extracts data, compares it against official records, and flags discrepancies.

Gong Xuan, Senior Project Manager for Digitalization at the Global Procurement Platform of the Purchasing Center, is even further ahead—using QwenWork as a core tool for daily data analysis, project reviews, and report writing, and currently building automated cost analysis functions based on enterprise databases.

In the supply chain, Zhang Yongze, a 55-year-old packaging manager in the logistics department, taught himself QwenWork and broke down the packaging approval process—which connects hundreds of suppliers nationwide and involves thousands of parts per project—into seven or eight automated scripts. These enable automatic data extraction, progress consolidation, and delay alerts, reducing his workload by over 90%.

In sales and service, each research project requires processing around 20,000 to 30,000 user samples. Classifying and analyzing these unstructured customer voices used to take days of manual effort. Now, with QwenWork, sentiment recognition and opinion clustering are done automatically, compressing the analysis cycle to under half a day and providing data-driven customer insights for product design.

Li Qiang, Vice President of Alibaba Cloud Intelligence Group and General Manager of AI in the Automotive Industry, said: "Looking ahead, industry competition will shift from 'smart vehicles' to 'smart enterprises.' What will differentiate automakers isn't who first adopts a model, but who can turn AI into a sustainable engine for evolution. Models will eventually become ubiquitous, and isolated applications easily replicated. What's truly hard to copy is whether an automaker can use AI to seamlessly connect the entire chain of R&D, production, supply, sales, and service. That’s precisely the示范 significance of QwenWork’s implementation at Changan."

Changan Automobile and Alibaba have a long-standing and fruitful partnership. The Changan Tianshu Linghang intelligent driving assistance system already uses Pingtouge’s self-developed AI chips for model training, and the Changan Tianshu Cabin has integrated the Qwen large model. Going forward, Changan and Alibaba will continue deepening their AI collaboration, jointly setting benchmarks for intelligence in the automotive industry.

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