Nano Cases on AI and Organizations
Gong Jie, Jin Li and Aiken Yuen. "Scaling Managers with AI at KFC China." Nano Case. HKU Centre for AI, Management and Organization, January 2026.
Summary: KFC is the leading quick-service restaurant chain in China. Its Restaurant General Managers (RGMs) are the backbone of store performance, but the job had drifted: forecasting, scheduling, ordering, and reporting had pulled managers into the back office and away from the floor. KFC deployed an AI toolkit that absorbed the routine planning work and freed managerial attention for judgment and leadership. The company then widened spans of control, letting experienced RGMs oversee multiple stores rather than one.
Gong Jie, Jin Li, Lilian Xu and Leila Zhang. "The C-Smart Agent: A New Service Model at Yum China." Nano Case. HKU Centre for AI, Management and Organization, February 2026.
Summary: Yum China, the largest restaurant company in China, processes over 60 million customer contacts per year. Between 2018 and 2025, contact volume grew six-fold while the service team got leaner. The company built C-Smart, an AI agent that now handles roughly 90% of contacts end-to-end, routing only complex and emotional cases to human agents. It broke the link between contact volume and headcount, and turned a support centre into an intelligence hub.
Gong, Jie and Lu Xiao. "Compressing Coordination with AI at Decathlon China." Nano Case. HKU Centre for AI, Management and Organization, March 2026.
Summary: AI can accelerate the execution of individual tasks, but it cannot improve an organization’s overall efficiency. After Decathlon China deployed AI in its software development process, coding efficiency increased by 20 to 40 percent, while overall delivery speed increased by only 10 to 15 percent. This gap forced the company to rethink its time allocation and revealed that the real bottleneck was not technical execution, but coordination.
Gong, Jie, Augustus Pan, and Harry Zhang. "Scaling Expert Knowledge with AI at CIMC." Nano Case. HKU Centre for AI, Management and Organization, March 2026.
Summary: In manufacturing, expertise often resides not in systems, but in people’s minds—embedded in experience, judgment, and informal processes that are difficult to transfer or scale. A subsidiary of CIMC used AI agents to interpret complex and unique engineering drawings that previously required manual interpretation by experienced team leaders. The result was that the processes that would have taken 18 dedicated team members more than two days could now be completed in less than one day, with an accuracy rate of 95%. This freed team leaders to get back to their real work—managing and producing on-site personnel.
Gong, Jie, Jin Li, and Elmer Liu. "AI Didn't Replace the Radiologist. It Replaced the Junior One." Nano Case. HKU Centre for AI, Management and Organization, June 2026.
Summary: In 2016, Geoffrey Hinton predicted that deep learning would make radiologists obsolete within a decade. Yet, a recent investigation published in 2025 in The New York Times reveals that the Mayo Clinic has increased its radiology staff by 55% in the past decade. In contrast, at iKang Healthcare, the deployment of AI technology led to a reduction in the number of radiologists by changing how image-reading work was divided. iKang runs more than 150 preventive medicine branches across China and interprets 7–8 million medical images per year. By using AI for first-round image reading and centralizing the remaining review into 16 regional centers with experienced specialists, it reduced the number of radiologists from 400 to 160 while maintaining the image volume and reading accuracy. AI did not make radiologists disappear. It changed which kind of radiologists the company needed.
Gong, Jie, Jin Li, Aofei Chen, and Kui Wang. "Tapread: From R&D Queue to Business Flow." Nano Case. HKU Centre for AI, Management and Organization, June 2026.
Summary: Many fast-moving companies have faced a common challenge: every request, no matter how large or small, is submitted to a single queue and processed one after the other. Although the funnel helps maintain order, it slows down the team. Tapread’s overseas technology team employed AI to recast this as a portfolio problem. It categorized them by risk and gave each type its own delivery path. Product and operations personnel used AI to develop small, low-risk tools; engineers worked with AI to implement mid-sized projects across technical stacks; and core systems stayed on the full engineering process, kept off the lighter paths. Monthly output increased from 5–8 items to 20–25 without increasing manpower.