China’s AI market has become an execution challenge, not just a strategy topic. In the China AI Playbook, we publish a seven-chapter series on the bottlenecks, enablers, and management decisions that determine whether AI remains stuck in pilots or becomes a real source of operating advantage in China. Developed with VDMA and Ming Labs, the series is built for senior business leaders who need a clearer path on governance, value proof, platform readiness, adoption, talent, and ecosystem choices in China’s rapidly evolving AI landscape.
An overview chapter introducing the China AI Adoption Path: three bottlenecks that commonly block progress and three enablers that turn pilots into scaled results. It frames AI in China as an execution discipline and outlines the six leadership decisions that need to be locked before AI can scale.
Why many China AI programs slow down not because risk is too high, but because approval logic is too unclear and decision latency becomes the real barrier. Covers fast-lane governance, explicit decision ownership, standardized evidence packs, and contracting rules that keep deployment fast, consistent, and auditable.
A practical guide to moving from pilot activity to finance-grade evidence. Covers peer-validated prioritization, baseline and KPI design, scale-ready business cases, and explicit stop criteria so weak pilots do not consume budget and trust indefinitely.
Why many organizations can prototype AI but still cannot run it at scale. Covers reference architectures, shared platform capabilities, secure foundations, LLMOps discipline, and building just enough platform in parallel with priority use cases instead of treating platform building as a detached program.
An execution view of adoption: AI only creates value when it becomes part of how work is done and is managed through a clear performance cadence. Covers workflow embedding, trust and enablement rules, telemetry and feedback loops, and scaling use based on measured outcomes rather than activity alone.
A practical view of the roles, operating routines, and supporting capabilities needed to sustain AI delivery in China. Focuses on day-one must-haves such as evaluation, LLMOps, FinOps, and reusable integrations, as well as operating roles that connect business needs to safe, measurable AI deployment.
How to use China's fast-moving AI ecosystem without letting vendor, model, and channel choices fragment the operating stack. Covers portability, benchmark-led vendor comparison, local workflow rails such as WeCom, DingTalk, and Feishu, and the FinOps discipline needed to keep scaling affordable.
Download the overview chapter to understand the China AI Adoption Path - the bottlenecks, enablers, and six leadership decisions required to move from pilot to scale.
Download Chapter 1 PDF
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