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As China turns applied AI into an engine of productivity and disruption, business leaders face high-stakes choices about which use cases and capabilities to back to stay competitive.
China is running a different AI race. While firms in the U.S. and Europe are investing heavily in frontier models alongside enterprise deployment, China is taking an application-first approach, embedding practical AI into the office platforms that power day-to-day work. Chinese executives returned the highest overall score in this year’s AlixPartners Disruption Index, yet 90% report feeling optimistic about AI’s impact, versus a global average of 80%, pointing to a market where disruption pressures may even be reinforcing leaders’ belief in AI as a way through the turbulence.
For leaders, the question is simple: where does China’s tightly integrated AI ecosystem truly outperform, and which applied-AI use cases are big enough—and repeatable enough—to justify putting serious capital to work, in a world where roughly 80% of AI projects still fail to scale?
AI on the ground in China
From sandbox to the centre of office life
Across most regions, recent global surveys show AI still stuck in pilot mode. AlixPartners’ 2026 Disruption Index reveals China as an outlier, especially in office automation (OA): about 38% of job functions at Chinese companies are already fully integrated with AI tools today, compared with 30% globally. Platforms like Alibaba’s DingTalk and Bytedance’s Feishu act as the operating system for office work—not standalone apps that employees dip in and out of. AI is ambient, not separate: woven into familiar interfaces without employees consciously “turning on” an AI feature.
The race to own the office is still in land-grab phase, with Feishu and DingTalk competing aggressively to lock in enterprise users. At the same time, the viral rise of OpenClaw—an open-source autonomous agent framework that has been the fastest to take off in China—shows how quickly AI can jump from experiment to mass adoption.
Its impact shows up less in showpiece demos and more in the texture of everyday work: how information flows, how quickly approvals move, how consistently teams follow standardized processes. These shifts may not make headlines, but this is where AI stops being theatre and starts to deliver real productivity and coordination gains.
A domestic ecosystem with emerging global reach
All of this runs on a technology stack that looks very different from ERP- and CRM-centric set-ups common elsewhere. Under stringent data-sovereignty rules and with cloud, models, and automation designed to work together, Chinese enterprises favor homegrown LLMs like Qwen (Alibaba), Ernie (Baidu) and GLM (Zhipu), wired into domestic clouds and software platforms as one tightly integrated system, rather than a patchwork of bolt-ons.
This domestically-anchored set-up means providers expand overseas selectively, starting with sectors and markets whose data rules and regulatory culture look most like China’s. Banking and telecoms already favor onshore or hybrid infrastructure; fast-growing firms in Southeast Asia and the Middle East operate on more greenfield and lightly built-out stacks and are more open to cloud platforms and mobile-first, chat-centric office tools, making them natural first ports of call for the integrated OA model. High-profile sales such as Meta’s purchase of Manus—a Chinese-founded, Singapore-based AI agent start-up acquired for over $2 billion in February—reveal what’s possible when solutions are built for global customers and regulators from the outset. The underlying LLMs are typically international rather than domestic, but data protection, security, and on-premises or local infrastructure remain central to the pitch.
Scaling through efficient AI deployment
Rather than chasing ever-growing, hardware-hungry models, Chinese firms focus on efficient deployment: trimming and redesigning models so they need less computing power, storage and memory. DeepSeek is a prominent example: its main innovation is compressing large models so they run on leaner hardware and slot into existing infrastructure—no large data centres are required. That shifts the race away from building the largest “frontier-scale” systems, and towards making powerful models affordable and easy to run at point of use.
From models to machines: China’s applied AI bench
That real-world focus is reflected in talent as much as products. China’s AI race is being led by builders, not lab theorists. Years spent scaling platforms for Alibaba, ByteDance, and Baidu have produced a large pool of engineers and data specialists used to handling large systems, pipelines and rapid iteration. Instead of clustering in a few frontier-model labs, this talent is mostly in applied engineering—fine-tuning models, wiring them into live products, and building domain-specific agents.
The same builder mindset is powering a surge in AI-powered robotics, with models and algorithms tightly coupled to hardware and rolled out at scale in logistics and factory settings. Paired with China’s strengths in manufacturing, supply chains, and device production, this shortens the loop from design to deployment, and gives Chinese firms an edge in turning AI pilots into working systems.
What does this mean for AI decision-makers in Chinese firms?
- Use office automation and workflow as the primary platform for AI-driven work redesign.
Even when AI is embedded in mainstream office suites and collaboration tools, the real investment is in change work: re-engineering workflows, cleaning and connecting data, training users, and monitoring performance. If those investments don’t move the metrics, treat that as a signal to rethink or retire the use case, not a reason to defend sunk costs.
- Focus AI bets where China has an edge.
Prioritize use cases that run on domestic LLMs and onshore AI infrastructure in data-rich, repeatable workflows, such as operations, support, and knowledge retrieval. Anchor efficiency plays on compressed models, hybrid on-premises and cloud architectures, and comparatively standalone business processes that can run economically on existing infrastructure. Use applied AI talent to design proprietary workflows, fine-tune domestic LLMs on customer, operations, and domain data, and develop internal models or agents tailored to specific business problems.
- Use this cycle to build an AI-ready enterprise architecture.
Move from fragmented, siloed systems to AI-native architecture, rather than layering new tools onto legacy systems. Standardize shared data and model platforms so new AI use cases can be deployed and scaled consistently, without being held back by integration work.
- Above all, build an AI-forward operating model.
For many Chinese enterprises, the next step is to turn isolated wins into a single, coherent way of running the business: a clear blueprint for processes, government and technology that lets AI deliver impact at scale, not just in pilots. That AI-forward operating model is what will keep them competitive with Western peers: advantage will come less from owning the biggest models and more from using today’s models to materially improve how work gets done.
Ready to move beyond pilots and wire AI into how you deliver results, not just manage workloads? AlixPartners is helping leadership teams make that shift. Talk to us
