As artificial intelligence moves beyond early experimentation in China, enterprises are entering a new phase in which scalability, governance and measurable business value are becoming central concerns, said a senior technology executive.

"Over the past two years, AI in China has gone from concepts to intensive pilot programs," said Liu Lifang, solutions engineering director at Cloudera Greater China, adding that whether it is large language models, agentic AI or automation applications, many enterprises have completed their initial exploration of such technology.

"Heading into 2026, AI is entering a new stage — moving from pilots to business-scale deployment," Liu said.

A report released this month by PwC also shows that AI adoption among Chinese enterprises has entered a phase of positive returns driven primarily by revenue growth. About 52 percent of surveyed Chinese CEOs said AI had boosted their revenues, far exceeding the global average of 29 percent.

As this transition unfolds, Liu noted that the questions enterprises are asking are also changing. "The core issue is no longer whether AI can be used, but whether it can run stably under controllable and sustainable conditions and be translated into measurable business outcomes."

Looking ahead in 2026, Liu said AI adoption in China is expected to accelerate its shift toward industrialized applications, with business value and replicability emerging as key benchmarks of success.

In sectors such as manufacturing, finance and telecommunications, companies are expected to prioritize reusing proven AI capabilities and embed them into core business processes through agent-based work-flows, rather than relying on single models or experimental projects, Liu explained.

"Enterprise AI applications will clearly move beyond chatbots and isolated tools," he said. "They will increasingly focus on process optimization, operational automation and industry-level intelligent applications."

As a result, indicators such as return on investment, efficiency gains and sustainable operations, will replace model parameters or computing scale as the primary measures of AI success.

Another major trend, Liu added, is the rising importance of trusted and governable private AI as a key differentiator for Chinese enterprises.

"In the China market, data security and regulatory compliance have always been prerequisites for AI adoption, and this will only be reinforced in 2026," he said.

While public cloud services and pre-trained models have significantly lowered the barrier to AI experimentation, Liu said many enterprises are realizing that inadequate data governance, access control and compliance mechanisms can magnify risks even as efficiency improves.

As a result, more Chinese companies are turning to private AI approaches, including deploying models in governed environments, ensuring data remains within defined domains with controlled access and full traceability, and using technologies such as retrieval-augmented generation to provide business context while keeping data under control.

"Trusted AI is no longer a best practice — it will become the basic threshold for enterprises seeking to scale AI," Liu said. "Governance and agility are not opposing choices, but two essential components of AI maturity."

According to Cloudera forecast, localized private deployment will become the foundational infrastructure for large-scale AI adoption in China.

As AI moves into production-level use, enterprises are increasingly focused on whether it can run continuously in private environments, evolve over time and reliably support core operations, Liu said.