At the 2026 Lean Digital Innovation Conference held from September 5 to 7 at the National Exhibition and Convention Center in Tianjin, Deng Zhiguo, CEO of Guangdong Yuantu Future Technology Co., Ltd., delivered a keynote address. He articulated that artificial intelligence signifies an industrial revolution, one that must inevitably manifest in productivity gains across the entire value-creation chain.

From this perspective, Deng posited that the most significant industrial opportunity of the coming half-decade is no longer creating AI models but rather deploying and applying AI solutions. Global AI spending is projected to hit $2.59 trillion in 2026, with data infrastructure investment alone reaching $1.4 trillion. China has already achieved a comparable scale of 1.2 trillion yuan in 2025, underscoring that AI technology has matured into a trillion-dollar market. The cost of computing power and inference has plummeted by a factor of 280 over the past two years, dramatically lowering the barrier to AI adoption. Concurrently, China's open-source models have made substantial contributions to this cost reduction.

Deng outlined that AI's industrial impact will transmit through four distinct layers in the coming years. First, at the computing infrastructure tier, investment will sustain rapid growth for the next three to five years, albeit with a pivotal shift from large model training to inference. Over the next two years, inference investment is expected to be two to three times that of model training, spurred by an explosion in enterprise applications and a corresponding demand for privatized computing deployments.

Second, at the model layer, the sharp decline in inference and computing costs is set to accelerate further. As raw computing power, model capabilities, and tuning precision continue to improve, the expense associated with model utilization will diminish even more drastically.

Third, at the application layer, the next few years will witness a transition from rudimentary consumer and office applications toward deep integration within core enterprise business processes, ultimately generating tangible productivity value.

Fourth, and perhaps most critically, manufacturing stands as the industry most profoundly bridging the physical and digital worlds. The evolution of physical models and embodied robotics will introduce immense incremental growth opportunities to this sector.

While confident that this AI transformation is inevitable, Deng emphasized five strategic principles for successful execution. The first is to persist with an industry-plus-AI approach, beginning by understanding specific industry pain points and business scenarios. AI must be embedded into primary business flows to generate value, fundamentally reconfiguring these processes around AI capabilities rather than simply retrofitting AI onto existing workflows.

The second principle calls for unwavering leadership from top executives, focusing on high-value pain points with the ultimate goal of delivering both customer and commercial value. Third, Deng advocates for prioritizing data infrastructure and enterprise ontology, initiating from single-point interventions in the value stream to create small closed loops that eventually expand into comprehensive, enterprise-wide big-loop transformations.

Fourth, organizations should adopt a hybrid path that combines proprietary research with an open ecosystem approach. Finally, persistent effort must be directed toward restructuring talent and organizational frameworks to foster seamless human-machine collaboration.