A Panoramic View of AI Commercialization: Short-Term Efficiency Gains, Long-Term Ecosystem Transformation

Algorithms replace human brain decision-making, data-driven value creation, and the global AI enterprise-level market of $153 billion is reshaping the underlying logic of the business world.

In early 2025, the Dongguan Municipal Government of China invested 50 million yuan to issue "computing power coupons" to support small and medium-sized enterprises to rent AI computing resources; almost at the same time, the US Congress passed the AI ​​Infrastructure Act, planning to invest US$100 billion in the next five years to build a national AI R&D center. Under the policy dividends, the global AI market size is running at a compound annual growth rate of over 40%, and is expected to exceed US$1.5 trillion this year.

Under the superficial fanaticism, business practices show uneven heat and cold. Enterprise-grade AI software spending is expected to reach $153 billion in 2028, and an Accenture survey shows that nearly 40% of AI projects have been terminated due to the inability to verify ROI. The gap between technology and business has become the core challenge for the full implementation of AI.

01 Current situation and characteristics, the triple turning point of AI commercialization

AI technology is undergoing a key transformation from laboratory to industrialization. According to the Stanford University's "2025 AI Index Report", the penetration rate of AI technology in the enterprise-level market has soared from 16% in 2020 to 54% in 2024. Behind this leap is the joint force of the three major structural changes.

The reasoning cost revolution has become the fuse for the outbreak of applications. The new generation of open source models represented by DeepSeek-R1 reduces the inference cost to 1/30 of the GPT-4 era. The sharp decline in costs has made it possible for small and medium-sized enterprises to deploy AI on a large scale.

The focus of technical value is moving from the underlying computing power to the upper application. The industrial value chain presents a clear pyramid structure: Nvidia, TSMC and others occupy the underlying computing power infrastructure; Baidu, SenseTime and others provide general technical capabilities; and the fastest growing value are application service providers that go deep into vertical scenarios.

The global AI commercialization path shows obvious regional differences. Chinese companies focus on scenario-driven rapid implementation, such as WeBank's federal learning model, which has reduced the bad debt rate of small and micro enterprises' loans from 3.5% to 1.8%; European and American companies pay more attention to building a technology ecosystem, such as Microsoft's Azure AI's hierarchical service system.

"AI is experiencing a key turning point from 'technical verification' to 'scale monetization'**." Analysts at Tianfeng Securities pointed out in their latest report that "the biggest opportunity in 2025 belongs to those vertical users who understand both technology and business pain points."

02 Scenarios are implemented, and certain opportunities driven by urgent needs of efficiency

When the technology boom encounters business reality, some fields have already run a clear business closed loop. These high-deterministic tracks show common characteristics: quantifying ROI, solving industry pain points, and adapting to existing workflows.

Medical imaging diagnosis has become a benchmark scenario for AI implementation. Tencent Miying's lung nodule recognition system has a sensitivity of 99.2%, shortening the CT reading time from 15 minutes to 30 seconds, and increasing the emergency response speed by 30 times. The hospital's labor costs have been reduced by 40% and the equipment utilization rate has increased by 25%, providing a solid business logic for AI payment.

AI quality inspection in the manufacturing industry has created an even more amazing efficiency improvement. Industrial Fulian’s intelligent quality inspection system achieves a missed detection rate of less than 0.001% on the iPhone production line, which is 20 times higher than the artificial quality inspection efficiency. In Dongguan’s “AI+Manufacturing” plan, 32 application scenarios are expected to create economic value of over 50 billion yuan.

In the enterprise-level service field, AI Agent is reconstructing workflows. Agentforce launched by Salesforce adopts a pay-per-view model (USD 2 per time), and has independently processed 380,000 service requests, with a resolution rate of 84%. The ROI calculator it developed clearly displays three types of value: reduced customer service costs, increased sales transaction rates, and automation of customer development.

Educational personalization is the highlight of C-end commercialization. Good Future’s “MathGPT” can automatically generate 100,000+ differentiated exercises, and teachers’ lesson preparation efficiency is 6 times higher; Squirrel AI’s “MCM system” breaks traditional score evaluation and accurately locates learning disabilities. The improvement in technology-driven effects is directly transformed into users' willingness to pay - the online language learning market is expected to exceed US$16 billion this year.

03 In the enterprise-level market, the commercialization path of layered penetration

Faced with the huge differences in corporate demand, overseas manufacturers have differentiated into three major commercialization paths, forming a strategic pattern of layered penetration.

The high-end market is dominated by independent product models. Salesforce's Agentforce and Freshworks' Freddy AI Agent package AI capabilities into standardized products that directly measure ROI. The success of this model depends on value quantifiability—when companies can clearly calculate the labor costs saved by AI tools or the additional revenue generated, the willingness to pay is greatly increased.

The mid-range market is more suitable for product enhancement strategies. SAP deeply integrates Business AI into core processes such as cloud ERP and finance, supply chain, etc., and improves operational efficiency by 30% through natural language interaction. This embedded AI design lowers the barrier to adoption and is particularly effective in areas such as high replacement costs such as ERP.

For small and medium-sized enterprises, penetration is a powerful tool to open up the market. ServiceNow clearly stated that it does not provide independent charges at this stage, but accelerates customer penetration by giving up early revenue. This strategy recognizes that there is a "network effect" in AI value - the more customers are used, the better the model performs, forming a positive cycle.

"Sacrificing income in exchange for long-term barriers is the wisdom of ToB AI." ServiceNow product manager explained in an investor meeting, "When AI is deeply integrated into the enterprise workflow and proves its value, commercialization will come naturally."

The differentiation of the Chinese market lies in the coordination of policies and ecology. Dongguan’s “computing power coupons” can cover 50% of the AI ​​investment costs of enterprises; the Ministry of Industry and Information Technology’s “AI+” Action Plan” proposes to build 100 intelligent manufacturing demonstration factories by 2027. Under the policy dividends, service providers that can help small and medium-sized enterprises enjoy the dividends will gain unique advantages.

04 Hardware revolution, a new carrier for the integration of end-side AI and multimodal

When algorithm breakthroughs encounter hardware innovation, consumer electronics is undergoing paradigm changes that have not been seen in ten years. Edge AI chip shipments are expected to grow by 20% in 2024, pushing the shift of AI processing center of gravity to the end side.

Wearable devices become a super entrance to health management. Smart watches analyze physiological data through AI algorithms to achieve disease risk warning; brain-computer interfaces explore the fusion of carbon-based and silicon-based systems, which may reshape human-computer interaction logic for a long time. These devices are building a healthy closed loop of "monitoring-analysis-intervention" to bring preventive medicine into the era of popularization.

Multimodal interactive devices redefine the human-computer interface. The space computing power equipped by Apple's Vision Pro and Huawei Mate70 combine 3D modeling and visual algorithms to open up new scenarios in low-altitude economy and other fields. The end-cloud convergence architecture balances performance and privacy - the new generation of chips improves the end-side computing power, and the cloud supplements complex reasoning to achieve both experience and security.

Embodied AI, as the physical embodiment of AI, has moved from a laboratory to a production line. Eston's "AI Teaching System" reduces the programming time of industrial robots from 8 hours to 30 minutes. In 2025, the humanoid robot industry chain will accelerate maturity, and the efficiency improvement of Tesla's Optimus production line has confirmed the feasibility of replacing "silicon-based labor" in the manufacturing industry.

The rise of the World Model allows hardware to truly "understand" the physical world. This type of model can grasp the logic of physical cognition and causal relationship, carry out counterfactual reasoning, and provide decision-making capabilities in complex environments for autonomous driving, robots, etc. In the medical field, multimodal AI helps doctors to make more comprehensive diagnosis by integrating imaging, medical records, and pathological data.

05 Challenges and responses, the key game in industrial transformation

When AI penetrates into core businesses, a series of deep challenges emerge. Technology maturity, ethical compliance, and employment reconstruction constitute the triple door on the road of commercialization.

Technical reliability remains the biggest obstacle. Although the concept of AI pharmaceuticals is very popular, the clinical success rate has only increased from 10% to 20-30%; in the manufacturing industry, the multimodal and high noise characteristics of industrial data make it difficult for general algorithms to compete in professional scenarios. The market is voting with its feet - 40% of AI projects have been terminated due to the inability to verify ROI, highlighting the risk of hype about the concept of "AI for AI".

Ethics and supervision build a second line of defense. The EU's AI Act implements mandatory certification for high-risk systems; China's Interim Measures for the Management of Generative Artificial Intelligence Services clearly defines the legality requirements for data sources. In the medical field, although the FDA approved 223 AI medical devices in 2024, the access standards are becoming increasingly strict.

The transformation of employment structure requires a systematic plan. McKinsey predicts that AI will replace 800 million jobs worldwide by 2030, but create 95 million new jobs at the same time. The practice of Germany's manufacturing industry points out the way out: through human-machine collaboration, employees can focus on high-value tasks and increase production efficiency by 50%.

"AI is not a job killer, but a catalyst for skill upgrades." McKinsey Global Research Institute emphasized in its latest report, "Enterprises need to rebuild their capabilities models and use AI literacy as the core talent standard."

For small and medium-sized enterprises, ecological empowerment has become the key to breaking the situation. Alibaba Cloud’s “ET Industrial Brain” provides solutions such as AI quality inspection and energy consumption optimization, reducing the cost of enterprise transformation by 60%; Salesforce AI CRM helps small and medium-sized enterprises increase the conversion rate of customer in small and medium-sized enterprises by 35%. These platform solutions are bridging the technological gap and achieving democratization of AI.

06 Future prospects, from tool revolution to ecological competition

As we pass through the current technological fanatic period, the end of AI commercialization is gradually clear: look at efficiency tools in the short term, scene reconstruction in the medium term, and ecological competition in the long term.

2025 has become the symbol of the first year of Agent, and AI assistants have shifted from passive response to active decision-making. OpenAI's o1 and o3 models promote the implementation of Agents with the ability to solve complex problems and make decisions. These "super assistants" will reshape the human-machine relationship. Coding Agent further lowers the programming threshold, and is expected to increase software development efficiency by more than ten times this year.

The phenomenon of "single entrepreneur" rewrites innovative rules. Individuals use the AI ​​Agent team to verify business creativity at extremely low cost. This model has prompted the investment industry to move towards rapid decision-making - shortened product construction cycles have caused trial and error costs to plummet. The traditional enterprise organizational structure has therefore changed in a more flexible and open direction.

The end of AI commercialization is ecological competition rather than technological competition. Amazon's practice reveals a complete closed loop: internal development of Project Amelia to improve seller efficiency, and externally provide full-stack AI services through AWS. Its Amazon Q Assistant has saved $260 million internally, creating a flywheel from technical verification to commercial promotion.

"The winners of the future belong to those companies that can build AI value networks," said Microsoft's Dr. Zhang Qi in his latest trend forecast. "The triangular resonance of technology, scenarios, and capital will determine the depth of commercialization."

The changes in a garment factory in Dongguan reveal the essence of this revolution: through the AI ​​visual quality inspection system, the defective rate dropped by 80%; using federal learning to share industry data, the new product development cycle was shortened by half; workers were liberated from mechanical work and transformed into AI trainers.

The real value of AI is not to replace humans, but to release the creativity of human beings. When Weining Health's medical AI covers 98% of the disease standards, and when DeepSeek-R1 provides GPT-4 level capabilities at 1/30 cost, the curtain of technology inclusiveness is slowly opening.

The rules of the business world have been rewritten. Those companies that master algorithms and are well versed in the pain points of the industry are quietly building a moat for the new era. In the next decade, the global business territory will be redefined around the triangular relationship between data, algorithms and scenarios.