On March 11, IBM held a media and analyst briefing in Beijing, where artificial intelligence emerged as a central topic of discussion. Chen Xudong, Chairman and General Manager of IBM Greater China Group, responded to recent market concerns following Anthropic's launch of Claude Code, which triggered significant stock price declines for many software companies. He stated that the market has underestimated the complexity of enterprise-level applications, clarifying that IT modernization is not merely about rewriting code or upgrading systems. Instead, it represents a comprehensive systematic project encompassing application modernization, infrastructure modernization, data and technology stack modernization, and organizational and process modernization.
Hou Miao, General Manager of IBM China Technology Unit, concurrently pointed out that IBM's stock fluctuations were largely attributable to a "news-driven effect." He indicated that the company's valuation is currently undergoing a correction and that there are "no unified, fundamental challenges observed." Contrary to the perception that software companies face inevitable decline in the AI era, he emphasized that software firms are actually the quickest to adopt and most extensive in applying AI technologies. AI is not only used to enhance products but also effectively improves internal operational efficiency, freeing up human resources to explore new business lines. It is reported that AI has already helped IBM save over $4.5 billion in annualized operational costs and has been adapted to more than 70 application scenarios.
"These validated pathways have also become crucial experiences for us and our clients in jointly advancing the large-scale application of AI," said Zhai Feng, Chief Technology Officer of IBM Greater China Group. With the advancement of artificial intelligence, IBM's business strategy in the Chinese market has become more defined. Zhai Feng remarked, "Nearly 30 strategic acquisitions by IBM since Red Hat have almost exclusively focused on the infrastructure layer, consistently concentrating on 'building the road'—integrating underlying architectures and establishing open standards and technological empowerment—rather than 'manufacturing the cars'—developing large models. The more prosperous AI applications become, the more critical the underlying technical architecture will be."
He believes that in the future, enterprises may need to manage tens of thousands of intelligent agents. IBM aims to assist companies in establishing a unified mechanism for deploying and managing AI applications in the new AI era, ensuring they maintain choice and control over their data, technology, and operations. IBM also provided recommendations for how ordinary enterprises should respond to the AI era. Chen Kedian, President of IBM Consulting Greater China Group and Korea, stated that AI-driven rapid decision-making and an operational model combining resilience and agility are becoming key to reshaping enterprise efficiency and long-term competitiveness.
"Small-scale Proof of Concept (POC) projects are unlikely to deliver scalable value; AI must be advanced as a longer-term, more systematic strategy," he said. "AI is transitioning from isolated experiments to enterprise-level engineering. Its competitiveness does not stem from the models themselves but from the synergistic development of infrastructure, data governance, and organizational capabilities. Without a sound technical architecture, data system, and organizational capacity, it is difficult for AI to achieve scale effects."
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