At a recent media briefing for the second quarter of 2026, the Chief Technology Officer for Greater China at IBM (NYSE: IBM) provided a comprehensive overview of the company's latest insights and practical approaches regarding the implementation of enterprise-level artificial intelligence.
Against the backdrop of increasing pressure for growth and efficiency coupled with the complexities of globalization, Chinese enterprises aiming to transform AI into a capability for sustainable growth must ascend a three-tiered progression. The first step involves laying a solid foundation for data and IT infrastructure to support the scaling and global expansion of AI. The second step focuses on fostering innovation by integrating AI into core areas such as research and development, production, and supply chains to enhance product and service capabilities. The final step entails expanding operations to support global management, compliance, and organizational collaboration, ultimately evolving into a truly global, AI-first leading enterprise.
Nevertheless, for the majority of companies, the AI transformation journey remains in its initial stages. Recent global CEO surveys conducted by IBM highlight this significant gap. Most organizations are still in the experimental and pilot phases, with only a handful successfully advancing to the "AI-enabled" stage, where AI tools are deployed to achieve the expected return on investment. Even fewer are exploring the path to becoming an "AI-first" enterprise. Many firms continue to rely on calling third-party APIs or integrating external large language models, essentially utilizing external capabilities. A genuine AI-first enterprise requires deploying the AI framework within private or on-premises environments, integrating local data, core processes, and proprietary skills with intelligent agents to form a secure and controllable closed-loop system.
Based on direct feedback from clients in China, the Chief Technology Officer for Greater China at IBM emphasized that competitive advantage in the AI era will hinge on the speed of evolution. For Chinese enterprises, the level of automation and intelligence in core business operations is determined by factors such as the real-time reliability of data, the seamless integration of processes across the organization, and how insights empower decision-making. These elements are crucial markers of the transition from AI strategy to implementation and from pilot projects to scaling, requiring a holistic approach that integrates strategy, organization, and the technology platform.
Over the past seven years, IBM has consistently pursued large-scale acquisitions and strategic positioning around critical foundational capabilities. Since the acquisition of Red Hat in 2018, the company has completed nearly thirty strategic purchases, establishing itself as the world's largest provider of open-source software. Its capabilities now span key areas including AI, data, integration, security, and operations, further solidifying the foundational strength of its hybrid cloud and AI strategy.
Beyond continuously strengthening an open and trustworthy technology foundation, the CTO noted that IBM's technical expert teams are accelerating their transformation into front-line deployment engineers. This involves integrating engineering capabilities, consulting expertise, and business acumen to deeply engage throughout the entire lifecycle—from identifying requirements and building proof-of-concept prototypes to deployment, go-live, and ongoing service support—thereby accelerating clients' innovation and business operations.
Furthermore, to address the critical engineering challenges of scaling AI applications from pilot phases to full-scale operation, IBM employs a dual-driven capability model to help enterprises build an application development and operational system for the AI era. On the development and construction side, IBM Bob serves as an AI-first development partner designed for enterprise teams, guiding the entire software development lifecycle from planning and coding to testing, deployment, and modernization, with built-in governance, security, and cost control. The watsonx platform provides a unified foundation from data to intelligence. Together, they empower AI transformation across the entire enterprise value chain. The SaaS version of IBM Bob is already available, with an on-premises deployment solution slated for release later this year. On the IT management side, IBM leverages Bob, HashiCorp, and Concert to build a full-stack DevSecOps capability for the AI era, using AI to enhance the entire lifecycle of development, delivery, operations, and security management.
This comprehensive capability set has already been validated at scale both within IBM and through global client engagements. Internally, IBM Bob is used by over 80,000 users across multiple product lines and development stages, resulting in a more than 90% reduction in repetitive work time and approximately a 40% decrease in development costs. In a client case study, a telecommunications operator utilized IBM Concert to consolidate vulnerability, patch, and compliance data into a single solution. This enabled AI-driven automation and patch process orchestration, leading to a fourfold increase in patch delivery, a 78% reduction in patching time, and an 80% decrease in overall patch workload.
The CTO concluded by stating that when AI integrates into core business scenarios such as R&D, production, supply chain, and management, the most significant challenges often lie not in the delivery and launch phases, but in the disconnect between initial strategic planning and subsequent iterative optimization. Enterprise AI applications are not one-time delivery projects; they require continuous tuning, governance, and alignment with real business processes. Throughout this journey, the most critical needs for clients are "companionship-style" innovation and "one-stop" service, which represent the unique value proposition of IBM's technical expert teams.