From Think 2026: The Breakthrough of AI Applications — How Can Enterprises Move from “Renting Intelligence” to “Owning Intelligence”?

BeijingJune 10, 2026 /PRNewswire/ — Recently, IBM (NYSE: IBM) held its Q2 2026 media briefing in Beijing. Zhai Feng, Chief Technology Officer of IBM Greater China Group, along with a team of senior technical experts from IBM’s China Technology Business Unit, systematically shared IBM’s latest thinking and practices on enterprise-grade AI implementation. This was based on the latest insights, products, and technologies unveiled at the Think 2026 conference, as well as the real-world scenarios of Chinese enterprises undergoing digital and intelligent transformation and global operations.

From integrating data and IT infrastructure to restructuring core business processes, IBM comprehensively demonstrated how its full-stack enterprise-grade AI capabilities support Chinese enterprises in moving from isolated pilots to large-scale applications. Through IBM’s own “Client Zero” first-hand practices and real Chinese customer cases, the company presented the verifiable value of AI in improving efficiency, optimizing costs, and enhancing operational resilience.

Technical experts participating in this event included: Lin Kaidi, AI Solutions Technical Expert, IBM China Technology Business Unit; Xu Qing, Solutions Architect, IBM China Technology Business Unit; Feng Shengbo, Automation and Security Solutions Technical Expert, IBM China Technology Business Unit; Yu Xianhao, Senior ELM Solutions Technical Expert, IBM China Technology Business Unit; Dong Zhao, Customer Success Manager, IBM China Technology Business Unit; Liu Pinyi, Customer Success Manager, IBM China Technology Business Unit; and Zhang Yanfa, Customer Success Manager, IBM China Technology Business Unit.

From “Renting Intelligence” to “Owning Intelligence”: A Key Step in Scaling Enterprise AI

Against the backdrop of increasing pressure for growth and efficiency, coupled with the complexities of globalization, Chinese enterprises need to ascend three levels to transform AI into sustainable growth capabilities: “Foundation Building” to solidify the data and IT infrastructure, supporting AI scaling and global expansion; “Innovation” to drive AI into core areas such as R&D, production, and supply chain, enhancing product and service capabilities; and “Territory Expansion” to support global operations, compliance, and organizational synergy, transforming into true global, AI-first leading enterprises.

However, for most enterprises, the AI transformation is still in its infancy. IBM refers to this phase as “Day Zero” of the AI revolution: AI remains largely in the realm of “tool rental” and isolated business scenarios, yet to penetrate core processes, decision-making mechanisms, and foundational architectures — the qualitative shift from “renting intelligence” to “owning intelligence” has not yet arrived.

IBM’s latest research on global CEOs also reveals this gap: most enterprises are still experimenting and piloting, with only a few successfully entering the “+AI” phase (deploying AI tools) and achieving expected ROI. Even fewer are exploring becoming “AI+” (AI-first) enterprises. Many companies still rely on calling third-party APIs and accessing external large models, essentially using external capabilities. True AI+ enterprises need to deploy AI systems in private or on-premises environments, combining local data, core processes, and proprietary skills with agents to form a secure, controllable closed loop.

IBM Chairman and CEO Arvind Krishna stated at Think 2026: “True leading enterprises are not focused on deploying more AI tools, but on redesigning their own business operating models.” Based on this insight, IBM proposed the “AI Operating Model” blueprint for becoming an AI-first enterprise, driving enterprise AI applications from “incremental business improvements” to “operating model transformation” through four key elements: Intelligence, Action, Trust, and Operations:

• Intelligence: Enabling models to understand not just general knowledge, but also the context, semantics, and ontology of enterprise data, forming enterprise-grade intelligence capable of supporting real-time decisions.
• Action: Ensuring the agency of intelligent agents goes beyond the surface, truly reaching core enterprise processes and systems, enabling cross-process collaboration through open APIs and tool calls.
• Trust: Building full-stack security, controllability, and compliance capabilities spanning data, models, agent execution, and IT infrastructure.
• Operations: Supporting the iterative implementation of AI strategy, models, and agents through a continuously optimized IT operations closed loop.

Based on real feedback from Chinese clients, Zhai Feng, CTO of IBM Greater China Group, further pointed out: “In the AI era, a company’s competitive advantage will depend on its speed of evolution. For Chinese enterprises, whether data is real-time and trustworthy, whether processes can be globally integrated, and how insights empower decision-making, determine the level of automation and intelligence in core businesses. This is a key indicator of AI moving from strategy to implementation, from pilot to scale — strategy, organization, and technology platform are all indispensable.”

Over the past seven years, IBM has continuously pursued large-scale acquisitions and strategic investments around key foundational capabilities: since acquiring Red Hat in 2018, IBM has completed nearly thirty strategic acquisitions, effectively becoming the world’s largest open-source software provider, with capabilities covering key areas such as AI, data, integration, security, and operations, further solidifying its hybrid cloud and AI strategy’s foundational capabilities.

Zhai Feng stated that, in addition to continuously strengthening an open and trustworthy technology foundation, on the delivery side, IBM’s technical expert team is accelerating its “FDE” transformation (Note: FDE, Field Deployment Engineer), integrating engineering capabilities, consulting expertise, and business acumen. They deeply participate in the entire lifecycle from requirements discovery and POC prototype building to deployment go-live and service handover, accelerating clients’ R&D innovation and business operations. “When AI enters core business scenarios like R&D, production, supply chain, and business management, the biggest difficulty is often not delivery and go-live, but the gap between initial strategy formulation and subsequent iterative optimization. Enterprise-grade AI applications are not one-off delivery projects; they require continuous tuning, governance, and alignment with real business processes. In this process, the ‘accompanying’ innovation and ‘one-stop’ service that clients need most is the unique value of the IBM technical expert team.”

“Dual-Wheel Drive” Empowering the Full Enterprise Value Chain: Breaking Down Data System Silos, Accelerating IT Infrastructure Modernization

As AI applications, agents, and automation processes enter core enterprise scenarios, the complexity of R&D, delivery, operations, and security increases simultaneously. Enterprises need to integrate these aspects into a unified full lifecycle management system, supporting the stable operation, continuous iteration, and secure expansion of AI applications through automation, governance, and controllability.

Addressing the key engineering challenge of moving AI applications from pilots to large-scale operations, IBM is helping enterprises build an application development and runtime system for the AI era through two sets of “dual-wheel drive” capabilities —

• IBM Bob + IBM watsonx: On the development and construction side, IBM Bob is an AI-first development companion designed for enterprise teams, spanning the entire software development lifecycle from planning, writing, testing, to deployment and modernization, embedding governance, security, and cost control within it; watsonx provides a unified foundation from data to intelligence, together empowering enterprise-wide value chain AI transformation. Currently, the SaaS version of IBM Bob has been released, with an on-premises deployment solution expected later this year.
• IBM Bob + DevSecOps: On the IT management side, IBM builds full-stack DevSecOps capabilities for the AI era through Bob, HashiCorp, and Concert, empowering the entire lifecycle of R&D, delivery, operations, and security management with AI.
  IBM Concert, as a unified intelligent agent IT operations platform, integrates signals across applications, infrastructure, and networks, helping enterprises proactively reduce downtime, compliance, and operational risks, and quickly achieve product R&D, configuration, deployment, and operations. Vault, one of HashiCorp’s core capabilities — machine identity security authentication and management — helps enterprises establish a controllable, auditable, and highly available security foundation in complex multi-cloud and AI agent environments by centrally managing keys, credentials, and access permissions, achieving “financial-grade high availability”. Globally, Vault has been downloaded over 250 million times and is trusted by large commercial organizations such as banks, telecommunications, and manufacturing.

This set of capabilities has been validated at scale within IBM and in global client practices. Internally at IBM, IBM Bob has covered over 80,000 users and multiple product lines and development processes, saving over 90% of repetitive work time and reducing development costs by approximately 40%.

In client practice, a telecom operator used IBM Concert to unify vulnerability, patch, and compliance data into a single solution, achieving AI-driven automation and patch process orchestration, resulting in a 4x increase in patch delivery, a 78% reduction in patching time, and an 80% reduction in overall patching workload. In security governance, a client adopted IBM Vault with a two-site, three-center high-availability federated architecture, providing full lifecycle cryptographic trust roots for distributed AI pipelines and agent orchestration, achieving “zero trust” credential governance for non-human entities without affecting high-frequency autonomy and elastic scaling of AI business, creating a strong security and compliance line of defense for the enterprise’s AI strategy implementation.

Delving into Core Business Scenarios, Making AI the Primary Productivity Driver

The support of a robust IT infrastructure allows AI to penetrate deeper into business processes. The AI implementation scenarios that truly determine a company’s competitiveness are moving from general-purpose assistants and edge scenarios deep into core chains like R&D, production, supply chain, and business management, becoming a key source of core competitiveness.

In China, areas such as complex engineering R&D, asset-intensive operations, global supply chain collaboration, and business performance management are becoming key battlefields where enterprise-grade AI first generates value. Taking industries like automotive, biopharmaceuticals, semiconductors, electronics, and consumer goods as examples, the combined pressures of increasing product complexity, stricter regulatory compliance, enhanced supply chain collaboration, and cost-efficiency optimization make core areas like R&D, production, supply chain, and business management the primary entry points for AI value validation, and also the “touchstones” for the continuously evolving IBM technical expert team.

In the R&D Innovation field, increasing product complexity and stricter compliance requirements demand that enterprises (especially automotive manufacturers, medical device companies, and frontier tech firms related to the “low-altitude economy”) respond simultaneously to market changes and regulatory standards. IBM Engineering Lifecycle Management (ELM) is designed for complex engineering R&D scenarios, covering requirements, design, development, testing, and reporting, helping enterprises “keep one eye on the market and one on compliance.” Simultaneously, ELM is actively integrating AI capabilities, supporting IBM Bob and third-party AI agents, aiming to automate repetitive, time-consuming engineering tasks, allowing engineers to focus more on creative work and problem-solving.

As a cross-engineering domain full lifecycle solution, ELM’s core value lies in connecting requirements management, system/software modeling, industrial tool flows, change testing, and flexible manufacturing, forming a “Digital Thread”, enabling bidirectional traceability from requirements to model architecture, product design, configuration, code, and test results. After deploying ELM, a leading automotive technology company achieved real-time management of 11 business processes, seamlessly integrated with existing tools, and expects a 10% reduction in cost investment.

In the Production and Manufacturing field, equipment stability directly impacts production efficiency, product quality, and profitability. Asset management is becoming a key link in improving asset efficiency and return on investment. IBM Maximo is designed for enterprise asset lifecycle management, integrating IoT, AI, and visual inspection capabilities, making the status of every device perceptible, risks predictable, and maintenance optimizable.

• A large biopharmaceutical enterprise achieved intelligent equipment management based on IBM Maximo for Life Science, resulting in an average 35% reduction in equipment failure rate, a 10% increase in equipment uptime, and a reduction of approximately 30,000 inventory items through MRO inventory optimization and ERP integration.
• A leading new energy vehicle company built a one-stop AI visual inspection platform based on IBM Maximo Visual Inspection, enabling self-service training and deployment of AI vision models. It automatically detects product defects at high production takt times, promptly identifies quality issues, and responds in real-time, significantly reducing rework costs caused by undetected quality defects and effectively improving product quality. Currently, the client has built over 115 models based on this platform, covering multiple stages of automobile production, saving 60% of model R&D and deployment costs.

In the Supply Chain Integration field, an increasing number of “going global” enterprises face the challenge of complex real-time data connections across systems, partners, and regions. Enterprise data interchange technology (B2B integration), as the foundation of supply chain digitization, is also evolving: besides mainstream EDI technology, today’s B2B integration platforms include APIs and event streaming platforms. For large enterprises, it is further defined as a security architecture, data governance, and compliance framework, deeply integrated into the enterprise’s standard system.

IBM webMethods B2B was thus created. It addresses not just “what can be integrated,” but provides high availability, flexible data transformation, business process monitoring and tracking, and the high reliability and convenient partner onboarding brought by hybrid deployment in large-scale, highly regulated, and critical business scenarios. An electronics high-tech enterprise used IBM webMethods as its core integration platform, integrating 11 core business systems, stably supporting real-time data synchronization and business connection with over 750 global distributors, improving overall supply chain coordination efficiency by 70% and reducing management costs by 30%.

In the Smart Business Management field, budget, sales, supply chain, and production data are scattered, hindering the efficiency of forecasting, simulation, and decision-making. As an AI-driven “smart business management brain,” IBM Planning Analytics integrates planning, budgeting, and forecasting onto a unified, secure, real-time platform. It comprehensively organizes and models business data from a professional financial perspective, laying a solid foundation for subsequent AI applications, including: comprehensive budget management, consolidated financial statement generation, dynamic management report generation (e.g., intelligent querying of business data), dynamic rolling budgets, and dynamic sandbox simulations.

Based on these capabilities, enterprises can establish a unified set of rules for business data across R&D, manufacturing, supply chain, service, and finance, thereby achieving business data governance and empowering dynamic, scientific decision-making. A fast-moving consumer goods company leveraged this to build a financial and marketing planning platform, enhancing cross-departmental collaborative decision-making efficiency through real-time multi-scenario calculations, strengthening demand forecasting, rapid response, and budget control capabilities, and promoting more rational allocation of enterprise resources.

Conclusion

AI is becoming a new engine for reshaping the operating models of Chinese enterprises, and its globalization is entering a deeper phase of value chain and supply chain collaboration. Beyond the ability to call upon intelligence, enterprises need to build a manageable, trustworthy, and scalable enterprise-grade intelligence system on top of their own data, processes, and proprietary skills.

Facing this new phase of intelligence and globalization, IBM will continue its “Consulting + Technology” dual-wheel drive approach, leveraging a full-stack technology platform oriented towards core business scenarios, an “accompanying” co-creation mechanism, and AI infrastructure covering the entire technology environment, to help Chinese enterprises solidify their AI foundation, restructure core processes, and unleash sustainable growth momentum.

About IBM

IBM is a leading global hybrid cloud, artificial intelligence, and enterprise services provider, helping clients in over 175 countries and regions derive business insights from their data, streamline business processes, reduce costs, and gain a competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure sectors such as financial services, telecommunications, and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to achieve digital transformation quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions, and enterprise services provide our clients with open and flexible choices. A long-standing commitment to corporate integrity, transparent governance, social responsibility, inclusive culture, and service spirit is the cornerstone of IBM’s business development. For more information, please visit: https://www.ibm.com/cn-zh

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