The AI for Process: Enterprise-Level Intelligent Process Transformation Blue Paper, jointly developed by Digital China, Deloitte China, and the China Academy of Information and Communications Technology (CAICT), focuses on the application and practice of enterprise-level AI in process digital-intelligent transformation. It establishes a full-cycle guidance framework from strategic planning to technological implementation, providing enterprises with a systematic solution to overcome the challenges of perception, methodology, and practice in AI adoption.

Over the next three to five years, the most significant shift in organizational structure will be a stronger move toward flattening.This transformation will directly reshape corporate decision-making: instead of relying on layer-by-layer outputs from different departments, management decisions will increasingly be based on the direct interpretation of data—and in many cases, on AI-driven analysis. As a result, decision chains will be dramatically shortened, and organizational structures will become significantly more streamlined.

—————— Lu Wenyan, Vice President, Shanghai Oriental Digital Commerce Co., Ltd.

Although there are not yet clear cases of AI models directly generating revenue, their impact in reducing enterprise costs—both directly and indirectly—is already evident. Overall, the return on investment (ROI) can be considered relatively reliable.

—————— Xu Dong, Vice President of Alibaba Cloud & General Manager of Tongyi Large Model Business

Choosing a reliable supplier for long-term collaboration can save significant hidden and communication costs. In particular, replacing suppliers in core business areas often requires a two- to four-year adjustment period, during which hidden communication and adaptation costs are substantial. Therefore, we prefer to establish a long-term, tightly integrated partnership with our collaborators.

—————— Wang Jinnan, General Manager of Digitalization and Information Technology, Swire Properties China

We can confidently predict that tasks such as data handling—transferring data from one system or offline source to another according to predefined rules—may eventually be fully automated. Currently, these tasks are primarily performed by clerical staff, but the demand for such positions is expected to gradually disappear in the future.

—————— Shi Jianhua, Assistant to the Chairman of the Board & General Manager of the Digital Innovation Center, Tasly Group

The advent of AI is disrupting the centralization trend established during the digital era. AI is no longer confined to improving efficiency within centralized frameworks; it is gradually being integrated into every unit of business processes, with each unit potentially generating independent agents capable of autonomous decision-making. This decentralized model is set to bring revolutionary and disruptive changes to the way data and computing resources are allocated in the future.

—————— Li Ying, Vice President & General Manager, China Software Ecosystem Division, Intel

From a theoretical perspective, if enterprises adopt AI technologies on a broad scale, in the short term—over the next five to ten years—human–machine symbiosis will remain the mainstream model. AI will primarily serve to enhance efficiency rather than replace human labor. Looking at the long-term trend, however, as robotic AI continues to advance, the role of humans in enterprise operations may gradually diminish.

—————— Zhong Xin, Chief Information Officer, AstraZeneca China

Enterprises should “be friends with models and friends with time,” rather than standing in the inevitable path of model evolution. It is essential to avoid investing in areas that are foreseeably bound to be covered by the capabilities of general-purpose large models in the future.

—————— Zhang Xin, Vice President, Volcengine

Over the next three to five years, the most significant shift in organizational structure will be a stronger move toward flattening.This transformation will directly reshape corporate decision-making: instead of relying on layer-by-layer outputs from different departments, management decisions will increasingly be based on the direct interpretation of data—and in many cases, on AI-driven analysis. As a result, decision chains will be dramatically shortened, and organizational structures will become significantly more streamlined.

—————— Lu Wenyan, Vice President, Shanghai Oriental Digital Commerce Co., Ltd.

Although there are not yet clear cases of AI models directly generating revenue, their impact in reducing enterprise costs—both directly and indirectly—is already evident. Overall, the return on investment (ROI) can be considered relatively reliable.

—————— Xu Dong, Vice President of Alibaba Cloud & General Manager of Tongyi Large Model Business

Choosing a reliable supplier for long-term collaboration can save significant hidden and communication costs. In particular, replacing suppliers in core business areas often requires a two- to four-year adjustment period, during which hidden communication and adaptation costs are substantial. Therefore, we prefer to establish a long-term, tightly integrated partnership with our collaborators.

—————— Wang Jinnan, General Manager of Digitalization and Information Technology, Swire Properties China

We can confidently predict that tasks such as data handling—transferring data from one system or offline source to another according to predefined rules—may eventually be fully automated. Currently, these tasks are primarily performed by clerical staff, but the demand for such positions is expected to gradually disappear in the future.

—————— Shi Jianhua, Assistant to the Chairman of the Board & General Manager of the Digital Innovation Center, Tasly Group

The advent of AI is disrupting the centralization trend established during the digital era. AI is no longer confined to improving efficiency within centralized frameworks; it is gradually being integrated into every unit of business processes, with each unit potentially generating independent agents capable of autonomous decision-making. This decentralized model is set to bring revolutionary and disruptive changes to the way data and computing resources are allocated in the future.

—————— Li Ying, Vice President & General Manager, China Software Ecosystem Division, Intel

From a theoretical perspective, if enterprises adopt AI technologies on a broad scale, in the short term—over the next five to ten years—human–machine symbiosis will remain the mainstream model. AI will primarily serve to enhance efficiency rather than replace human labor. Looking at the long-term trend, however, as robotic AI continues to advance, the role of humans in enterprise operations may gradually diminish.

—————— Zhong Xin, Chief Information Officer, AstraZeneca China

Enterprises should “be friends with models and friends with time,” rather than standing in the inevitable path of model evolution. It is essential to avoid investing in areas that are foreseeably bound to be covered by the capabilities of general-purpose large models in the future.

—————— Zhang Xin, Vice President, Volcengine

Over the next three to five years, the most significant shift in organizational structure will be a stronger move toward flattening.This transformation will directly reshape corporate decision-making: instead of relying on layer-by-layer outputs from different departments, management decisions will increasingly be based on the direct interpretation of data—and in many cases, on AI-driven analysis. As a result, decision chains will be dramatically shortened, and organizational structures will become significantly more streamlined.

—————— Lu Wenyan, Vice President, Shanghai Oriental Digital Commerce Co., Ltd.

Although there are not yet clear cases of AI models directly generating revenue, their impact in reducing enterprise costs—both directly and indirectly—is already evident. Overall, the return on investment (ROI) can be considered relatively reliable.

—————— Xu Dong, Vice President of Alibaba Cloud & General Manager of Tongyi Large Model Business

Choosing a reliable supplier for long-term collaboration can save significant hidden and communication costs. In particular, replacing suppliers in core business areas often requires a two- to four-year adjustment period, during which hidden communication and adaptation costs are substantial. Therefore, we prefer to establish a long-term, tightly integrated partnership with our collaborators.

—————— Wang Jinnan, General Manager of Digitalization and Information Technology, Swire Properties China

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AI for Process: A Methodology for Driving Enterprise Process Transformation

AI for Process is a cutting-edge methodology that leverages AI technologies to drive enterprise process transformation and achieve significant value leaps. It helps organizations build more innovative and competitive business models by using advanced AI to deeply understand the intrinsic relationships within complex business logic. This enables automated execution of processes and intelligent decision-making, fostering self-optimization and continuous evolution of workflows.

How AI for Process Gets Implemented

The TD (Twin-Drive) Model, proposed by Digital China, is an AI implementation methodology that synergizes top-level strategic decomposition with bottom-up scenario validation. Its goal is to balance long-term enterprise strategy with short-term value creation, addressing the challenges of “strategy implementation difficulty” and “fragmented business scenarios.”

AI-Native Enterprise Intelligent Reference Architecture

Smart Vision Intelligent Process Workbench
By leveraging the technical capabilities of LLMs and Agents, the enterprise processes are properly arranged to achieve the design and optimization of the processes, and ultimately increases the penetration rate of AI.
Learn more >
Smart Vision Enterprise-level Agent Middle Platform
Relying on the resources provided by AI cloud-native, by obtaining the contents from the service resource pool and data resource pool, various applications of intelligent agents are constructed.
Learn more >

Are You Ready for AI for Process?

AI for Process
Enterprise-Ready
  • Enterprise Knowledge: Laying the Foundation for Future Growth
    Adopt a “Standards + Agile” knowledge management approach
    Build high-quality corpora to strengthen the foundation for AI-driven processes
    Drive the evolution of execution models to adapt to complex business scenarios
    Enable AI to become a process expert, achieving continuous workflow optimization
  • Talent Capabilities: Building a Workforce Ready for the Future
    Develop “Business + Algorithm” hybrid teams
    Core competencies for next-generation talent: business acumen, data literacy, AI/GenAI awareness, communication & collaboration, and continuous learning
    AI-driven new roles: AI-enabled business analysts, process mining & optimization specialists, data scientists
    Internal talent transformation: skill reshaping programs for all employees, high-potential talent, and dedicated AI positions
  • Organizational Transformation: Building an Agile and Efficient AI Governance System
    Break down organizational silos and establish a professional AI enablement center to achieve a balance between centralized governance and distributed execution
    New leadership roles in the AI era:
    Chief Process Officer (CPO)
    AI Product Manager
    Data & AI Governance Lead
  • Ecosystem Collaboration: Building an Open and Win-Win Partnership Network
    From suppliers to strategic partners: redefining technology collaborations
    Creating an intelligent value chain
    Co-developing industry standards and fostering industry–academia–research synergy

AI for Process - Enterprise-Level Intelligent Process Transformation Blue Paper

The Role of AI in Enterprise Processes in the Digital-Intelligence Era
The Right Way to Unlock Enterprise-Grade AI
AI-Native Reference Architecture for Enterprise Digital Intelligence
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