> **来源：[研报客](https://pc.yanbaoke.cn)** # Engineering Re-invention for Human + AI: A Summary ## Core Content This document outlines a strategic approach to transforming engineering into a compounding growth engine by integrating AI and digital tools across the value chain. It emphasizes the need for a shift from traditional, siloed practices to a more connected, efficient and AI-augmented system that enables faster innovation, improved reliability and sustainable performance. ## Main Viewpoints - **Engineering is at an inflection point**: The industry is facing increased complexity, tighter regulations, and cost pressures, which require a fundamental rethinking of engineering processes. - **AI can transform engineering**: AI is no longer just a support tool but a core component of the engineering system, enabling automation, traceability, and real-time decision-making. - **Digital thread is essential**: A cloud-based digital core and a single source of data access are critical to creating a continuous, traceable record of the product lifecycle. - **Five system-level shifts are required**: 1. Run the V-model as a continuous evidence system 2. Move to model-based, simulation-first development 3. Automate verification and compliance at scale 4. Redesign the talent model for AI-augmented engineering 5. Make partner collaboration structured, not scrambled ## Key Information ### The Challenges - Legacy systems are nearing their limits, with engineers spending up to half their time on documentation, reporting, and information search. - There is a global estimated annual productivity loss of **US$39 billion** due to inefficient engineering practices. - **80%** of IT budgets are spent on maintaining legacy systems, highlighting the need for modernization. - **50%** of C-suite and engineering leaders cite validating software releases in regulated environments as a top challenge. ### The Opportunities - **AI can eliminate repetitive tasks**, allowing engineers to focus on high-value work such as judgment, creativity, and problem-solving. - **Model-based development** improves productivity by at least **20%** when AI is integrated. - **Structured partner collaboration** can reduce development cycles by up to **30%** through early integration and shared digital environments. ### The Enablers - **Cloud-based digital core**: Provides a single source of data access, standardizes data governance, and enables seamless integration across systems. - **AI integration**: Supports continuous evidence, traceability, and real-time decision-making, transforming engineering from a cost center to a growth engine. ### Case Studies - **Siemens Energy**: Used a digital thread and AI to complete **26 design iterations** and deliver the world’s first 100% hydrogen gas turbine. - **BMW**: Implemented a **Mobile Data Recorder (MDR)** on Azure with an AI copilot, achieving **10x faster** data analysis and engineering insights. - **CNH Industrial**: Created a shared single source of data access, enabling **faster product development** and uncovering **US$9 million** in cost-saving opportunities. - **ABB**: Integrated AI into its **RobotStudio** simulation tool, achieving **99% simulation-to-real correlation** and reducing deployment costs by **40%**. - **Volkswagen**: Established a structured engineering hub in Hefei, China, enabling **co-development and co-validation** with local partners like XPeng. ## Systemic Changes | Move | Systemic Change | |------|-----------------| | Run the V-model as a continuous evidence system | Continuous evidence capture, explicit decision ownership, traceability, and field signal integration | | Move to model-based, simulation-first development | Upstream learning, model linkage to requirements, platform rationalization, and reduced physical prototyping | | Automate verification and compliance at scale | Testable requirements from the start, multi-domain evidence linkage, and real-time compliance documentation | | Redesign the talent model for AI-augmented engineering | Cross-domain ownership, hybrid roles, AI handling low-value tasks, and human judgment at the final decision gate | | Make partner collaboration structured, not scrambled | Shared baseline, controlled data sharing, early supplier integration, and faster issue resolution | ## Conclusion The document argues that engineering must evolve from a cost center to a **growth engine** through a holistic reinvention of processes, tools, and collaboration models. This transformation is enabled by a **cloud-based digital core**, **AI integration**, and **structured cross-functional collaboration**, all aimed at improving speed, reliability, and innovation. The goal is to create a system where engineering operates at the **speed of software** while maintaining **safety, compliance, and quality**.