> **来源：[研报客](https://pc.yanbaoke.cn)** # Accenture: Reinventing Engineering for Human + AI Collaboration ## 核心内容 Accenture's report outlines the critical need for **reinventing engineering processes** to align with the demands of the future, where **adaptability, speed, and integration** are paramount. The document emphasizes that traditional engineering models are no longer sufficient to meet the challenges posed by **software-defined products**, **tighter regulations**, and **relentless cost pressures**. Engineering must evolve into a **compounding growth engine**, leveraging **AI and digital tools** to transform how work is done across the value chain. ## 主要观点 - **Engineering is at an inflection point**: By 2030, adaptability and speed will define performance. Legacy systems are reaching their limits, and engineers are spending too much time on documentation and reporting rather than core work. - **The biggest AI blocker is the old toolchain**: Fragmented systems, broken data flows, and disconnected processes hinder AI's potential. These issues force engineers to manually validate and chase approvals, which AI can eliminate. - **The scarcest asset is capability, not compute**: While compute power is abundant, **cross-domain judgment, deep expertise, and fast decision-making** are in short supply. Organizations must focus on building **human + AI engineering systems**. - **Engineering must become a growth engine**: This requires **system-level shifts**, including the integration of AI, the development of a **single source of data access**, and **structured partner collaboration**. - **Five key moves** are proposed to transform engineering into a growth engine: 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** ## 关键信息 - **Digital Thread**: A cloud-based digital core enables a **single source of data access** that links all aspects of the product lifecycle, from requirements to field data. This allows for **continuous traceability** and **real-time decision-making**. - **Productivity loss**: Engineers globally are losing **US\$39 billion annually** due to inefficiencies in documentation and reporting. - **Case studies**: - **CNH Industrial** reduced design iterations from 40 hours to minutes, uncovering **US\$9 million in cost savings**. - **Siemens Energy** completed **26 design iterations** using a digital thread, achieving **four times more** than traditional methods. - **ABB** achieved **99% simulation-to-real correlation**, reducing deployment costs by **40%** and **setup time by 80%**. - **Talent model transformation**: AI-augmented engineering requires **hybrid roles** and **structured workflows**, with **AI handling repetitive tasks** and **humans making final decisions**. - **Partner collaboration**: Structured and integrated collaboration with suppliers and customers is essential to **avoid late-stage rework** and **improve product performance**. ## 五项关键举措 ### 1. 运行V模型作为持续证据系统 - **目标**：使V模型成为持续的证据系统，而不是线性流程。 - **AI的作用**：AI可识别决策偏差，生成检查清单，并支持持续的证据积累。 - **系统变化**： - 团队持续捕捉证据 - 决策权明确 - 可追溯性支持决策 - 场地信号反馈至下一周期 ### 2. 采用模型驱动、仿真优先的开发方式 - **目标**：将模型与需求和架构直接连接，实现早期验证。 - **AI的作用**：AI通过分析历史数据，识别常见故障模式，建议测试和验证方案。 - **系统变化**： - 学习过程前置 - 模型与需求、架构保持连接 - 产品团队优化平台和变体 - 工程师复用已验证模型 ### 3. 大规模自动化验证和合规 - **目标**：将验证和合规嵌入开发流程，而非后期集中处理。 - **AI的作用**：AI可识别模糊的表述，建议合规标准，并持续生成证据包。 - **系统变化**： - 需求从一开始就是可测试的 - 测试和配置控制嵌入开发流程 - 多领域证据保持与验证对象的链接 - 持续生成合规和网络安全证据 ### 4. 重新设计AI增强型工程人才模型 - **目标**：构建**Human + AI**的协作模式，减少低价值任务。 - **AI的作用**：AI识别瓶颈，推荐学习路径，支持跨领域决策。 - **系统变化**： - 跨领域决策权明确 - 混合角色传递跨领域知识 - AI处理重复性任务 - 人类保留最终决策权 ### 5. 建立结构化合作伙伴协作 - **目标**：将供应商和客户纳入统一的数字环境，确保协作一致。 - **AI的作用**：AI检测接口冲突，识别异常测试结果，加速审核流程。 - **系统变化**： - 领导层明确核心与合作伙伴边界 - 供应商在受控环境中协作 - 共同开发和验证基于统一基线 ## 结论 工程部门必须从**成本中心**转型为**增长引擎**，通过构建**云原生数字核心**和**单一数据源**，实现**持续验证、快速迭代和跨价值链协作**。AI将从支持工具转变为**工程系统的一部分**，推动**自动化、智能化和高效化**的工程流程，从而提升整体绩效和竞争力。