为人类+AI工程重塑未来_将工程体系打造为持续增长引擎_27页_1mb

报告摘要

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.