2026人机协同工程重构研究报告五大举措打造工程复利增长引擎_27页_1mb

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

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.