> **来源：[研报客](https://pc.yanbaoke.cn)** # KPMG Global Tech Report 2026: Automotive Summary ## Core Content The KPMG Global Tech Report 2026: Automotive provides an in-depth analysis of the current technological landscape and future transformation strategies within the global automotive industry. It highlights the challenges and opportunities faced by different segments of the industry, including OEMs, truck manufacturers, tier-1 suppliers, new technology component providers, and mobility solution providers. ## Main Points ### 1. Industry Overview - The automotive industry is entering the **Intelligence Age**, driven by AI, advanced software, and next-generation computing. - Despite strong strategic conviction about the importance of technology, **operational challenges** such as technical debt, talent shortages, and legacy systems hinder progress. - The pace of technological change is **accelerating**, creating both **opportunities** and **pressures** for transformation. ### 2. Strategic Conviction - **All segments** of the automotive industry agree that **advanced technologies are essential** for future competitiveness. - **OEMs** and **truck manufacturers** are more cautious, while **mobility providers** and **new tech suppliers** are more willing to take bold risks. ### 3. Technological Maturity - **AI and automation** are being adopted unevenly across segments. - **OEMs** are expected to see a **+21% increase** in technological maturity within the next year. - **Tier 1 suppliers** and **mobility providers** are leading in AI adoption and are more advanced in scaling technologies. ### 4. Tech Investment Focus - Organizations are focusing on **high-value domains** such as AI, cybersecurity, digital twins, and modern delivery. - **ROI realization** is still challenging, particularly due to **legacy processes** and **data governance issues**. ### 5. Workforce Transformation - The shift toward **digital labor** (AI agents, automation, low-code) is evident across all segments. - **Upskilling** and **redefining roles** are necessary to support AI and automation initiatives. ## Key Findings ### 1. **Technology Readiness and Future Ambition** - There is a **strong alignment** on the strategic importance of advanced technologies. - However, **execution capacity** remains a major constraint, especially in terms of **capital, talent, and integration complexity**. ### 2. **Segment-Specific Constraints** - **OEMs** are burdened by **legacy complexity**, which hinders the adoption of new technologies. - **Truck manufacturers** are more **risk-sensitive**, due to long product cycles and regulatory pressures. - **Tier 1 suppliers** are more **agile** and **eager to modernize**, but their progress is limited by **OEM investment timing**. - **New technology suppliers** are concerned about **integration speed** and **staying ahead of innovation**. - **Mobility providers** are **most adaptive**, with a focus on **speed, iteration, and digital maturity**. ### 3. **Resilience and Adaptability** - Most organizations rate themselves as **resilient and adaptive**, but the **manifestation differs** by segment. - **OEMs** and **truck manufacturers** rely on **governance and operational buffers**, while **mobility providers** and **new tech suppliers** emphasize **velocity and innovation**. ### 4. **Geopolitical and Macroeconomic Responses** - Companies are responding to **geopolitical uncertainty** by **strengthening data foundations**, **localizing talent**, and **de-risking supply chains**. - **OEMs** and **truck manufacturers** are focusing on **data sovereignty** and **onshore hiring**. - **Tier 1 suppliers** are **tightening capital discipline** and **increasing innovation centers**. - **New technology suppliers** are **prioritizing cost control** and **domestic skill development**. - **Mobility providers** are **reducing investments** in adversarial regions and **expanding innovation hubs**. ## Actionable Insights ### 1. **OEMs** - Focus on **scaling AI use cases** across engineering, operations, and customer domains. - Key steps: Rationalize platforms, embed **Security-by-Design**, move AI to product-level execution, create **budget-shock playbooks**, and industrialize collaboration templates. ### 2. **Truck Manufacturers** - Accelerate **operations-adjacent capabilities** (digital twins, simulation). - Key steps: Standardize **cyber controls**, build **canonical data models**, establish a **Simulation Factory**, use **Pilot-to-Plant** playbooks, and set up an **ESG-execution PMO**. ### 3. **Tier 1 Suppliers** - Already leaders in **AI, data, and simulation** with high **capital efficiency**. - Key steps: Reposition **XaaS** as a diversification engine, shift to **solution-led platforms**, pursue **targeted M&A**, design for **regionalization**, industrialize **reusable assets**, and automate **regulatory compliance**. ### 4. **New Technology Component Suppliers** - High **innovation velocity**, but **scaling is lagging** due to **immature data plumbing** and **MLOps**. - Key steps: Build an **enterprise-grade data backbone**, publish **integration playbooks**, clarify **GTM strategies**, differentiate with **post-quantum readiness**, and use **proof-of-value cascades**. ### 5. **Mobility Solution Providers** - Demonstrating **strong AI/data maturity**, **decentralized innovation**, and **high ROI**. - Key steps: Intensify **security hardening**, drive **interoperability**, operationalize **responsible AI**, execute **workforce transition plans**, and secure **scale economics**. ## Industry Myths vs. Reality - **Myth 1:** Strategy is the bottleneck. - **Reality:** Conviction on technology is widespread; the real issue is **execution capacity**. - **Myth 2:** Automotive is slow to adapt. - **Reality:** The industry is **dynamic**, with **different operating models** (control vs. velocity). - **Myth 3:** Geopolitics will slow progress. - **Reality:** Volatility is **accelerating** transformation, with companies **doubling down** on digital foundations. ## Conclusion The automotive industry is **capable of turning disruption into momentum**. The report emphasizes the need for **strategic alignment**, **operational realism**, and **industrializing technology delivery**. Success will depend on **scaling AI**, **improving data governance**, **building resilient operating models**, and **developing a future-ready workforce**. The opportunity is clear: **turning strategic intent into operating-model excellence** through secure data, reusable architecture, responsible AI, and digital workforce capacity.