KPMG全球技术报告2026汽车

understanding today’s industry realities Foreword This KPMG study draws on insights from leaders across the globalTechnical debt, talent shortages and legacy processes continue toThe global automotive industry stands at a crossroads. The Intelligence Age is no longer a distant concept; it is reshaping products, valuechains, and competitive advantage in real time. AI, advanced software architectures and next-generation computing paradigms are transforminghow vehicles are conceived, engineered and experienced. Meanwhile, geopolitical uncertainty and supply chain volatility are heighteningstrategic and operational pressures. In this environment, separating signals from noise is critical. automotive ecosystem, cutting through speculation to provide anevidence-based view of where the industry truly stands.The findings are based on responses from senior executives acrossfive key sub-segments: OEMs, commercial vehicle manufacturers slow transformation, reflecting challenges seen across the broadertechnology landscape.Despite these constraints, optimism remains strong. Automotiveexecutives believe they can unlock higher levels of technological and operational maturity within the next few years, even though fewhave reached that threshold today. Realizing this ambition will requireadaptive strategies, disciplined execution and a commitment todeveloping the workforce alongside technology.As we look ahead, one question frames this report:Can the industrywithstand the disruption ahead and turn it into momentum?We (truck manufacturers), tier-1 suppliers, new technology componentproviders and mobility solution providers.In our recent 25th Global Automotive Executive Survey, technologywas identified as one of the five core transformation priorities shaping the future of the industry. Building on this foundation, the presentstudy provides a deeper perspective on the “Manage Technology”dimension — exploring how companies are translating strategic intentinto execution.Organizations are now moving beyond fragmented experimentationand working to embed AI and automation at scale across operations, Global Head of AutomotiveKPMG International believe the answer lies in rejecting hype, grounding decisions in dataand pursuing bold technological ambition with operational realism.We welcome the opportunity to discuss these findings and theirimplications with you. engineering and customer lifecycles. However, progress remains uneven. Key findings The technological landscape — Understanding today’sindustry realities Part one but continues to struggle with legacy friction, uneven readiness and wideningcapability demands. Leaders see technology as central to competitiveness yetacknowledge the operational barriers that slow execution. +21%63%Truck83%Tier 183%New tech90%Mobility provider Technological maturity is advancing unevenly across segments, with each cluster acceleration is expected over the next year even though scaling advancedtechnologies remains highly complex. Part two Investment strategies focus on high-value domains such as AI, cybersecurity,modern delivery and digital twins. While organizations consistently realize value 37%43%Truck59%Tier 160%New tech from technology, frequent trade-offs reveal persistent execution challenges. Key findings Building adaptive strategies amid continual disruption control risk, while selective decentralization enables speed and business proximity.Organizations increasingly rely on structured experimentation to manage uncertaintyaround breakthrough technologies. Part three AI adoption is accelerating sharply across all segments, complemented by a 42%54%Truck84%Tier 1 organizations expect significant progress toward scaled AI deployments. Actionable insights PrioritizetechnologieswithscalableROI Treatgovernanceasaperformanceaccelerator,notaconstraint Movefrompilotstoplatform-scaleexecution Buildaworkforcemodelthatscaleswithintelligence,notheadcount create value from isolated AI and data usecases, but scaling remains constrained by legacyestates, fragmented ownership and inconsistentdata contracts (for example, high “hitting blocks”rates in AI, data and cybersecurity). Leaders muststandardize architectures, enforce “golden paths”and industrialize Machine Learning Operations(MLOps) and data governance to make AI arepeatable capability rather than a collection ofisolated wins. tier 1s and mobility solution providers capturethe highest returns through standardized digitalcapabilities and data-intensive architectures.At the same time, legacy-heavy players lag. Leadersshould prioritize technologies that enable scalableeconomics — AI, simulation, digital twins andmodern delivery — and sequence transformationwhere it boosts capital efficiency most.automation, low-code) is evident across allsegments. Executives must redesign operatingmodels around hybrid digital-human capacity,upskilling teams for AI oversight, orchestration andmodel-driven operations to ensure talent be