Automotive digitalisation refers to the comprehensive upgrading and transformation of vehicle R&D, manufacturing, operational management, and business models driven by next-generation information technology, with data serving as the core catalyst. At its heart lies a closed-loop data architecture spanning acquisition, transmission, storage, processing, and feedback to extract actionable insights, enabling holistic management, strategic decision-making, and process control across the entire automotive value chain and vehicle lifecycle. In current industry practice, digitalisation is deeply integrated with artificial intelligence (AI), charting an evolutionary path towards "digital intelligence".
The automotive industry is undergoing a paradigm shift from "Software-Defined Vehicles" (SDVs) to "AI-Defined Vehicles" (AIDVs). While SDVs centre on functional digitalisation (such as over-the-air [OTA] updates), AIDVs focus on cognitive intelligence—leveraging machine learning and closed-loop data to equip vehicles with autonomous decision-making and predictive situational awareness. Today, AI has seamlessly permeated the entire automotive lifecycle, spanning R&D, production, in-cabin interaction, and connected mobility services.
| Technology / Scenario | Core Focus | Industry Value |
|---|---|---|
| Digital Twin | Building real-time virtual replicas of physical vehicles and systems for R&D validation, production simulation, and predictive maintenance | Shortens R&D lead times and slashes physical crash/durability testing costs; the global passenger vehicle digital twin market is projected to reach USD 2.1 billion by 2026 |
| Smart Factory & "Industrial Brain" | Enabling agile manufacturing and end-to-end automation via "future-ready smart plants", while synchronising supply chain collaboration through an "Industrial Brain" platform | SERES' "Industrial Brain" links over 300 supply chain partners, boosting R&D efficiency by 20% and improving on-time delivery rates by 13% |
| Smart Cockpit & Interaction | Leveraging AI large models and multimodal recognition technologies to deliver bespoke, hyper-personalised user experiences | Transforms vehicles from simple transport tools into intuitive "intelligent companions" offering genuine emotional and lifestyle value |
Industry Case Study: Chinese automaker FAW built an intelligent operational hub guided by the "EOA" (Enterprise Operations AI) framework, deploying 20 "digital employees" to slash manual approval workflows by 60% and reduce manufacturing costs by 40%. By executing a full cloud-native transition and migrating 290 IT systems to the cloud, its annual operations and maintenance (O&M) expenditure dropped dramatically from RM200,000,000 to RM27,000,000.
Policy Framework: In December 2025, four Chinese government bodies, including the Ministry of Industry and Information Technology (MIIT), jointly released the *Implementation Plan for Digital Transformation in the Automotive Industry*, setting a clear two-phase roadmap: by 2027, the adoption rate of digital R&D design tools will exceed 95%, with key production processes achieving over 70% numerical control; by 2030, deep integration between digital intelligence and core automotive business operations will be fully realised.
Automotive digitalisation is maturing from piecemeal pilot projects into full-scale, end-to-end commercial adoption. The automotive battleground is shifting from conventional hardware specifications to a multidimensional contest anchored by computing power, intelligent algorithms, and software ecosystems. Digital twins are advancing towards fully integrated, lifecycle-spanning platforms that unite R&D, manufacturing, supply chain logistics, and aftersales operations. Furthermore, the convergence of AI foundation models and digital twin architectures is poised to resolve long-standing industry pain points, notably data silos, cybersecurity, and regulatory compliance.
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