Automotive's AI Readiness Gap: Big Spend, Low Readiness
AI budgets are up, but operational readiness isn't. In the latest KPMG findings, 86% of automotive executives say they're investing in AI, while only 20% believe their organisations are prepared to use it effectively.
This gap is widening under real pressure: rising input costs, geopolitical risk, and a cooling EV market. Strategy without execution speed will lose ground fast.
The Five T's of Survival
A small elite-about 15%-is pulling ahead by running on five pillars. Use these as your operating checklist:
- Transformation: Treat AI, SDVs, and supply chain redesign as a company-wide reset, not a side project.
- Technology: Prioritise software architecture, data pipelines, and platform choices that shorten time-to-value.
- Trust: Build consumer trust with safety, security, and transparent data use-then back it with certifications and audits.
- Tensions (management): Balance cost, speed, and risk with clear tradeoff rules owned by the C-suite.
- Thriving Together: Partner across ecosystems-chips, cloud, charging, logistics-to compress timelines and de-risk scale-up.
As one industry leader put it, the sector is being "completely reimagined," and staying competitive now requires a major strategic overhaul to meet shifting customer expectations and geopolitical conflicts.
2030: Software-Defined by Default
- Autonomy baseline: 87% of executives expect autonomous driving to be standard across segments by 2030.
- Cyber priority: 71% of EMEA leaders rank digital security as a top concern as vehicles become fully connected.
- AI value zones: Biggest expected gains: R&D productivity (48%) and supply chain efficiency (46%).
Translation: product value shifts from hardware to software, data, and services. Your operating model must treat vehicles as updatable platforms with recurring revenue-backed by security-first engineering.
The Customer Disconnect
Only 16% of executives see customer satisfaction as the main driver of long-term profit. Among frontrunners, that number jumps to 48%-and it shows up in retention and lifetime value.
EV adoption stalls where price and charging gaps persist. That makes digital retention harder: if onboarding is clunky and charging is unreliable, no app feature will save churn. Anchor product roadmaps to real customer jobs-to-be-done, not internal release cycles.
From Global to Local-for-Local
68% of firms are restructuring footprints. Nearshoring and friendshoring are replacing fragile global chains, shifting to "local-for-local" production to cut risk and lead times.
If you need a primer on friendshoring's policy logic and tradeoffs, see the IMF's overview here. Practical move: model total cost of ownership with tariff scenarios, logistics variability, and working-capital impact-not just labour rates.
Thailand's High-Value Pivot
Thailand, Southeast Asia's auto hub, is shifting from volume manufacturing to higher-value, tech-led production. The message from industry leaders is clear: digital and green transitions are no longer optional.
Expect winners to invest in data governance, local supplier development, and sustainability as baseline requirements to compete globally.
Executive Action Plan (Next 12 Months)
- Set the AI thesis: Define where AI drives P&L impact in 3 horizons (R&D, quality, supply chain, services). Align capital allocation and exit low-yield bets. For C-suite guidance, see AI for Executives & Strategy.
- Build the SDV backbone: Decide on in-house vs. partner for middleware, OTA, data platforms, and safety cases. Standardise interfaces to speed feature rollout.
- Close the readiness gap: Stand up an AI PMO with model lifecycle, data contracts, and product owners tied to measurable outcomes (throughput, yield, scrap, time-to-release).
- Harden cybersecurity: Implement secure-by-design, SBOM, red-teaming, and incident drills across vehicle, cloud, and supplier touchpoints.
- Rewire for customer value: Tie roadmaps to retention, charging experience, and TCO. Make pricing, financing, and service bundles transparent and regionalised.
- Friendshore with intent: Map critical components, dual-source Tier-1/Tier-2, and build buffer inventory only where risk warrants it. Train supply chain leaders on AI-driven planning via the AI Learning Path for Supply Chain Managers.
- Data governance that ships: Treat data as a product-owners, SLAs, lineage, and access controls. Prioritise high-signal datasets for R&D and quality.
- Talent and partners: Upskill engineers in ML, cybersecurity, and functional safety. Co-develop with chipmakers, cloud providers, and charging networks to accelerate launches.
- Scorecard and cadence: Monthly operating reviews on AI ROI, SDV release velocity, supplier risk, and customer health-no vanity metrics.
KPIs That Matter
- AI impact: R&D cycle time, test coverage automation, yield improvement, scrap reduction, forecast accuracy.
- SDV velocity: OTA cadence, defects per release, time-to-patch for critical vulnerabilities.
- Customer health: Retention, charging satisfaction, cost-per-mile vs. ICE, NPS by segment.
- Supply chain resilience: Days-at-risk for top components, dual-source coverage, order-to-delivery lead time.
- Sustainability: Scope 1-3 intensity per vehicle, recycled content, energy mix at plants.
Context and Further Reading
For broader industry context on AI adoption and SDV trends, see KPMG's automotive insights here. Use this to benchmark your roadmap and pressure-test investment priorities.
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