Case studies suggest continued but selective investment, with partnerships and open-source or alliances to mitigate costs while pursuing AI-driven efficiency.

Overall, AI in automotive is in a maturity cycle: hype fading into a disciplined, ROI-focused phase where only well-prepared players accelerate and dominate.

The retreat in AI investment could lead to consolidation and a widening performance gap, with AI capabilities concentrating among a small group of leaders in predictive maintenance and advanced driver-assistance systems.

AI efforts will include in-car systems that learn driver preferences and adapt vehicle behavior, with software- and data-led organizations at the forefront.

Notable 2025 partnerships include Hyundai with NVIDIA, GM embedding NVIDIA AI, Honda and Helm.ai collaboration, and Stellantis with NVIDIA, Uber, and Foxconn on global robotaxi development and autonomous mobility initiatives.

Legacy automakers must pursue digital-first transformations, including direct reporting lines for software leadership to the CEO and removal of internal barriers to AI adoption.

A digital-first restructuring, led by software leadership reporting to the CEO, is essential for accelerating AI adoption in traditional automakers.

Analyst Pedro Pacheco highlights the need for legacy automakers to shed old mindsets and embrace a software-led, CEO-driven approach to AI initiatives.

Weak software infrastructure and data maturity remain major barriers to scalable AI in automotive, impacting reliability and commercial viability.

Executives who prioritize AI over traditional automotive concerns and who have software and data experience are more likely to gain a competitive edge in this transition.

The firms most likely to stay ahead will build solid software foundations, deploy tech-savvy leadership, and maintain a long-term, disciplined focus on AI, a combination that could widen the AI leadership gap.

In the end, roughly five percent of automakers are expected to sustain heavy AI investments through the decade, with most abandoning AI efforts within five years due to unrealistic returns.

Automation in vehicle manufacturing is on track to advance, with at least one automaker running a fully automated assembly line by 2030 and nearly half of top automakers piloting advanced robotics to reduce labor costs and shorten production cycles.

Nearly half of the world’s top 25 automakers are already piloting advanced robotics, signaling potential improvements in quality and production speed.

Industry experts say success will require removing internal barriers and placing technology at the highest levels to sustain AI momentum.

Geopolitical and regulatory pressures, including emissions rules and China-West trade frictions, add uncertainty and may divert resources from AI toward compliance and strategic realignments.

Cost pressures from EV development, battery supply chains, and factory modernization will tighten AI budgets unless fast, measurable returns are demonstrated.

In China, XPeng is highlighted as a dominant AI integrator with the XPILOT 5.0 system, offering Level 3+ autonomy, voice-controlled AI, and data-center collaboration for real-time learning.

The consensus is that current AI enthusiasm will give way to disappointment as firms underestimate the extensive training and foundational work needed beyond basic AI.

Legacy automakers like Volkswagen are trying to catch up with software-driven rivals such as Tesla and BYD, highlighting a widening gap between hardware-centric incumbents and tech-driven newcomers.

Gartner plans to publish additional automotive forecasts through 2026, noting its AskGartner AI tool is used by clients for guidance on AI strategy.

The broader implication is a consolidation phase in the global auto industry, with strategic partnerships and targeted investments focusing on AI-enabled manufacturing, autonomy, and software-defined vehicles.

Author attribution: Rajani Baburajan.

Gartner forecasts only a small fraction of automakers will sustain strong AI investment growth by 2029, dropping to about 5% from today’s levels.

Today, more than 95% of automakers are in a high-enthusiasm cohort for AI, but momentum is expected to wane as projects fall short of expectations.

AI adoption in the automotive sector is rising in a wave of enthusiasm, with Tesla leading in AI integration via its Full Self-Driving system that uses real-time data from millions of vehicles.

Despite the slowdown, automated vehicle assembly is expected to advance toward 2030 as robotics adoption grows, though broader AI programs may retract outside factories.

Gartner emphasizes that internal software expertise enables faster AI adaptation, while reliance on external suppliers can slow progress; leadership with technical know-how will influence investments.

Automation will shift plant job roles toward AI oversight, robotics maintenance, and software development, with reskilling contingent on actions by automakers and policymakers.

Gartner stresses building robust software foundations, breaking down silos, and embedding AI across operations rather than treating it as a standalone initiative; strong software talent and long-term commitment are essential.