As automakers shift toward recurring digital revenue models and connected vehicle ecosystems, 74% of executives now believe vehicles will be software-defined and AI-powered by 2035, fundamentally changing how value is created across the vehicle lifecycle.
The writing on the wall is clear: the future of automotive will be written in software, powered by data, and charted by AI.
The challenge, by extension, is no longer whether to invest in data and AI, but how to operationalize them at speed and scale. And despite aggressive investment and bold vision, many automotive organizations are still struggling to translate AI ambition into operational reality.
Software-Defined Vehicles Are Forcing a Data-First Industry Reset
Software-defined vehicles (SDVs) sit at the center of automotive transformation. The IBM Institute for Business Value reports that automakers are nearly tripling R&D investment in software, rising from 21% today to 58% by 2035, while 75% of executives say software-defined experiences will be the core of brand value.
This shift changes everything:
- Vehicles become digital platforms, not static products.
- Over-the-air (OTA) updates replace traditional release cycles.
- Data becomes the foundation for differentiation across the entire vehicle lifecycle.
But the transition is far from simple. Nearly 48% of OEMs and suppliers cite technical difficulty separating software and hardware layers as a major SDV challenge, exposing the limits of legacy architectures built for mechanical engineering rather than continuous digital evolution. These and other challenges point to the necessity of data modernization and AI strategies architected with this paradigm shift in mind.
Optimizing with AI is the Name of the Game
AI has moved from pilot projects to strategic necessity. Across the industry:
- 62% of automotive executives expect generative AI to improve production quality and optimization.
- 72% of automotive CEOs say competitive advantage depends on who has the most advanced generative AI.
- 70% say agentic AI is critical to their organization’s future.
AI is also reshaping every layer of the value chain:
- Engineering: AI-driven simulation, digital twins, and generative design reduce development time and cost.
- Manufacturing & supply chain: Predictive analytics and AI agents optimize scheduling, logistics, and resilience.
- Customer experience: Hyper-personalized infotainment, predictive maintenance, and AI assistants drive loyalty.
Yet maturity trails mbition. While AI-powered supply chain optimization is projected to deliver a 36% ROI by 2027, only 24% of organizations report high maturity. This gap separates early leaders from the rest of the pack, and will only widen if enterprise leaders aren’t strategic and decisive in their efforts to catch up.
Data Is the Constraint Holding AI Back
IBM IBV is clear: AI success depends on data readiness. As vehicles generate exponential volumes of telematics, sensor, infotainment, and operational data, automakers walk a precarious tightrope between managing data securely and turning it into real-time value.
Key findings underscore the issue:
- 72% of automotive CEOs say proprietary data is key to unlocking generative AI value.
- Yet only 23% of organizations have clear guidelines and guardrails for AI-driven decisions.
- Just 32% of organizations are effectively implementing “security by design.”
Siloed data, fragmented IT/OT systems, and inconsistent governance remain the biggest blockers to AI success. Without unified data platforms, AI initiatives stall, trust erodes, and monetization remains elusive.
Hybrid Cloud Is the Backbone of Modern Automotive Operations
To support SDVs, AI, and real-time decision-making, infrastructure matters. IBM IBV highlights hybrid cloud by design as a foundational capability:
- 71% of IT professionals say it’s impossible to realize full digital transformation without hybrid cloud
- Yet only 35% of mission-critical automotive workloads currently run in the cloud
Hybrid cloud enables automakers to break down silos, integrate IT and OT, process data where it performs best (vehicle edge, plant floor, or cloud), and scale AI safely across global operations while meeting regulatory and latency requirements.
Operational Reinvention Is Now a Leadership Imperative
The transformation isn’t limited to technology. It requires rethinking how automotive organizations operate:
- 50% of automotive COOs say productivity gains from AI and automation are so significant that risk must be accepted
- Automotive organizations expect 30% of their workforce to be AI-enabled by 2026, up from just 8% in 2024
- Yet 74% of executives say their mechanical-driven culture is difficult to change
Legacy processes, organizational silos, and talent shortages continue to slow progress — even as expectations for productivity, resilience, and speed increase.
What Automotive Leaders Should Do in 2026: Pragmatic Steps Forward
The vision is clear. The challenge is execution. To move from ambition to impact in 2026, automotive companies should focus on practical, high-leverage actions:
- Build a Unified, AI-Ready Data Foundation: Break down silos across engineering, manufacturing, supply chain, and customer systems. Establish real-time data architectures with embedded governance so AI can scale safely and deliver measurable outcomes.
- Operationalize AI — Not Just Pilot It: Shift from experimentation to production. Tie AI initiatives directly to KPIs like time-to-market, production yield, supply chain resilience, and vehicle lifetime value.
- Design for Hybrid Cloud from the Start: Modernize core platforms using hybrid-by-design principles to support OTA updates, edge analytics, and enterprise AI without sacrificing security or compliance.
- Embed Security and Compliance by Design: Treat cybersecurity, privacy, and regulatory readiness as competitive differentiators. Only 25% of automotive organizations feel prepared for regulatory change. Closing that gap builds trust and resilience.
- Invest in People, Not Just Platforms: Upskill teams, redesign workflows, and embrace automation and agentic AI as force multipliers. This frees human talent to focus on high-value decisions instead of manual processes.
Why Hakkōda Is the Right Partner for the Road Ahead
Executing this transformation requires more than the right data stack. Deep expertise spanning modern cloud and hybrid architectures, industry experience, and pragmatic delivery are equally critical for success.
At the same time, Hakkoda and IBM understand that the ability to balance those competing necessities can be overwhelming for organizations at any level of maturity. That’s why our teams come together to help automotive organizations turn data and AI strategy into operational reality, with proven experience building modern, governed data platforms, accelerating AI adoption across SDVs, manufacturing, and customer experience, and designing hybrid cloud architectures that scale securely.
As the IBM IBV report makes clear, clear winners have not yet emerged. The companies that succeed in 2026 will be those that move decisively, grounding bold ambition in execution excellence. And with Hakkoda as a partner, automotive leaders can bridge that gap and turn the next era of mobility into measurable business advantage.
Want to see what those advantages might look like for your enterprise? Let’s talk today.
