The Value of AI in Automotive Development

What Is AI in the automotive industry and why is it important?

As the automotive industry transforms into a technology-driven ecosystem, Artificial intelligence (AI) is becoming a strategic enabler across the entire product development lifecycle. From smarter engineering and faster testing to data-driven decision-making and enhanced customer experiences, AI is no longer a futuristic concept—it's a competitive necessity.

By augmenting human capabilities and automating complex tasks, AI empowers OEMs and suppliers to build better products, faster, and with fewer errors.

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How is AI transforming automotive development?

AI is an accelerator unlocking insights and automating workflows, enabling people to work smarter and faster. It enables knowledge to be scaled across disparate systems, providing access to institutional knowledge. AI reduces the effort of repetitive tasks such as documenting, formatting, and checking work. Ultimately, it unlocks innovation by providing time-saving efficiencies for engineers, so they can focus on more creative and collaborative work.

AI turns complexity into clarity through:

Requirements management/validation

• Requirements assistant: Enhances efficiency and focuses on customer needs

• Ensures requirements meet industry standards with automated quality checks
• Improves requirement quality with AI-powered rewrite suggestions
• Simplifies review and content updates with an intuitive editing experience

AI can be used to check for quality, suggest improvements, and generate compliant content.

• Test case authoring: Validates that designs and products satisfy requirements

• Speeds up authoring and standardizes authoring
• Derives test cases based on requirement content
• Grounds content in guidelines and project data

AI can derive structured, high-quality test cases directly from requirements and project data—faster and more consistently.

Optimize design

• Design automation assistant: Reduces repetitive tasks to improve productivity

• Performs complex and repetitive tasks using runtime scripting based on user prompts
• Provides real-time, context-aware assistance by leveraging existing knowledge bases
• Identifies system bottlenecks, troubleshooting issues, and design efficiencies

An AI assistant provides real-time answers to questions and automates repetitive CAD tasks, saving time and reducing errors in the design process.

• 3D model optimization: Explores and evolves design options for better solutions

• Uses algorithms and computational methods to explore a variety of design options based on specific constraints and objectives
• Considers parameters such as material property, manufacturing methods, and performance

AI can be used to instantly generate and evaluate multiple geometry alternatives based on performance, cost, and manufacturability.

Parts rationalization

• Parts reuse and classification: Identifies and reduces duplicate part creation

• Searches for similar parts during design to reduce duplicate creation
• Finds duplicates across your data to reduce carrying costs
• Streamlines classification with AI assistance to assign part categories and attributes

AI can be used to find existing components, detect duplicates, and recommend reusable components earlier in the development process.

• Document insight access: Quickly access vital knowledge in your document vault
• Enables self-serve questions and answers from compiled data
• Summarizes lengthy documents for deeper insights
• Easily uncovers answers that aren't readily available in standard reports or the application interface

AI can be used to provide instant answers from technical documents, making critical information easier to access and act on.

The challenges of adopting AI into the automotive sector

While AI offers game-changing potential in automotive product development, the path to adoption is not without roadblocks. Many OEMs and suppliers are eager to capitalize on AI’s promise—yet legacy systems, cultural barriers, and technical constraints often slow momentum.

Here are the key challenges many automotive leaders must navigate to unlock AI’s full value:

Legacy infrastructure and siloed data

AI thrives on data, but traditional automotive development relies on fragmented systems and disconnected toolchains. Engineering, manufacturing, and quality data are often locked in silos, making it difficult to train robust AI models. Incomplete datasets limit AI accuracy and scalability.

Skills gap and organizational readiness

AI adoption requires a blend of domain expertise and data science skills—a combination many automotive teams are still building. Engineers may lack AI training, while AI practitioners may not fully understand vehicle systems or safety standards. Misalignment slows development and weakens model effectiveness.

Safety and regulatory concerns

Automotive is a safety-critical industry, governed by standards like ISO 26262, ASPICE, and UNECE R155/156. AI systems—especially those using deep learning—can behave like black boxes, making them difficult to verify and certify. Navigating the complex web of global automotive regulations is challenging. AI implementation must align with these regulations to ensure compliance and avoid costly penalties.

Integration with traditional development models

Most automotive programs still rely on V-model, systems engineering, and gated milestones, which don’t always align with AI’s iterative, data-driven nature. Embedding AI into these workflows requires process rethinking and toolchain alignment. AI initiatives remain isolated from core vehicle programs.

Unclear ROI and pilot fatigue

Many AI pilots struggle to move beyond proof of concept. Without clear KPIs and business alignment, organizations face “pilot purgatory”—where experiments don’t scale or deliver measurable value. Innovation fatigue sets in while funding and executive support decline.

Benefits of using AI in automotive development

Accelerated time to market

Shorten the overall design cycle and bring innovative products to market faster. Enabling smarter, faster decision-making, AI can process vast volumes of engineering data, surfacing actionable insights in real time.

Strengthened quality and safety

By detecting redundant data and assessing content for consistency and completeness, AI helps ensure products meet stringent safety and compliance standards. AI helps support ISO 26262, ASPICE, and regulatory audit readiness, while improving first-time quality.

Boost productivity and minimize errors

AI automates complex workflows and manual, repetitive tasks like requirements classification, model-based code generation, and test case generation, freeing engineers for higher-value work.

Increase software reliability

Enhanced software testing and validation reduces test effort and ensures faster certification cycles. AI can predict defects, prioritize test cases, and simulate real-world scenarios—including cases for ADAS and autonomous driving.

Cost reduction and resource optimization

AI lowers development costs and shortens time to market, while maximizing ROI through more efficient use of materials, development resources, and time by optimizing product architectures, supply decisions, and production plans.

Elevate user experience

Support and guide users through complex processes with embedded generative and agentic AI, reducing frustration and increasing successful outcomes.

The future of AI in the automotive industry

AI isn’t just another tool—it’s a strategic capability that can redefine how the automotive industry approaches product development. When embedded thoughtfully across the lifecycle, AI empowers organizations to:

• Deliver better vehicles, faster
• Respond dynamically to market and regulatory shifts
• Create sustainable, scalable engineering processes

The future will see AI enhancing vehicle connectivity, pushing the boundaries of autonomous driving, and supporting sustainable practices through optimized resource usage and reduced emissions.

The integration of AI in automotive engineering is not just a trend but a necessity for OEMs aiming to stay competitive in this rapidly evolving industry. Embracing AI will enable automotive leaders to accelerate innovation, improve operational efficiency, and deliver products that meet the changing demands of the global market.

For further insights into how AI is shaping the future of the automotive industry, visit PTC's AI page.

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