Cindy Dustin serves as the Automotive Industry Marketing Lead at PTC, specializing in GTM strategy, outcome-driven messaging, thought leadership, and executive engagement. Drawing on years of experience shaping messaging for engineering and executive audiences, she brings a practical, business-first perspective to how the industry can deliver faster, more efficient, and compliant innovation in the face of rising software and systems complexity.
On March 3, 2026, automotive leaders from across North America gathered in Dearborn, Michigan at the legendary Automotive Hall of Fame for PTC’s Automotive Executive Exchange (AEE) Detroit—a focused, executive forum designed to tackle the industry’s most pressing challenges.
The exchange featured thought leadership, peer-driven dialogues and real-world strategies for managing rising software complexity, accelerating innovation speed and efficiency, and operationalizing AI across the product lifecycle.
The event brought together executives from leading OEMs, Tier 1 suppliers, and technology partners—including Ford, General Motors, Magna, BorgWarner, PACCAR, TCS, and Deloitte—to share real-world experiences and approaches to transformation.
Across sessions and discussions, a clear reality emerged:
With the unprecedented convergence of pressures—software-defined vehicles, AI-driven engineering, regulatory complexity, and cost constraints—all happening at once. Incremental change is no longer enough.
Themes that defined the conversations
The role of AI and the digital thread
AI was a central theme throughout the event—not as a future concept, but as a practical tool already being deployed across engineering and operations.
However, leaders emphasized a key dependency: The value that AI delivers increases exponentially when built on a foundation of trusted product data. This is where the digital thread becomes critical - connecting requirements, design, validation, manufacturing, and service into a single, traceable flow.
Organizations that achieve this connectivity gain:
- Better visibility into complex systems
- Faster decision-making
- Improved quality and compliance
The key takeaway: AI delivers measurable value when grounded in a strong product data foundation and embedded directly into workflows.
Why traceability and lifecycle integration matter now
Software is redefining the product, increasing complexity and dependencies across hardware, software, and systems. Successfully managing integrations throughout the lifecycle is essential, but it is widely recognized that legacy tools—especially spreadsheets— are the silent killers of traceability, scalability, and efficiency.
Leaders stressed the need to:
- Connect requirements across hardware and software domains
- Maintain alignment across global teams and suppliers
- Ensure compliance with evolving regulatory standards
The growing focus on ALM–PLM integration reflects this need—linking software and hardware development into a unified lifecycle environment.
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Across discussions, leaders repeatedly highlighted that many of today’s delays, cost overruns, and quality issues are not caused by lack of innovation—but relying on disconnected systems, manual processes, and siloed workflows. Speed and efficiency are no longer driven by effort or additional tools, but by eliminating fragmentation through standardization and digital transformation.
Forward-looking organizations are moving away from:
- Highly customized processes per program or team
- Tool-by-tool optimization
- Localized engineering decisions
And toward:
- Standardized development frameworks across programs and platforms
- Common data models and architectures
- Reusable assets and processes
The result: Faster execution, reduced complexity, and scalable innovation, with speed emerging not from working harder, but from standardized processes and a unified, data-driven foundation.
How leading companies are executing today
The most powerful moments came from customer and partner-led sessions, where companies shared how they are operationalizing transformation at scale.
- Tenneco detailed its rapid, enterprise PLM rollout, building a centralized digital backbone to eliminate silos, standardize tools, and enable more efficient global engineering collaboration. Leveraging out-of-the-box capabilities to avoid customizations reinforced their core value of simplification.
- Rivian discussed the challenges of fragmented systems and the company’s efforts to unify data and leverage AI for predictive modeling and problem-solving. The session showcased how AI is already driving measurable impact in service and supply chain planning, demonstrating how intelligent automation and deeper data analysis in service parts management can improve operational performance in real-world scenarios.
- PACCAR shared its shift from manual, disconnected processes to a modern, intelligent product lifecycle, to manage complex truck configurations, global change processes, corrective action tracking, and automation. PTC solutions help PACCAR improve quality, reduce parts complexity, accelerate engineering workflows, and create a stronger digital thread across development.
- Deloitte highlighted how software-defined vehicles are intensifying OEM-supplier complexity, requiring stronger digital requirements management, traceability, compliance, and change control. Platform-based development, product line engineering, feature management, and architecture modeling can improve collaboration, reduce rework, accelerate validation, and help suppliers and OEMs manage evolving regulations and program dependencies.
- TCS discussed the growing complexity of software-defined vehicle development, emphasizing integrated requirements, software, validation, configuration, and AI model management. The session highlighted customer transformation challenges, the need for traceability across platforms and releases, and opportunities to improve speed, reuse, and outcomes through structured discovery and integrated engineering practices.
Across these examples, a consistent pattern emerged - transformation is driven by simplification, standardization, AI, and a unified data foundation.
What leaders are prioritizing now
While strategies differ, the direction is clear: automotive leaders are focused on improving efficiency and collaboration, modernizing legacy systems, and embedding AI into everyday engineering workflows to keep pace with growing complexity and competition.
Organizations are focused on:
- Modernizing legacy systems and eliminating spreadsheet-driven processes
- Standardizing development approaches to improve speed and scalability
- Embedding AI into everyday workflows
- Building connected digital foundations to support SDV complexity
These are not future initiatives - they are active transformation programs already underway.
The bottom line
The Detroit exchange reinforced a defining truth for the automotive industry: Innovation alone is no longer enough—execution at scale is what differentiates leaders.
The companies moving ahead are those that can:
- Manage software-driven complexity across the lifecycle
- Connect data and processes across engineering and operations
- Leverage AI where it delivers measurable impact
- Deliver faster—without compromising quality or compliance
The future of automotive belongs to organizations that can efficiently manage software-driven complexity, scale AI adoption, and accelerate innovation through a connected, intelligent product lifecycle.
Thank you to our attendees and speakers
Thank you to all attendees and speakers who joined us in Detroit. Your collaboration and shared expertise made this exchange truly impactful—especially the real-world insights from industry leaders that turned discussion into actionable value.
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