The State of AI in Product Development: What CIMdata’s AI in PLM Global Study Reveals

• Am I Behind?" Anxiety: The non-stop flow of AI announcements makes our leadership team feel like they are lagging behind, but a lack of hard, independent data makes this fear impossible to calibrate.
• Hype Outrunning Reality: Provider roadmaps, massive productivity claims, and "agentic AI" messaging are moving significantly faster than our engineering organization can actually deploy and validate.
• Over-Promising and Under-Delivering: Our #1 concern is how difficult it is to evaluate solution provider claims without an objective, outside reference point.
• The "Build vs. Buy" Dilemma: It is hard for us to decide whether to wait for embedded features from current PLM providers, build custom tools internally, or stitch together capabilities from multiple third-party sources.
• Where to Invest First: It is unclear which AI application areas deliver tangible value today and which are still purely aspirational, making it hard to know where to allocate budget.
• Hidden Readiness Gaps: Data quality, governance, and change management are widely underestimated prerequisites for AI success. These are foundational issues and won't fix themselves just by waiting.
• Trust and Validation Bottlenecks: Because our engineering decisions require absolute precision, there is limited confidence in validating AI outputs. This makes moving from a pilot project to full-scale production incredibly hard for us to justify.

• The AI Journey: Where your peers actually stand, from initial awareness and pilots to operationalizing and scaling.
• Real-World Application & Blindspots: Exactly where your peers are applying AI across the lifecycle, and which product development areas remain completely untouched.
• Unexpected Value: Examples of the benefits early adopters are realizing today, including unexpected wins in design innovation and compliance.
• The True Bottlenecks: Why data readiness, not technology, is the dominant constraint slowing down adoption.
• Trust & Validation: How confident your peers are that they can safely rely on AI outputs for engineering decisions.
• Sourcing & Selecting: The factors driving AI provider selection and the providers’ industrial companies want leading their implementations.
• Actionable Next Steps: The webinar will offer you four practical implications for how to pace, select, govern, and lay the foundations for AI in your product development.

This webinar is designed for anyone making or influencing AI decisions in product development, including:

• Engineering & R&D Leaders: Evaluating AI for product development and seeking actual productivity gains.
• PLM Program Managers & Strategists: Architects tasked with integrating AI into the digital thread without creating new silos.
• IT & Digital Transformation Executives: Leaders assessing "Build vs. Buy" trade-offs and enterprise infrastructure requirements.
• Digital Operations Leaders: Those assessing AI's role in streamlining product lifecycle and cross-functional processes.
• Solution Providers & Consultants: Anyone needing a consistent framework to help clients evaluate competing AI implementation patterns.