AI in Composites

What's Inside?

• Core Contribution Areas: An analysis of how AI is driving massive gains across efficiency (80% waste reduction in AFP), design velocity (80% faster mold design for fuselage sections), and production quality (96% first-time-right yields).

• Expert Perspectives: Opinions from industry experts on which applications will see the highest impact and what the realistic adoption timeline looks like for the broader market.

  Company Capability Mapping: The study provides a high-level mapping of how different AI solution providers for the composites industry are positioned.

• Navigating Adoption Challenges: Critical hurdles currently slowing integration, including the specialized skill gap, data fragmentation, and the regulatory "step changes" required for aerospace certification.

• The Digital Thread: Why connecting fragmented data across design, material management, and inspection is the only way to achieve system-level intelligence.

This report provides a comprehensive evaluation of the role of Artificial Intelligence in the composites industry. As the manufacturing sector sits at less than 10% AI adoption despite high potential, this study maps the shift from manual, stage-centric processes toward integrated, system-level intelligence. 

The report highlights a significant paradox:

While the outputs of the composites industry are found in the world's most advanced machines, the underlying manufacturing processes remain heavily reliant on manual labour and lack the automation seen in more mature industries. Current investment has been disproportionately directed toward generative AI, leaving a substantial "Blue Ocean" opportunity for broader industrial and operational applications in composites.

A defining insight of this study is that the true value of AI lies in connecting isolated tasks. Many of today s inefficiencies stem from fragmented decision-making between the design, materials, processing, and inspection stages. By creating a continuous digital thread, manufacturers can move from local optimizations to global manufacturing outcomes, enabling predictable quality and faster industrialization.

Looking ahead, the emergence of quality-aware digital twins and virtual manufacturing ecosystems signals a transition toward a more predictive paradigm. As AI becomes embedded across the value chain, the distinction between simulation and production will increasingly blur, allowing decisions to be validated virtually before they are executed physically.