Strategic Deployment of Generative AI in Manufacturing Will Unlock $10.5B Added Revenue by 2033

Generative AI could scale, create new designs and ultimately overhaul entire production processes.


Generative AI has been growing at a significant rate, with notable technology companies such as Microsoft investing $10 billion into OpenAI this year. The excitement around generative AI in manufacturing comes from building out potential use cases, scaling from creating new designs to ultimately overhauling entire production processes.

According to global technology intelligence firm ABI Research, manufacturers can tie investments in generative AI to additional revenues with a significant spike of $4.4 billion from 2026 to 2029. By 2033, revenue added from the use of generative AI in manufacturing will reach $10.5 billion.

“Generative AI has growth that will derive from functionality and use cases across market verticals. The deployment of generative AI will come in three waves as the technology matures, with manufacturing seeing the largest revenue growth during the second and third waves. During the second and third waves of adoption, generative AI will be deployed into four domains of manufacturing: design, engineering, production and operations,” explained James Iversen, Manufacturing and Industrial Industry Analyst at ABI Research.

Design will see the fastest mainstream deployment with use cases such as generative design and MBOM (manufacturing bill of materials) and EBOM (electrical bill of materials) reductions already having existing solution offerings from companies such as Siemens and Microsoft. Engineering, production and operations use cases will take longer and require further maturity from generative AI providers due to the complexity of the tasks and required model training.

Use cases for generative AI in manufacturing can be compared by looking at expected TTV (time to value) and ROI (return on investment). For the four domains, the top performers are:

  • Design: Generative design, part consolidation
  • Engineering: Tool path optimization, part nesting
  • Production: Product quality root cause analysis, correction of bugged software code
  • Operations: Inventory stock and purchasing period management, employee work path optimization

Both manufacturers and manufacturing software providers should prioritize top-performing use cases as they yield the highest returns and can be easily built out with existing generative AI capabilities.

“Starting from the ground up, implementing these use cases will lay the groundwork for more extensive use cases. It is important not to jump the gun and develop high-functioning use cases that will see little implementation as trust in generative AI will need to be built up before overhauling significant portions of current manufacturing operations,” Iversen advised.

Manufacturers and manufacturing software providers that are initiating use cases are BMW, Boeing, ByteLAKE, General Motors, Markforged, Nike, NVIDIA and SprutCAM X with the help of generative AI companies such Nike’s Celect, Gradio, OpenAI, Retrocausal, Work Metrics and Zapata AI.

These findings are from ABI Research’s Generative AI Use Cases in Manufacturing report. This report is part of the company’s Industrial and Manufacturing Technologies research service, which includes research, data and ABI Insights. Based on extensive primary interviews, Application Analysis reports present an in-depth analysis of key market trends and factors for a specific application, which could focus on an individual market or geography.

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