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Strategic deployment of generative AI in manufacturing will unlock US$10.5B added revenue by 2033

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Generative AI

The growth of generative AI has been remarkable, with major tech firms like Microsoft recently allocating $10 billion to OpenAI. The enthusiasm for applying generative AI in the manufacturing sector stems from its potential to revolutionize various aspects, from designing new products to transforming entire production processes. According to a report by ABI Research, manufacturers can link their investments in generative AI to a substantial revenue increase, projected to surge by $4.4 billion between 2026 and 2029. By 2033, the additional revenue generated from the implementation of generative AI in manufacturing is forecasted to 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,” explains James Iversen, Manufacturing and Industrial Industry Analyst at ABI Research.

The most rapid adoption in mainstream applications is expected in the design sector, where use cases like generative design and streamlining manufacturing bill of materials (MBOM) and electrical bill of materials (EBOM) processes already have established solutions offered by companies like Siemens and Microsoft. On the other hand, applications in engineering, production, and operations will take more time to develop and mature, as they involve complex tasks and necessitate additional training for generative AI models.

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 make top-performing use cases a priority as they provide the highest returns and can be easily developed 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 advises.

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