Artificial Intelligence Expected To Add 10 5 Billion To Manufacturing By 2033 64fb5a86a28d5

Artificial intelligence expected to add $10.5 billion to manufacturing by 2033

Sept. 15, 2023
Generative AI is growing because participants are building potential use cases and scaling from creating new designs to overhauling production processes

Investments in generative artificial intelligence (AI) are expected to increase manufacturing revenues by $4.4 billion during 2026-29, and continue onward to reach $10.5 billion by 2033, according to a recent report, “Generative AI use cases in manufacturing,” by ABI Research. It reported Aug. 30 that generative AI is growing because participants are building potential use cases, and scaling from creating new designs to overhauling production processes.     

“Generative AI has growth that will derive from functionality and use cases across market verticals,” says James Iversen, manufacturing and industrial industry analyst at ABI. “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.”

ABI projects the design field will see the fastest mainstream AI deployment. It adds that use cases, such as generative design, manufacturing bill of materials (MBOM) and electrical bill of materials (EBOM) reductions, have already generated design products by companies, such as Siemens and Microsoft. ABI adds that engineering, production, and operations use cases will take longer and require further maturity from generative AI providers due to the task complexity and required model training.

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

  • Design—generative design and part consolidation;
  • Engineering—tool path optimization and part nesting;
  • Production—root-cause analysis of product quality and correcting buggy software code; and
  • Operations—inventory stock and purchasing period management, as well as employee work path optimization. 

API adds that manufacturers and manufacturing software providers initiating use cases include BMW, Boeing, ByteLake, General Motors, Markforged, Nike, Nvidia and SprutCam X. They’re aided by generative AI companies, such as Nike’s Celect, Gradio, OpenAI, Retrocausal, Work Metrics and Zapata AI.

API concludes that manufacturers and manufacturing software providers should prioritize top-performing use cases because 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,” explains Iversen. “It’s important not to jump the gun and develop high-functioning use cases that will see little implementation because trust in generative AI will need to be built up before overhauling significant portions of current manufacturing operations.”  

About the Author

Jim Montague | Executive Editor

Jim Montague is executive editor of Control.