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You Can't Scale AI on Ambition Alone. 

Adoption is settled. Turning it into results is not. New research from 500 manufacturing technology leaders shows what separates manufacturers who scale from those who stall.
Architectural blueprint with yellow highlights, featuring building outlines, structural details, and curved roadways intersecting the layout—integrated elements suggest innovative solutions such as AI for Metal Fabrication shaping the design process.

AI is on the shop floor.
For metalworkers and fabricators, what comes next is practical.

In the metal and steel industry as in design and engineering, only 12% of professionals aren’t using or piloting AI, the lowest non-adoption of any audience we surveyed. The ones already using it report gains worth paying attention to, with faster design iteration cited as the top realized outcome (48%). On the fabrication floor, reported gains include significantly less manual work in routine drafting and press-brake jobs that once took ten minutes running in under two.

This report tells you what’s happening on the ground where AI is actually working, where it isn’t, and what the organizations pulling ahead are doing differently.
What you’ll find inside:
  • Where AI in design and engineering is already producing results, no hype
  • The human-plus-machine model: why AI augmentation works, and full automation doesn't (yet)
  • What 500 manufacturing decision-makers say about ROI, barriers, and what comes next
  • The AI confidence gap: executive optimism vs. front-line reality
  • The latest macroeconomic outlook across US and DACH markets
  • Our top recommendations for a successful path to AI adoption
Find out where AI is already saving hours on the shop floor and what it takes to get the same results.
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of industry professionals use AI or are actively piloting it.
0 %
cite faster design iteration as AI’s top benefit, the highest realized outcome of any audience surveyed.
UpTo
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less manual work in some fabrication workflows. Press-brake jobs: ~10 min → under 2 min. (Trade-reported figures.)
0 %
say data quality is their #1 barrier. Getting the data right is what separates the teams that scale from the ones that stall.