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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.

Four white and silver pipes intersect, with turquoise liquid inside glass sections, surrounded by cracks and shards suggesting breakage or impact—evoking the precision and complexity of AI engineering simulation in advanced manufacturing environments.

The AI boom is over.
For design and engineering teams, that means the real work has started.

Design and engineering is the most scale-ready audience in our survey. Just 12% haven’t started with AI, the lowest non-adoption of any group. 45% are piloting right now. The frontier has moved from experimentation to daily execution. Faster design iteration is the standout realized benefit, cited by 48%, higher than any other outcome across any other audience. For fluid-handling engineers, the combination of digital simulation and AI isn’t on the horizon. It’s already on the desk.
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:
  • Why design and engineering is the most scale-ready audience, and what that means for your team
  • The human-plus-machine model: why physics-based validation still matters
  • The ROI problem: only 49% of manufacturers measure it rigorously
  • The AI confidence gap and what it signals about front-line adoption
  • The latest macroeconomic outlook across US and DACH markets
  • Our top recommendations for a successful path to AI adoption
Find out how the most AI-mature organizations are turning simulation speed into a competitive edge.
Download Now
0 %
of D&E professionals use AI or are actively piloting it. Only 12% haven’t started.
0 %
measure AI ROI rigorously. Everyone else is going on instinct.
0 %
cite faster design iteration as AI’s top benefit, the highest realized outcome of any audience surveyed.
0 %
say data quality is their top barrier. Scaling AI on bad data doesn’t scale anything.