AI is everywhere in the conversation about engineering today, but how far is it actually in the practice of engineering?
That’s the question that led us to commission our latest global survey: the State of Engineering AI 2025.
We spoke to 300 senior engineering leaders – CTOs, VPs of Engineering, Simulation leaders – across the US and Europe, to understand how prepared engineering organizations really are to adopt and scale AI in their design engineering and simulation workflows.
The results are fascinating, and for me, both a clear validation of the opportunity and a sharp reminder of where the real work lies.
AI Ambition Is Not the Problem – Execution Is
The headline is simple:
- 93% of leaders expect AI to drive productivity gains.
- 30% expect those gains to be “very high”.
- But only 3% say they are achieving that level of impact today.
This is a massive gap (10:1) between current ambition and experience – what we’re calling the Engineering AI expectation v. execution gap. It’s also not unique to Engineering, many industries go through this phase. But the depth of this gap in Engineering is shaped by some very specific challenges:
Why Engineering Is Different
Unlike fields where large-scale public data and cloud-native workflows are the norm, engineering teams face structural barriers that AI alone cannot magically remove:
- Siloed data: 55% of leaders cite fragmented, inaccessible data as the top barrier to AI progress.
- Legacy tools: 42% cite the limitations of traditional desktop CAE tools. Many workflows are not cloud-native or even cloud-connected.
- Leadership disconnect: Interestingly, 42% of CTOs perceive significant resistance to AI adoption within their teams, but engineering leaders themselves report this only 25% of the time. In other words: many teams are more AI ready and enthusiastic than leadership assumes.
And finally, engineering data itself is often fundamentally harder to leverage for AI than the text or image data used to train other types of foundation models. This is why I believe the evolution of Physics AI and Engineering AI will take a path that is very much grounded in accelerating the adoption of cloud-native tech stacks across engineering workflows.
What the Leaders Are Doing Differently
The good news is that our survey clearly shows a cohort of engineering leaders who are already achieving transformational results.
These teams share several traits:
- They have modernized their toolstack – favoring cloud-native, open platforms.
- They have invested in ensuring centralized, clean engineering data is captured across workflows – not perfectly, but enough to enable scalable AI.
- They are building and integrating AI agents directly into live workflows – not as bolt-on tools, but as embedded decision-makers at setup, evaluation, and optimization stages.
- They have moved from pilots to production-grade AI use cases that drive real business value (faster design cycles, improved product performance, faster time to market) – rapidly, with confidence, and with clear mandates.
- Critically, they have fostered strong alignment between leadership and engineering teams -AI initiatives are not being led in isolation.
Cloud-native users in our survey are 3x more likely to have mature AI programs and 6x more likely to have clean, centralized data – and they are twice as confident they’ll achieve their AI goals in the next 12 months. It’s clear that confidence in AI follows capability with cloud-native CAE tooling, rather than the other way around.
Where We Go From Here
One of my favorite lessons from the many conversations with engineering leaders had while creating this report is simple:
👉 Don’t aim to “do AI.”
👉 Aim to solve engineering problems better – with AI as a transformational enabler.
Teams that start with a clear, high-impact use case – where collapsing a process from days to seconds changes outcomes – make the fastest progress. Engineering AI is not about replacing engineers. It’s about creating machine-in-the-loop workflows that supercharge engineering creativity and productivity.
The goal is not to bypass human insight, but to multiply it, to deliver unseen levels of engineering innovation.
My Call to Action for Engineering Leaders
If you are a VP of Engineering, CTO, or simulation leader reading this:
✅ Be aware of the expectation-execution gap – don’t let your organization be part of the “93% hoping, only 3% achieving” statistic.
✅ Look hard at your toolstack, your data readiness, and the leadership alignment needed to move forward. Does your legacy tooling hinder or help AI adoption?
✅ Start with one high-value application and push hard; prove out the impact, then scale with confidence.
And above all: the time to start is now. Engineering AI is no longer a future vision or add-on capability, it is a fundamental enabler and accelerator, and is already transforming how some teams design and innovate today.
We created this report not just to benchmark the market, but to help drive the conversation forward. I encourage you to read it, and more importantly, to act on it!
I look forward to hearing what you think.
David
