Everyone's talking about AI in engineering workflows. But here's what nobody's talking about: the massive adoption gap between different types of engineers.
EETech’s annual study of over 1,700 engineers revealed something striking: while 56% of engineers are currently using AI in their workflows, the how and where they're using it varies dramatically between control engineers and electronics engineers. And perhaps more telling—the 21% who aren't using it have very different reasons for staying on the sidelines.
Let's start with the headline: 56% of engineers are actively using AI in at least some part of their workflow right now. Another 23% aren't using it yet but are planning to. That leaves only 21% who are actively not using AI with no immediate plans to change.
On the surface, that looks like a pretty enthusiastic embrace of AI technology. But when you dig into where and how engineers are using AI, you start to see a more nuanced picture.
When asked which phases of the product development cycle they're using (or planning to use) AI, here's what emerged:
Electronics Engineers are diving deep into design:
Control Engineers are playing it safer:
Notice the difference? Electronics engineers are using AI right at the creative heart of their work—the conceptual design phase where they're making fundamental architectural decisions. Control engineers are more likely to use AI for documentation—important work, but less mission-critical.
Looking across all the development phases, a clear pattern emerges:
Electronics engineers are using AI for innovation:
They're comfortable letting AI help them explore possibilities, generate alternatives, and push boundaries during the creative phases of design.
Control engineers are using AI for execution:
They're more comfortable using AI for well-defined tasks with clear success criteria—writing documentation, generating code, validating against specifications.
This isn't about control engineers being technophobes or electronics engineers being reckless. The difference likely comes down to the nature of their work:
Electronics engineers often work in fast-moving product categories where time-to-market is everything. They're designing consumer devices, IoT sensors, and communications equipment where getting version 1.0 out the door quickly can matter more than perfecting every detail. AI that helps them explore design alternatives faster? That's valuable.
Control engineers work in industrial automation, energy infrastructure, and process control—domains where reliability and safety aren't just nice-to-haves, they're legally mandated. A failed consumer electronic device is annoying. A failed industrial control system can shut down a factory or worse. They're naturally more conservative about letting AI make creative decisions in critical systems.
Interestingly, 14% of control engineers and 12% of electronics engineers said they're using AI across all phases of the product development cycle. These early adopters aren't just dabbling—they've fully integrated AI into their workflows.
Who are these engineers? Likely:
The engineers who aren't using AI—and aren't planning to—aren't just skeptics or luddites. Based on the broader data in our study, here's what's likely holding them back:
Concern about reliability: In fields where documentation and support matter (and the data shows they matter a lot), engineers need to trust their tools. AI that occasionally hallucinates or generates plausible-but-wrong answers is a dealbreaker.
Integration challenges: Engineers already use complex toolchains. Adding AI tools that don't integrate smoothly with existing CAD, simulation, or documentation systems creates friction.
IP and security concerns: Especially in regulated industries, sending proprietary designs through cloud-based AI systems raises red flags.
"I don't see where it helps": For experienced engineers with established workflows, AI needs to solve a real problem—not just be a shiny new toy.
Here's the thing: even among engineers who aren't using AI today, 23% are planning to adopt it. That suggests we're still in early innings.
As AI tools become:
That 21% "not using, not planning" group will likely shrink. The question isn't whether AI will become standard in engineering workflows—it's how fast, and for which tasks.
If you're managing engineering teams or developing AI tools for engineers, here's what the data suggests:
For Electronics Engineering Teams:
For Control Engineering Teams:
For AI Tool Developers:
AI adoption in engineering isn't a simple yes/no question. It's a spectrum, and where engineers fall on that spectrum depends heavily on their field, their risk tolerance, and the specific problems they're trying to solve.
Electronics engineers are charging ahead, using AI to accelerate design cycles and explore more alternatives. Control engineers are moving more cautiously, focusing on well-defined tasks where AI can augment—not replace—human judgment.
Neither approach is wrong. They're both rational responses to different engineering realities. The winners will be the companies and engineers who figure out where AI genuinely helps—and where it's just hype.
This analysis covers AI adoption, but the complete 2025 Engineering Insights Report reveals how engineers work across every phase of product development:
Download the full 2025 Engineering Insights Report →
This analysis is based on the 2025 Engineering Insights Report, a global study of over 1,700 qualified control and electronics engineers conducted by EETech.
.svg){kind=icon}
