Despite very high hopes, engineering firms are overwhelmingly failing to get much in the way of productivity gains from AI.
While 93% of engineering leaders expect AI to deliver productivity gains - and 30% anticipating very high gains - only 3% report achieving that level of impact.
However, according to the State of Engineering AI 2025 report from SimScale, organizations using cloud native simulation tools are twice as confident about achieving their AI goals within the next 12 months.
Notably, these firms are three times as likely to have mature AI programs, and six times as likely to have clean, centralized data.
"Engineering leaders see the potential of AI — but knowing isn’t doing,” said David Heiny, CEO at SimScale. “The challenge is no longer about believing in AI’s promise, but about overcoming the very real systemic blockers that stop teams from scaling it successfully.”
The biggest barriers, as with most industries, are siloed data and legacy tools, the study found. More than half (55%) cite siloed data and 42% legacy desktop computer aided engineering (CAE) tools as major blockers to their AI ambitions.
Meanwhile, 42% of CTOs complained about resistance to AI adoption within technical teams. However, engineering team leaders themselves reported resistance just 29% of the time.
This, SimScale noted, suggests that technical teams are more open, ready and motivated to adopt AI than leadership assumes.
AI as a growth driver in engineering
In terms of hopes, AI is seen as a growth driver, as well as a tool for more efficiency. Engineering leaders expect AI to fuel greater design innovation (54%), engineering productivity (51%), and faster time to market (47%). Reduced costs rank lowest on the list of expected benefits.
As for the lucky 3% who are seeing significant productivity gains, SimScale said it's all about project execution, with four key traits in common.
First and foremost, they have eliminated siloed, desktop-era toolchains in favor of cloud-native platforms, and their engineering data is centralized, accessible and structured, using open formats and APIs.
These companies also have integrated agentic workflows, building and integrating AI agents directly into live workflows — not as bolt-on tools, but as embedded decision-makers at the setup, evaluation, and optimization stages.
Similarly, successful deployers test in low-risk settings, but move quickly to real-world, in-the-loop deployment. This, the study noted, delivers value in weeks, not years.
“Forward thinking teams are proving that engineering AI can deliver significant changes in innovation and performance," said Jon Wilde, VP of Product at SimScale.
"The execution gap for others is not technical feasibility — it’s architectural and organizational readiness. Now it’s about helping those companies make that leap with confidence —before the gap becomes too wide to close."
Engineering firms aren't the only ones to find AI productivity gains turning out to be less than expected.
Research from workflow management platform Wrike earlier this year, for example, found that botched adoption strategies mean many UK enterprises are still facing critical inefficiencies and rising workloads.
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