A survey of 350 engineering leaders in organizations with 1,000 employees or more has revealed a widening gap in AI adoption. Of those surveyed, only 9% have a mature, scaling AI program in place, while 80% are working on limited deployment pilots, 8% are planning to start this year, and 3% have no plans to start at all. This is according to the ‘State of Engineering Report’ from AI-native cloud simulation platform, SimScale.
The survey results show that many companies planning to start AI projects last year (44% in 2025) have now taken the next step and started pilots (80% in 2026). However, the speed of progression from a pilot program to a large-scale deployment tells a different story. In 2025, 7% of organisations had mature, scalable AI programs. In 2026, that number has only increased by 29%, to 9%.
The speed at which engineering organisations are scaling AI into engineering workflows is also a key differentiator. According to the survey, the average deployment timeframe from pilot to mature AI (or agentic AI) is eight months, but over half of respondents (55%) said it was taking between seven to 12 months, and 9% over a year. The fastest moving organizations are shrinking this timeline down to just three-six months.
“Moving from AI pilots to scaled deployment is still one of the most critical challenges for engineering organizations right now,” says David Heiny, Co-Founder and CEO at SimScale. “The fastest moving teams are moving from pilot to scaled projects as quickly as three months, but many are lagging behind, creating another big gap.”
Despite delays in program progression, AI is now playing a meaningful part in engineering workflows. The survey found that 36% of design and simulation projects conducted in the last twelve months used AI or agentic engineering methods. However, it has not yet become universal across all projects. The survey identifies three primary blockers to faster and more comprehensive scale-up:
- Data preparation and availability for AI (74%)
- Governance and compliance concerns (48%)
- Software interoperability challenges (42%)
But many Engineering AI applications – including agentic assistants, workflow automation, and design exploration support – can begin delivering value with far less data preparation than teams think. And governance is becoming less of a concern, with 87% of respondents saying their organizations permit AI to make pass or fail decisions at design gates.
“The fact that over a third of design and simulation projects are using AI demonstrates a tipping point, but we need AI to transition from being a special project to becoming the organizational norm and this means addressing the broader challenges and perceived blockers,” says David. “When it comes to software interoperability challenges, while modern cloud-based infrastructure is critical, the primary bottleneck in engineering is the workflow itself, not a lack of computational power. SimScale has built the architecture that makes AI actually work inside engineering workflows.”
After running a successful AI pilot, clean-technology company, Convion has established a new standard for Physics AI-driven research and development. By making simulation insights immediately accessible through validated AI models – released as internal tools – the engineering team can now explore design changes interactively without direct reliance on computationally intensive solvers.
And global engineering and consulting firm, RLE International, used SimScale to build an end-to-end AI prediction workflow for automotive computational fluid dynamics (CFD) able to predict vehicle aerodynamics in seconds.
One area where there is almost full unanimity is in the return-on-investment (ROI) opportunity. 99% of respondents say they are confident their organisation will realize meaningful business value from AI or agentic engineering within the next 12 months. And this is evident from the results already being seen by first-adopters. Where conventional simulation requests take up to 17 hours, whole AI workflows are being completed within six. Introducing AI into workflows can increase simulation iteration speed from as low as once a week to multiple times a day, and increase the number of design variants from as low as 1-5 to more than 200.
“The teams using Physics AI and agentic AI in engineering workflows are innovating faster as they’re able to explore thousands of ideas in seconds,” continues David. “This is not just about accelerating time-to-market, boosting revenues and reducing risks – although it does all that – it’s about a shift from human-paced iteration to machine-paced iteration, fundamentally changing how physical products are invented. If companies aren’t using these tools – they’ll be left behind.”