A new report has revealed how legacy tools, data silos and leadership gaps are stalling the progress of implementing AI in product development workflows.
The 2025 State of Engineering AI report, published by SimScale in partnership with Global Surveyz, surveyed 300 senior engineering leaders from enterprises with over 1,000 employees across the US, UK and Germany, across six core industries: industrial machinery, automotive, electronics, life sciences, energy, and AEC.
The report is one of the first clear benchmarks of AI readiness in the engineering sector – highlighting the cultural, process, and technology barriers that remain in place despite soaring expectations.
In the report, 93% of the engineering leaders interviewed expect AI to deliver productivity gains, with 30% anticipating very high gains. But just 3% report achieving that level of impact today.
55% cite siloed data and 42% cite legacy desktop CAE tools as major blockers of advancement.
Organisations using cloud-native simulation tools are reported to be three-times more likely to have mature AI programs and six-times more likely to have clean, centralised data necessary critical for scaling AI. They are also twice as confident in achieving AI goals within the next 12 months.
A leadership misalignment is labelled as slowing progress, with 42% of CTOs suggesting resistance to AI adoption within their organisation’s technical teams, while engineer team leaders themselves report resistance just 29% of the time, suggesting technical teams are more open, ready, and motivated to adopt AI than leadership assumes.
“Engineering leaders see the potential of AI, but knowing isn’t doing,” said Simscale CEO David Heiny. “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 ‘3% Club’ attaining benefits from AI in their workflows today offer the following trends – building and integrating AI agents directly into live workflows, not as bolt-on tools, but as embedded decision-makers at setup, evaluation, and optimisation stages. Virtual prototypes are tested in low-risk settings, but move quickly to real-world, in-the-loop deployment.
By treating data and models as infrastructure – logging and versioning everything in a set, clear fashion – they allow AI to be scaled, trusted, and portable across their tools and processes.
Jon Wilde, VP of product at SimScale concluded that forward thinking teams are proving AI can deliver significant changes in innovation and performance. “The execution gap for others is not technical feasibility – it’s architectural and organisational readiness.
“Now it’s about helping those companies make that leap with confidence before the gap becomes too wide to close.”
