Across engineering sectors, conversations about AI are shifting. Instead of the familiar debate about whether AI is over‑promised or under‑utilised, the discussion in engineering circles has moved toward something more grounded: how AI can be integrated into existing workflows in a way that complements established engineering methods and supports the expertise already present in teams.
This shift is giving rise to what many research groups, engineering bodies and industrial case studies are calling Hybrid AI - an approach that combines human engineering judgement with the strengths of machine intelligence. The aim isn’t to build fully autonomous design systems, but to create tools that work with engineers, bringing together computation, data, and domain knowledge in a more structured and practical way.
Hybrid AI is quickly becoming the default pathway for AI adoption in engineering because it fits how engineering actually operates - high‑rigour environments where traceability, reliability and human oversight are non‑negotiable.
Engineering Assistants
One of the clearest indicators of this shift is the emergence of engineering-specific AI assistants. These are being built directly into established engineering tools such as CAD platforms, simulation environments and product lifecycle management systems.
Early evidence from manufacturing, automotive development and energy systems shows that these assistants are helping teams suggest parameter ranges that satisfy constraints, detect potential failure points before formal analysis, compare materials based on performance criteria and flag inconsistencies in requirements or design documentation.
The key point echoed across multiple sources is that AI isn’t generating final designs. Instead, it accelerates the reasoning steps engineers already perform, making room for deeper analysis and more creative engineering thinking.
Why Pure Machine Learning Doesn't Work
Pure machine learning models often struggle in engineering environments due to noisy data, rare failure modes and rapidly changing operating conditions. Studies across mechanical, chemical and production engineering show that Hybrid AI models, those combining data-driven predictions with rules, boundaries or heuristics set by engineers, perform far more reliably.
Capturing Hidden Engineering Knowledge
One of the biggest practical shifts happening in 2026 is the use of AI to extract and formalise knowledge that has historically lived inside the heads of experienced engineers. Many organisations have decades of tacit expertise buried in maintenance logs, test reports, design reviews and production notes. Historically, this information has been difficult to retrieve and even harder to interpret, especially when key staff move roles or retire.
Hybrid AI systems are being used to mine these archives for recurring patterns, subtle indicators that precede equipment failures, design decisions that repeatedly lead to successful outcomes, or operational adjustments that consistently improve yield or stability. What makes this particularly valuable is that AI is not inventing new knowledge; it is making existing, often hidden knowledge visible and searchable.
This is proving especially useful in production settings where practical know‑how often outweighs what is formally documented. The ability to preserve, organise and surface this expertise is helping teams maintain continuity, reduce training gaps and ensure that hard‑won engineering insights don’t disappear over time.
Governance and Traceability
As AI becomes more embedded in technical decision‑making, organisations are also strengthening governance frameworks to ensure transparency and accountability. Hybrid AI aligns naturally with these expectations because the engineer remains the responsible decision‑maker, while the AI’s contribution can be traced, documented and validated. This makes it easier to meet regulatory requirements, justify decisions during audits and maintain confidence in safety‑critical environments.
Key Learning Point
Hybrid AI marks a practical turning point: instead of framing AI as either a threat to engineering roles or a magical solution to complex problems, organisations are increasingly recognising that the most effective path forward is collaborative. AI augments engineering reasoning, accelerates analysis, and helps capture knowledge, but only within rigorous structures shaped by human expertise. The organisations that embrace this partnership mindset are already seeing the strongest results.
More Information
If you want a structured introduction to how AI is applied in engineering, the IET’s AI for Engineering: Foundations and Applications course is a helpful starting point. For more information, visit https://www.theiet.org/career/training-courses/ai-for-engineering-foundations-and-applications
We also offer a course for decision makers, to empower them with strategic insights needed to identify which business problems AI can solve within their organisation. For more information, visit https://www.theiet.org/career/training-courses/masterclass-in-ai-engineering-for-decision-makers