Harnessing the engineering prowess through generative artificial intelligence technology
In the rapidly evolving world of engineering, Generative AI (GenAI) is making a significant impact. This cutting-edge technology is revolutionising the way engineers approach their work, offering unprecedented opportunities for efficiency, innovation, and cost savings.
One of the most promising developments is the advent of multimodal capabilities for large language models. This allows for the effective retrieval and exploitation of critical information presented in visual form in engineering reports. The rise of specialized agents and their orchestration (multi-agent systems) will further enable greater automation of processes, making them more streamlined and efficient while reducing the need for human oversight.
The evaluation process for GenAI use cases is comprehensive, encompassing eight dimensions: strategy, governance and compliance, processes, data, IT infrastructure and security, employees, cost and investment readiness, and ethical and ecological impact. Companies like Fraunhofer Institutes are actively exploring the full potential of GenAI in engineering, aiming to optimise and automate processes with specialized models handling complex data such as production time series and robotics multimodal data.
GenAI is proving particularly effective in improving product quality and compliance by automating certain quality control tasks. With the integration of GenAI into everyday tools, new capabilities are helping to revolutionise the design process, leading to significant cost savings and reductions in product design times.
However, the full potential of GenAI in engineering has not yet been realised due to the limitation of a text-based knowledge base. The need for large multimodal models (LMMs) to include various data formats from engineering data sources is apparent. Companies are integrating GenAI technologies to optimise engineering processes and reduce time-to-market, but they struggle to unlock the potential value of GenAI due to a lack of an efficient end-to-end assessment supporting the implementation of suitable use cases.
To get the most value from GenAI, it's important to choose the right use cases and develop a strategic order of pursuit. The minimal criteria to be considered within the GenAI use cases selection process covers four focal points: functional criteria, technical criteria, regulatory criteria, and strategic criteria. Companies are encouraged to start with simple and cost-sensitive use cases like Retrieval-Augmented Generation (RAG) technology, particularly in requirements engineering and compliance demonstration, and progressively move to more complex ones while keeping the bigger picture of scaling targets in mind.
As the implementation activities of generative AI for engineering gain focus, an overall AI strategy is essential to guide the starting process, connect existing GenAI applications, and identify synergy potentials from the beginning. Hybrid AI, the combination of GenAI with deterministic AI, is emerging as a key solution to meet the specific demands of the engineering field, particularly for system and process safety.
The integration of GenAI technologies is not without challenges. The 2025 MIT report highlighted that 95% of GenAI pilots are unsuccessful primarily because of integration and process issues rather than model quality. To overcome these challenges, software vendors are actively working to integrate GenAI modules directly into their tools, especially for generative design, with the goal of making these features an integral part of the engineer's daily use.
As we move towards 2027, it's expected that 80% of the engineering workforce will need to upskill to fully leverage the capabilities of GenAI. The blog discusses effective strategies for integrating GenAI technologies in engineering, offering valuable insights for companies looking to lead in the field and unlock lasting innovation, resilience, and competitive edge.
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