‘State of AI in Engineering’ study
Published: 17 May, 2023
Monolith, artificial intelligence (AI) software provider to leading engineering teams, has announced that a new commissioned Forrester Consulting study has found that the majority (67%) of engineering leaders feel pressure to adopt AI, and those who have are more likely to achieve increased revenue, profitability and competitiveness for their employers.
Representing a comprehensive study into the current state and views of AI in engineering product development, the new study – titled The State of AI in Engineering – surveyed 163 senior engineering leaders at multinational automotive, aerospace and industrial/manufacturing enterprises in the US and Europe. The study is reported to be the first of its kind in the area of engineering product development and sheds light on the challenges and key priorities that engineering leaders face in the validation and verification stage of the development workflow – and how intelligent AI solutions can support them achieving better results, faster.
“The perfect storm is brewing in engineering as market trends around sustainability and digitalisation are creating even more intractable physics problems that current validation and testing methods are unable to solve," said Dr. Richard Ahlfeld, CEO and founder of Monolith. "As data from this study shows, engineering leaders are at a fork in the road to innovate in new ways as pressure to stay profitable and competitive rises."
In an industry where rapid development of innovative, effective solutions is paramount, engineers are feeling the pressure to introduce products to market faster; indeed, the data in this new study shows that 71% of surveyed engineering leaders indicated they need to find ways to accelerate product development to stay competitive.
To compound the challenge, the new data reveals that many engineering leaders feel unequipped to effect change, with more than half (55%) of those surveyed stating that they lack the required tools, and that existing virtual validation and simulation tools are insufficient.
The severity of the impact of these deficiencies on business success is clear for engineering decision-makers, with 82% of respondents noting that a one-month delay in product launch costs their business millions, or even billions, of US dollars.
While existing physical testing and simulation methods fall short in meeting engineers’ needs for product designs to pass validation, industry leaders see AI as being ideally placed to empower their efforts in producing highly effective solutions for the market, and delivering commercial success. According to the research, engineering leaders who have already implemented AI are 43% more likely to see an increase in revenue, profitability, and competitiveness compared to those who haven’t.
Despite the power of AI to produce meaningful results for businesses in the engineering sector, many engineering organisations are missing out on the full potential of advanced AI technology; less than 19% of engineering leaders reported using unsupervised learning algorithms to analyse historic or current test data, and less than half utilise any of their engineering test data.
Monolith is democratising AI for engineering with its bespoke SaaS platform that uses no-code, machine-learning software, giving domain experts the power to leverage existing, valuable testing datasets for their product development. The platform analyses and learns from this information, using it to generate accurate, reliable predictions that enable engineering teams to reduce costly, time-intensive prototype testing programmes and develop higher-quality products in half the time.
Dr. Ahlfeld added: “With AI, engineering domain experts can quickly understand and instantly predict complex physics, allowing them to test less, learn more and get to market much quicker. The new insights we have uncovered via the Forrester study underline just how crucial the widespread implementation of technologies such as AI has become. The industry now needs to take the necessary steps to prepare itself for a data-driven future.”
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