Engineering executives are feeling the pressure to adopt AI, according to Monolith’s inaugural State of AI in Engineering study. The survey finds that companies who have adopted AI are more likely to achieve increased revenue, profitability and competitiveness.
The report, State of AI in Engineering, conducted by Forrester Consulting, surveyed 163 engineering leaders at multinational automotive, aerospace and industrial/manufacturing companies in the U.S. and Europe. Called a first of its kind study in engineering product development, survey participants were asked questions related to challenges and key priorities they face in the validation and verification stage of the development workflow and how AI solutions can help them achieve better and faster results.
(Source: Monolith)
Overall findings indicate that engineering leaders face several key challenges, including a lack of insights into their testing and validation processes as well as financial risks from product delays. Other barriers to success include not having the right technology to analyze test data and struggles to improve time to market for new complex products. They also are dealing with a talent shortage.
With more pressure to introduce products to market faster, 71% of surveyed engineering leaders said they need to find ways to accelerate product development to stay competitive. A key reason: 82% said a one-month delay in product launches can cost their businesses millions or billions of U.S. dollars.
Cost is a big challenge for engineering leaders, with 69% reporting that they need to find ways to reduce the cost of design iterations. With too many iterations between designing and testing, 64% said they risk delaying product launches and 61% said they need to find ways to reduce the number of design iterations.
Sixty-seven percent of survey respondents said they feel pressure to adopt AI to avoid losing their competitive advantage. Can AI help? The study finds that companies that have already implemented AI are 43% more likely to see an increase in revenue, profitability, and competitiveness compared to those who haven’t.
When asked how they use engineering test data to support the validation and verification of new, hard-to-model products and systems, on average, only 50% said they use AI to analyze test data from current or upcoming products, and only 29% use it to analyze test data from historic products. Half don’t analyze historic data at all, according to the research. In addition, less than 19% of engineering leaders reported using unsupervised learning algorithms to analyze historic or current test data.
This contributes to big challenges for engineers. The biggest challenges include meeting project deadlines and product launch dates (57%), finding time for engineers to innovate (55%), recording and storing test data properly for others to use later (54%) and running a lot of tests but still not getting the insights needed to design the product (51%).
In terms of existing validation tools, 55% of engineering leaders reported that they lack the required tools, and existing virtual validation and simulation tools are insufficient.
“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 Monolith, an AI software provider.
Engineering leaders reported several benefits by implementing AI. In addition to reporting higher revenue, profitability and competitiveness (47%), they also increased productivity (55%) and creativity/innovation (45%). They also achieved better product testing and testing insights (52%) and improved predictability of the time to market for new products (44%).
Indicating the value of AI to engineering teams, 65% of survey respondents said they are currently expanding their AI implementation, exploring AI vendors or defining their AI business cases.
Other research highlights include:
- 48% struggle to find experts for product design and 36% for data science
- 44% report a perceived risk in changing the way their team works
- 41% said there is a lack of expertise/skills among engineers
The full report can be downloaded here.
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