AI use in engineering includes designing parts in industries, such as automotive, naval, and aerospace. Here, engineers can run their models on AI algorithms to identify potential issues, or they may even decide to have some parts designed entirely by AI before optimizing them further with human knowledge. AI technologies, such as predictive analysis come into play as well.
Engineers depend on new materials to craft better, stronger, lighter, or more efficient parts and machines. New materials that are developed with the help of AI can play a big role – Google AI, for example, has already found millions of new materials, some of which could allow engineers to create better products in the future.
Digital twins, virtual representations of physical objects, can help in the design process as well. Engineers can use this AI-driven technology to see how consumers or customers will eventually interact with the product and what problems may arise.
Products that need to be optimized for better/easier maintenance, for example, can be constructed in special ergonomic ways where it’s useful to do so, and so on.
Also, engineers benefit from the fact that AI can help with documentation – this can be a time-consuming and not especially value-creating task. Automating documentation, translations, and so on frees up engineers’ time to focus on the most relevant core tasks, thereby shortening development times while also bringing down development costs.
Thanks to these many advantages, it’s not surprising that many manufacturing and industrial companies that employ engineers are happy to use AI in engineering. This includes automobile companies, such as General Motors (NYSE: GM), that employ Artificial Intelligence today. Over time, more and more companies will follow that route.
How Investors Profit from Rising Use of AI in Engineering
Investing in industrial companies that use AI technologies in their engineering departments is possible, although there might be better ways.
Investors could also look to companies that power this trend at the back end. NVIDIA (NASDAQ: NVDA) is the best example of that. Without its AI chips, the algorithms that power predictive analysis, digital twinning, and so on wouldn’t work.
All companies that want to deploy AI tech in their engineering departments depend on NVDA’s hardware and solutions to some degree, meaning the market potential for NVDA only grows when more companies use AI in engineering.
Broadcom (NASDAQ: AVGO) and Arista Networks (NASDAQ: ANET), which are major AI chip and AI data center players, also will benefit from more demand for AI hardware, computing power, and so on. The growing deployment of AI in engineering will be more wins for these companies as well.
Investors can also choose among some of our Artificial Intelligence picks to benefit from increased Artificial Intelligence usage in engineering departments all around the world.
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