Engineering and Design

The engineering and design industry is going through significant innovations. What would typically take professionals months to complete can now happen in days or even hours using artificial intelligence-powered tools. Engineers utilize machine learning to predict how products will perform before they are built, and designers use generative innovative systems to explore design options that are nearly impossible to create manually. Cloud-based software enables teams to collaborate across continents, sharing 3D models and simulations in real-time. Engineering practices are now faster, cheaper, and more innovative. However, there are a couple of concerns based on the integration of artificial intelligence, such as what happens to the skills that these professionals have learned and mastered. If AI can resolve complicated procedures or generate long video content, what does that mean for the engineers and designers who spent years learning to do those tasks?

There are some perspectives that these systems work in AI, as a tool that supports these professions in creative problem-solving and higher-level thinking. Others worry it will make specific, specialized skills obsolete and create gaps between those who can afford to adapt and those who cannot. Industries such as automotive, aerospace, and manufacturing are currently using artificial intelligence and similar systems to reduce costs and enhance performance. Companies that adopt these technologies gain competitive advantages. Companies that resist risk fall behind. The challenge for engineers and designers is figuring out how to work alongside AI rather than being replaced by it. Acquiring new skills can help maintain a good integration of professionals with technological advancements, such as programming, data analysis, and having a complete understanding of how machine learning models operate. It also means recognizing that some problems still require human judgment, creativity, and the ability to think through complex trade-offs that AI cannot handle. Technology is reshaping what it means to be an engineer or designer. Still, it has not eliminated the need for individuals who deeply understand the work and can make informed decisions that balance performance, cost, safety, and innovation.

Curated Resources:

Resource 1:

Artlist. (n.d.). AI video generator built for video creators [Video]. YouTube. https://www.youtube.com/watch?v=Bm9gl8Nec2A

Type: Video

Resource Explanation: This video shows what the Artlist's AI video generator can do. It is designed for video creators who need to produce content efficiently and effectively. I chose this one because it illustrates how AI is transforming creative work in fields such as engineering and design. Making videos used to mean you needed technical skills, expensive gear, and hours spent editing. Now, AI tools can create video content just from text prompts, which opens it up to people who have never learned video production. This matters for engineers and designers who need to present their ideas visually but lack a profitable budget or the time to hire someone with high-level skills to shoot and edit videos for them. A broader pattern is seen in the platform because it uses artificial intelligence to handle technical aspects, and allows professionals to focus entirely on creative decisions and the overall direction of a project. However, it also raises questions about what happens to the people whose jobs involve doing that technical work. If AI can edit footage and create visuals on its own, what does this mean for video editors and designers? This resource questions and examines whether AI is a supporter or diminishes certain types of work, and whether the convenience is worth the trade-offs.

Resource 2:

Johns Hopkins University Engineering for Professionals. (2024, June 14). The impact of AI on the engineering field. https://ep.jhu.edu/news/the-impact-of-ai-on-the-engineering-field/

Type: Article / Educational resource

Resource Explanation: This article from Johns Hopkins breaks down how AI is transforming engineering and what that means for professionals in the field. I went with this source because it moves past the publicity and gives real examples of how engineers actually use AI day to day. Predictive maintenance programs utilize machine learning to analyze how equipment is performing and predict when a failure is likely to occur before it happens. That saves companies money and extends the lifespan of expensive machinery. Design optimization depends on artificial intelligence because it studies and determines the optimal configuration of complex products, such as those in the automotive and aviation industries, where both performance and cost are paramount. Autonomous systems, powered by AI, are transforming robotics, transportation, and other areas by making real-time decisions without requiring human intervention. What I value about this article is that it covers both the good and the complex parts. Engineers who know AI are getting hired left and right, but that also means engineers who do not learn these tools might get left behind. These systems can eventually take over repetitive tasks, which allows engineers to focus on complex issues and creativity. It also enhances the idea that in order for engineers to be competitive, they must adopt new skills, for example, coding in Python, analyzing data, and collaborating with people from diverse fields to utilize AI systems correctly. This piece supports the idea that technology changes what engineering looks like, but it does not eliminate the need for people who know what they are doing.

Resource 3:

Neural Concept. (n.d.). AI in engineering: Applications, key benefits, and future trends. https://www.neuralconcept.com/post/what-is-artificial-intelligence-engineering

Type Article / Industry perspective

Resource Explanation: This article from Neural Concept explores how AI and machine learning are being applied in engineering across various industries, including automotive, aviation, and marine. I added this one because it gives real examples of AI actually working, not just ideas about what it could do someday. Car companies utilize machine learning models to existing car designs to predict the impact of design changes on a car's performance. That allows them to test changes virtually before building anything physical, which saves both time and money. In aerospace, Airbus partnered with Neural Concept to utilize deep learning in aircraft aerodynamics. They cut the time it takes to predict pressure on airplane bodies from one hour down to 30 milliseconds. That is more than 10,000 times faster. In boat building, a team working on a high-speed sailboat utilized AI to optimize underwater foil designs and overcome cavitation issues that had previously limited traditional sailboats to speeds of around 100 km/h. These examples demonstrate that AI is not replacing engineers in their jobs. It is giving them tools to test design ideas faster and more thoroughly than the old trial-and-error approach ever could. The article also discusses what is coming next, such as generative design, where AI generates a range of design options based on factors like available materials and required performance. These AI designs often appear unusual or unexpected, but they consistently meet stringent performance requirements. The main point is that AI speeds up engineering work and expands what can be done, but engineers still determine which designs make sense and how to verify that the results actually work.

Summary Paragraph:

These three resources demonstrate that AI is transforming engineering and design work in significant ways, but not in the manner people often worry about. AI is not kicking engineers out. It is changing what engineers spend their time on. The Artlist video demonstrates how AI tools make creative work easier to access by handling the technical aspects, but it also raises concerns about individuals whose skills are less valued when AI can perform their work more efficiently and cost-effectively. The Johns Hopkins article supports the notion that engineers who learn to work with AI are being hired because they can utilize these tools to solve problems more effectively. Predictive maintenance, design optimization, and self-running systems all use technology to analyze data and help professionals make informed decisions; however, they still require human oversight and expertise. The Neural Concept article provides solid evidence that AI yields tangible results in industries such as automotive and aviation. Companies are using AI to test designs on computers, predict how things will perform, and explore options that would take months or years the old way. What connects these resources is recognizing that AI is a tool, not a substitute for people. Engineers still need to understand the problems they are trying to solve, make sense of what AI tells them, and decide which solutions actually to use. The challenge is that not every engineer will adapt at the same speed. The ones who learn programming, data analysis, and machine learning will do well. Those who refuse to adopt new tools might see their jobs shrink or disappear. Future engineering and design work will involve more remote collaboration using AI tools. Technology will continue to innovate design work and better systems in these sectors, but engineers and designers who succeed will be those who learn how to integrate these systems and maintain the unique creativity that machines cannot replicate.

Do you think AI replace human engineers and designers?

• Yes, eventually
• No, it will just change their roles