The following is an article based on an episode of the Being an Engineer podcast series by Aaron Moncur, edited and published here with permission.
In the ever-evolving world of engineering, the integration of artificial intelligence (AI) is no longer a distant concept confined to science fiction. It’s here, and it’s changing the way engineers approach design, problem-solving, and innovation. In this episode of the Being an Engineer podcast, we sat down with Kevin Surace, a renowned innovator, futurist, and AI expert, to discuss the immense potential AI holds for the engineering profession. Kevin’s insights not only reveal the capabilities of AI but also how engineers can strategically use AI tools to transform their work and the industries they serve.
A New Era of Engineering: AI as a Strategic Tool
Kevin Surace opens by emphasizing that AI is much more than a buzzword—it’s a powerful tool that engineers can leverage to achieve unprecedented levels of efficiency, creativity, and precision. “AI is not here to replace engineers,” Kevin explained, “but to augment human intelligence, providing new ways to tackle complex problems that were previously insurmountable.”
Surace highlighted how AI can process vast amounts of data quickly, identify patterns, and make predictions that would take human engineers exponentially more time to discover. In fields like civil engineering, mechanical design, and manufacturing, AI’s ability to analyze data sets enables faster decision-making and more informed choices, from the design phase all the way to execution.
For instance, in mechanical design, AI can simulate hundreds of prototypes within minutes, optimizing variables such as material selection, geometry, and stress. Engineers no longer need to rely solely on iterative physical testing, which can be time-consuming and expensive. AI enables them to rapidly refine designs virtually, reducing the time to market and driving down costs.
The Role of GPT in Engineering
One of the fascinating technologies Kevin focused on during the discussion was GPT (generative pretrained transformer), the same AI model behind tools like ChatGPT. He pointed out how GPT models, traditionally seen as language processors, have immense potential in the engineering space. Beyond generating text, these models can understand and predict patterns within complex systems, provide insights into design options, and even assist with troubleshooting issues in real-time.
“Imagine you’re working on a design for a new bridge,” Kevin illustrated. “You input your parameters into an AI-powered GPT tool—length, load capacity, materials—and within moments, it generates several structurally sound designs based on historical data and real-time simulations. It’s like having a brainstorming partner that never runs out of ideas and works 24/7.”
He added that GPT could become a real asset when it comes to documentation, one of the most tedious but critical tasks engineers face. AI can now auto-generate technical reports, specification documents, and even predictive maintenance guidelines, which frees up engineers’ time to focus on high-level creative problem-solving.
Kevin also highlighted AI’s ability to detect anomalies in datasets or systems, which makes it a valuable partner in industries where safety and precision are paramount, such as aerospace or biomedical engineering. AI systems can monitor vast amounts of sensory input, flagging potential issues before they become catastrophic failures—something human engineers simply can’t achieve with the same speed or precision.
AI in Manufacturing: Efficiency and Customization at Scale
Kevin, a key player in developing AI for industries like manufacturing, touched on how AI is revolutionizing the production line. “Automation has been around for a long time, but AI-driven automation is a game-changer,” Kevin noted. Traditional automation systems are rigid—they follow pre-programmed steps without the flexibility to adapt to new variables. AI, however, brings adaptability and intelligence to automation systems.
One of the most exciting uses of AI in manufacturing is predictive maintenance. “With AI, machines can essentially ‘tell’ you when they are about to fail,” Kevin explained. “By analyzing vibration patterns, temperature changes, and performance metrics, AI systems can predict when parts will need replacement before they break down, minimizing downtime and preventing costly equipment failures.” This proactive approach to maintenance can save companies millions of dollars and vastly improve production efficiency.
Additionally, AI has ushered in a new era of customization in manufacturing. Kevin illustrated how, through AI systems, manufacturers can produce highly customized products on demand, adjusting designs and production processes in real time to meet the specific needs of individual clients without sacrificing efficiency. For example, automotive manufacturers can now offer more custom-built features without slowing down production, thanks to AI algorithms that optimize each stage of the build process based on the customer’s unique specifications.
Bridging Creativity and Engineering with AI
A common concern that engineers might have is the misconception that AI’s involvement could reduce the need for creative problem-solving. Kevin, however, sees it differently. AI doesn’t replace human creativity—it enhances it.
“AI allows engineers to focus more on the ‘what ifs’ rather than the ‘how-tos,’” Kevin elaborated. “For example, engineers might spend hours running simulations or creating models manually, but AI can automate those tasks, providing more time for engineers to explore innovative solutions and think outside the box.”
Kevin stressed that AI doesn’t think in a vacuum—it feeds off the data it’s given. Human engineers are still responsible for defining the parameters, identifying the goals, and ensuring that AI-driven solutions are aligned with the project’s objectives. AI can suggest solutions that humans might not immediately think of, but it’s the engineers who validate and refine those ideas.
A prime example Kevin mentioned is in the realm of architecture and building design, where AI can suggest more sustainable construction methods, identify the optimal placement of energy-efficient materials, or propose architectural designs that maximize natural light—all based on data analysis of thousands of previous designs.
AI and the Human Element: Building Better Collaboration
While AI can process more data and simulations than any individual engineer, Kevin made it clear that collaboration between humans and AI is the real key to unlocking the full potential of this technology. He coined the term “augmented intelligence” as opposed to “artificial intelligence” to emphasize this point. “AI is most effective when used to augment human decision-making rather than replace it,” he said.
Engineers still bring to the table elements of intuition, creativity, and ethical consideration—qualities that AI, at least for now, cannot replicate. In fact, engineers must play a critical role in ensuring that AI systems are applied ethically and responsibly. For example, when AI tools are used in safety-critical environments, human oversight is crucial in making sure that decisions made by AI align with regulatory standards and human values.
Kevin also discussed how AI can foster better collaboration within engineering teams. AI tools can act as intermediaries that analyze feedback, data, and input from various team members, allowing everyone to work more cohesively. This becomes especially important in large, distributed teams working across different time zones, as AI systems can centralize communication, offer solutions to issues that arise, and keep track of project progress—all in real-time.
“AI can be the glue that holds these large, distributed engineering teams together,” Kevin emphasized. “It bridges gaps in knowledge, communication, and workflow, ensuring that everyone has access to the latest insights and data to make informed decisions.”
Ethical Considerations and the Future of AI in Engineering
A key part of the conversation delved into the ethical implications of AI in engineering. Kevin is acutely aware of the challenges that come with AI, particularly regarding transparency, accountability, and bias. AI systems are only as good as the data they are fed, and if that data is biased or flawed, the AI’s recommendations will reflect those biases.
Kevin warned that engineers need to be vigilant about the data they are using to train their AI systems. “Bias can creep in, often without us even realizing it, especially in industries like healthcare or hiring, where AI is increasingly being used,” he explained. Engineers must carefully audit and validate the data sets they use, ensuring that AI systems operate fairly and inclusively.
Looking ahead, Kevin sees AI continuing to evolve, with more sophisticated models emerging that will blur the lines between human and machine capabilities. One exciting frontier is AI’s role in adaptive learning. AI systems are beginning to not just follow orders but learn from their interactions with engineers and other systems, becoming smarter and more attuned to specific problems.
However, Surace also predicted that as AI becomes more integrated into engineering practices, there will be a growing need for engineers who understand how to work with AI—those who can not only use AI tools but also build, train, and troubleshoot them. In this sense, AI isn’t reducing the need for engineers—it’s changing the skills that engineers will need to thrive in the industry.
Embracing the AI Revolution
As the podcast concluded, Kevin left listeners with a powerful message: engineers shouldn’t fear AI—they should embrace it. AI is rapidly becoming an essential tool in the engineering toolbox, one that, when used effectively, can elevate the quality of work and unlock new possibilities.
“We’re at the beginning of an exciting era,” Kevin said. “AI is going to change how we work, how we create, and how we think about engineering problems. But at the heart of it, engineering will always be about human ingenuity. AI is just the next step in that evolution.”
For engineers who are curious or even apprehensive about AI, Kevin’s advice is clear: start experimenting with AI tools now. The sooner you familiarize yourself with how these systems work and how they can assist in your daily tasks, the more prepared you’ll be for the future of engineering—a future that is undeniably intertwined with artificial intelligence.
Connect with Kevin Surace on his LinkedIn profile.
Get more Being an Engineer Podcasts here: https://teampipeline.us/being-an-engineer-podcast/.
