How Generative AI Requires Engineering Companies to Evolve their Talent Strategies

Generative AI is quickly becoming ubiquitous within the engineering sector. Employers have taken to a number of tactics in response, ranging from going “all-in” to ignoring it and hoping it goes away.

But here’s the reality: generative AI is here to stay. Which means that, if you want to be competitive in the future, you can’t ignore it. Rather, you need to be clear-eyed about what AI does better (and worse) than humans. Then you can adapt your talent acquisition strategies to apply the best of both worlds—human and AI—to your engineering processes.

Artificial intelligence (AI) is a game changer; in fact, it already has been. But AI has been around, believe it or not, since the 1950s. Machine learning has been undergirding popular software for over a decade (e.g. Google’s search algorithm). So what’s changed in recent years to bring AI to the forefront of the conversation?

Like any phenomenon, it’s multi-faceted. But a key move was the release of ChatGPT by OpenAI. Although ChatGPT didn’t really feature any new capabilities—companies like Jasper were already offering LLM-based software—it elevated public awareness of the software, illuminating just how far it had come. 

But just as with any public phenomenon, there’s the challenge of separating the hype from reality, fact from fiction. What exactly is generative AI going to do to traditionally “safe” career roles? How is it going to change how we think about “work” in the future?

For engineers, specifically, we already have a number of AI use cases that are already in play. Let’s take a look and, from there, see if we can spot some trends to inform how we think about AI going forward.

Generative AI can ingest vast quantities of design data, using the insights generated from that experience to generate multiple virtual models with different configurations. Then, that same AI can evaluate the designs based on specific criteria—like maximizing strength while minimizing weight—and select the optimal design to meet the criteria.

Also known as text-to-design, generative AI can take high-level specifications, requirements, and prompts provided by engineers to generate CAD models. The engineer, then, can tweak and adjust the design as needed. CAD can be a significant time saver and provide design ideas that a human engineer may not have otherwise considered.

Pressure-testing complex systems requires extensive testing. When done in the physical world, this can be costly and time-consuming. 

AI-powered virtual modeling, on the other hand, enables engineers to simulate and analyze system behavior under a range of conditions. These conditions can help drive further design iteration that optimizes for factors like stress distribution, fluid flow, or heat dissipation. 

What’s more, AI simulations can run at a fraction of the speed of physical testing. As such, you can gather more performance data in a much shorter time frame.

Many complex geometries for 3D printed components can be difficult for humans to envision, although they’re robust in practice. Generative AI can help to optimize geometries and push them to print immediately, saving time and enabling more fine-tuned outputs.

One often overlooked benefit of generative AI is the discovery of new materials with desired properties. By predicting and modeling material behavior based on known chemical data, generative AI can discover new alloys and compositions that would take humans too long to do.

Now, looking at the examples above, what are some common threads we can trace among them? Here are few that jump to mind:

• AI is faster than humans. By sheer force of computing power, AI can perform tasks in less than a fraction of the time it takes a human to perform the same task. 

• AI is more comprehensive than humans. AI can accumulate a much wider range of information, building its body of knowledge faster than any human could.
• AI can perform more iterations than humans. By accounting for variables that humans don’t think about, AI can iterate faster and more extensively. 

As such, many engineering companies are rethinking the value of human talent in the future. There is already talk about engineers having to move into more editor-like roles in the future. 

However, if you jump to AI too quickly, you risk misunderstanding what it can (and can’t) do for you. If you don’t understand the risks, you’ll fall for the shallow, trendy promises and hurt your business. 

Because for all AI’s advantages and benefits, there are some things that only human beings can do. And these, above and beyond mere technical competence, are the traits you should hire for now and in the future.

AI is limited by the scenarios and data it’s encountered before. Humans, on the other hand, can approach challenges with fresh perspectives and devise innovative solutions to technical problems. While AI can implement those solutions more efficiently, only humans have the creativity to pioneer new technologies and methods that lead to a competitive edge. In other words, humans are better at figuring out what to build and why, while AI figures out how to build it.

For the most part, generative AI output is limited to the quality of the prompt. The AI isn’t designed to think about the system as a whole and account for unintended consequences—rather, it provides the best possible output based on the prompt it has received. 

As such, humans are uniquely positioned to provide a holistic perspective, considering interconnections, potential ripple effects, and orchestrating AI tools to account for these complexities.

By virtue of their operations, AIs are always going to be two steps behind. In a highly evolving field, this is a costly lag you can’t afford. Human engineers are better equipped to adapt to change, stay ahead of the curve, and direct AI tools to bring more novel and competitive concepts to market.

Intuition: it’s that X factor that humans have and AI doesn’t. Developed through experience and expertise, intuition empowers human engineers to make quick decisions when data is incomplete.

Finally, AI can’t manage teams or people. A lack of emotional intelligence prohibits the technology from effectively orchestrating and communicating technical ideas among multidisciplinary teams. 

Human engineers are essential to maintain positive relationships and working conditions among all team members, which are both essential to efficiently driving project outcomes.

Technical expertise still matters, but it matters a little less than in the past. Now, human engineers will need not only a solid background in AI-capable tools, but also in other soft skills that AI just can’t reproduce.

This means that your hiring processes need to account for these changes. Often it’s difficult to identify soft skills on paper, and only slightly less difficult to do so in an interview.

To avoid the high cost of a mishire, it’s important to have an in-market expert who knows what to look for. Better yet, find someone with a bench of talent that they have long-term relationships with—who they’ve already validated. 

Get started with a Brightwing representative, and we’ll help you navigate the changing tides of talent acquisition in an AI-dominated industry.