Will AI Replace Engineers in the Next Decade?

Anxiety about artificial intelligence (AI), and its potential impact on engineering careers, is understandable. You may have seen dramatic statistics, such as the World Economic Forum's finding that 40% of employers anticipate workforce reductions due to AI automation of tasks.¹ Perhaps you saw the Ernst & Young report suggesting that up to 25% of entry-level tech jobs in India might vanish due to AI.² You've surely seen at least one article concerned that employment for software engineers,³ among others in the profession, might not exist in 10 years.

So, will AI replace engineers in the next decade? This article considers whether that’s a realistic prospect, or whether the future of engineering and AI might be more about collaboration than attrition.

The Types of Engineering Work AI Can Already Automate

Those who would answer "Yes" to the question, "Will AI take over engineering jobs?" can reasonably point out that AI is already widely used to automate tasks and workflows. For example, civil engineers can use AI for surrogate modeling, a technique allowing design simulations to be run when there isn't sufficient data on hand for more traditional modeling and evaluations.⁴ Engineers also regularly use AI and machine learning tools for routine tasks such as project scheduling, as well as more advanced data analysis, such as using AI tools to perform 3D design optimization.⁵

Asking, "Can AI replace engineers?" isn't paranoia. Over the last few years, advances in AI and machine learning have made striking contributions to the world around us. We see this in impressive AI-powered innovations, such as self-driving cars and the latest generation of autonomous robotics, and AI is increasingly powering the profession of engineering itself.

The Human Skills that Engineering Still Requires

Even so, some skills seem likely to remain distinctly human: too nuanced or judgment-dependent to delegate to machine learning and AI. While AI is extremely useful for handling routine tasks, for instance, it lacks creativity. Engineers are encouraged to focus on the skills they have that cannot be handed off to machine learning.⁶ These include problem framing—defining the issues that AI might be tapped to help resolve—and overseeing ethics and safety concerns.

Ultimately, most engineers provide solutions to human problems, and solving human problems requires a human element. Experience, empathy and an instinctive understanding of why a problem matters in the first place are qualities AI cannot replicate. To a certain extent, this boils down to communication and leadership. AI might take on much of the heavy lifting of data processing and analysis, but deciding what problems to tackle and explaining why certain approaches were favored over others will likely remain human responsibilities.

How Are Engineering Jobs Evolving with AI Integration?

As AI integration becomes increasingly common, engineering jobs are evolving to include specialized AI knowledge. Roles such as AI product engineer, focused on using AI tools throughout the product development process, are becoming increasingly visible in the job market, alongside growing demand for engineers who apply machine learning to refine and optimize industrial systems. There is also a growing need for engineers who can provide oversight: AI safety engineering, defined as ensuring that AI systems are safe, reliable and aligned with human values to prevent unintended harm,⁷ is an increasingly prominent area of the profession.

We can reasonably expect AI expertise to become a required skill in many aspects of engineering work, as more powerful tools deliver solutions across a wide range of disciplines, including coding, mechanical design and project management. It's already commonplace, for example, for software engineers to spend their time instructing and reviewing AI-generated code.⁸ Further, AI project management tools automate many reporting and data-analysis functions.⁹

Will AI Replace Engineers in Entry-Level Roles?

Of all the concerns about AI's impact on engineering, perhaps none is more pressing than its potential effect on entry-level roles. AI has proven very useful for automating the routine data-entry and processing tasks that entry-level employees might previously have handled. The fact remains, however, that the output of all this AI-driven automation must still be inspected and the work must still be directed. As such, foundational engineering knowledge is arguably more important than ever for entry-level engineers, who may now be expected to supervise machines that can work quickly but still need direction.

What This Means for Students and Early-Career Engineers

For engineering students and early-career engineers, the rise of AI can be concerning. "Will engineers be replaced by AI?" seems a valid question when the articles you read suggest that some countries’ entry-level jobs are being subsumed by a tidal wave of automation.²

The shift in entry-level engineering jobs, though, appears to be away from routinized tasks and toward the use of AI tools to improve efficiency and productivity. Engineering as a profession isn't going away because of AI. Indeed, engineering may be working more closely with AI than any other profession, with engineers using machine learning both as a tool in their work and as part of the products and solutions they develop. The engineers best positioned for long-term success won't be those who avoided AI. They'll be the ones who learned to work with it first.

Join the Next Generation of AI-Savvy Engineers

We’d all be wise to worry less about AI replacing engineers and focus more on the fact that engineers must be AI-proficient. Some engineers will work directly with AI, developing tools and systems for others to use. Others will incorporate AI-driven products and processes into their daily work.

As has always been the case, engineers need training that is aligned with current practices. At Case Western Reserve University, you'll find an online Master of Engineering program that’s practice-oriented and aligned with the latest developments in the profession.

As a student in the online Master of Engineering program, you can choose from four areas of concentration: Mechanical Engineering, Biomedical Engineering, Systems and Control Engineering or Engineering Innovation, Management and Leadership. Alternatively, you can pursue specialization with the online MS in Biomedical Engineering or the online MS in Mechanical Engineering.

At Case Western Reserve, you'll be guided by expert faculty, such as Professor Xin Yu, Professor of Biomedical Engineering and a principal investigator on multiple NIH grants, or Professor Roger Quinn, Arthur P. Armington Professor of Engineering, Mechanical and Aerospace Engineering, and author of more than 250 full-length publications.

With more than 270 recent industry partners and $373.5 million in competitive research projects (as of FY2022), you'll also find an engineering department that is a research hub with extensive networking opportunities.

Join the next generation of engineering leaders at Case Western Reserve University. Find our admission requirements and tuition information online. For an in-depth discussion of the career-focused engineering training and networking opportunities you'll find at CWRU, schedule a call with one of our admissions outreach advisors today.