How Machine Learning and AI Will Impact Engineering

Rapid advancements in computer technologies are allowing engineers to design better than ever.
Trevor English

As engineers, we face a time of rapid technological advancement of the tools available to us. While we spend our days innovating our designs, our tools are being innovated beyond what we ever imagined. With the drive for practical artificial intelligence embedded in our workflows, how we work may look quite different even just a few years from now.

While many professionals are wary of AI as it may steal their job, a report from the University of Oxford places Engineering and Science professions as the least threatened industries by AI. In fact, most studies point to engineers experiencing great benefits from the rise of AI tools.

AI and machine learning will replace jobs that we know about today, but because it brings new capabilities, it will open up new industries and new jobs that aren’t even on the modern engineer’s radar. Low value and meaningless jobs currently in place will get automated away. It will allow you to spend more time on better decisions.


No matter whether you have adopted machine learning technologies and in the grander picture, artificial intelligence, most engineers recognize that a change is coming. It would seem a natural fit to incorporate artificial intelligence into CAD, into our workflows, into our engineering. This not only facilitates our forward growth as engineers, but it gives us the ability to design with complexities never before possible.

Remaining at the top of our engineering game is no easy task when the game is constantly innovating with new technologies. To remain relevant as engineers, we must understand – even predict – how machine learning and AI will change the game and adapt before we are left in the dust.

The rise of advanced engineering

AI is the next platform. The world’s top minds affirm it. Engineering innovation directs it. Now it’s the time to get on board. All of this can be scary to face as an engineer. How we engineer has shifted over the years as CAD evolved from 2D into 3D and now into a 3D design hub. In essence, CAD has shifted from a supplementary tool to engineers into now a fundamental tool of how we work. If we examine the trend of AI close enough, we can begin to see how the solidification of CAD into our daily workflow isn’t too different from what might happen with AI implementation.

Much like additive manufacturing in the past, AI implementation has been surrounded by a decent amount of hype. As engineers, we’re naturally skeptical when one innovation or technology is broadcasted as the “cure-all” to the future. Even with the hype around AI in the past, current trends place practical AI and machine learning tools right in the laps of engineers and technology leaders.

Generative design, simulation improvements, sensors, and big data design optimization – all of these areas of engineering are at the forefront of being significantly impacted by AI technologies. All of these advanced engineering technologies have the ability to impact us as engineers and our profession as a whole in 3 main ways.

Job Evolution - Every innovation in the past has created a new sector of work and research. Our modern model of technological and often, digital innovation has AI at the leading edge. This means that as engineers, the way we do our jobs and even the areas we work in may shift as the needs of the industry adapt to coming tools. Our workflows will evolve in a micro-perspective as artificial intelligence infiltrates how we design. In the macro-perspective, new industries will be created that existing engineers will need to flow into.

Capability Improvement – AI and machine learning tools bring drastic capabilities to the engineer relative to what we are capable of on our own. Through organic latticing tools, we have weight-saving capabilities like never before possible. With generative design, we can explore design options that never would have been an option. As these tools slowly are implemented into our CAD tools and engineering system, our capabilities as engineers will only be expanded.

Design and Data Management – Perhaps the most impactful aspect of AI innovation in engineering is just how it will affect our workflow management. AI extends far beyond the design process and stretches into data management. In the bigger picture, we arrive at the Internet of Things. AI will surely help us manage our designs and improve interoperability, but it will also break down barriers between departments – between tasks.

Through AI programs managing big data, we will be able to seamlessly integrate manufacturing and design with cloud computing, smart machines, and real-time monitoring. AI can and will take on much of the supervisory side of engineering, allowing us to perform our jobs better. We can understand this idea as a whole by realizing that low-value tasks of today will now be automated, allowing us as engineers to focus our energy into higher-value areas.

Artificial intelligence will help us manage our engineering data efficiently, effectively ushering in Industry 4.0. It solves the problem of big data and makes other engineering advancements more manageable. Advanced engineering, the future of making things, in large part hinges on the implementation of AI into engineering.

How do we adapt?

All of this advancement sounds great in the grand scheme of things, but adapting to these changing workflows is easier said than done. The beginning of the adaption and adoption process for these new technologies begins with refining how each directly impacts our lives and jobs as engineers.

Machine learning is a big player in engineering innovation right now, simply because it presents the greatest opportunity for further innovation. Machine learning surely brings job evolution. If computers and systems have the ability to learn over time, then they can optimize and conduct many tasks that we execute on a daily basis. Machine learning inherently improves our capabilities as it leverages computer learning on our own.


Lastly, machine learning greatly impacts how we manage designs and product creation. Our entire workflow from job definition to job application can be or is already being impacted by machine learning tools. To adapt, we must embrace the changes that are already here and predict the changes that will come. For machine learning specifically, we can start getting comfortable with altering our workflows alongside computers. By doing so, we place ourselves far ahead of our competition.

Industry 4.0 and the rise of connectivity is another poignant AI trend that is making strides in the engineering profession. It evolves our jobs by eliminating the need for constant oversight during production and creates new industries for data application. The Internet of Things improves our capability to collect and manage data through a web of AI tools and physical sensors.

These new tools that allow us to gather data result in a significant increase in the knowledge available to us. AI and Industry 4.0 as a whole seek to help manage and apply these new tools. All of this data collection and smart management done automatically through computers finally makes the management of our designs and production thereafter, an effortless task.

Adapting to the next generation of the industry will surely be hard for nearly every engineering profession, but the steps we can take today are clear. Embracing the cloud in engineering presents the most consequential impact. By trusting big data and cloud management of our engineering, we can become experts in the core technologies Industry 4.0 builds on.

Generative design is the last significant innovation in current AI that impacts engineers. In many senses, we have already had first-hand glimpses of this technology in our simulation and CAD tools. It evolves how we perform our jobs by allowing us to simulate and design better, which twofold improves our capabilities.

The influence of generative design on our design management is seen in how we will begin the design process and handle the design revision process. Of all the technologies we’ve discussed, Generative design tools are perhaps the most real to you and me as engineers. To prepare for the further growth of this capability, we must accept and experiment with the tools already out there. In doing so, we become more prepared than the significant portion of engineers who haven’t used generative design before.