June 26, 2025
Engineering and technology are closely interconnected in a continuous cycle. Advancements in engineering lead to new technologies, which in turn propel further engineering innovations. New technological developments happen every day, but some developments become so impactful that their influence is noteworthy to both engineers and the general public.
One example of this is artificial intelligence, a key Industry 4.0 development that has fundamentally shifted existing engineering models. AI can refer to a range of technologies, including large language models, machine learning frameworks, and generative models. As such, its applications are vast and highly impactful. AI solutions have altered processes and outcomes so extensively that many industries and fields are changing their approaches to everything from robotics to system optimization.
Read on to better understand how AI is reshaping engineering, including an intro to AI applications and technologies, as well as the skills you’ll need moving forward in a rapidly evolving landscape of innovation and advancement.
AI Applications in Engineering
Supply Chain Optimization
By dedicating time and resources to optimization, we can create more robust and sustainable supply chains, reduce waste, and build resiliency into processes. AI and machine learning in supply chains can be used in a variety of ways that lead to improved outcomes and performance.
| AI Use Case | Outcome |
|---|---|
| Predict and forecast future demand | Optimized inventory control |
| Track and manage inventory in real time | Improved live inventory systems |
| Analyze datasets to discover potential risks | Fewer chances for defects and better risk management |
Smart Manufacturing
Smart manufacturing integrates smart devices and software into manufacturing processes. Integrating IoT sensors and artificial intelligence into manufacturing can lead to improvements in precision, efficiency, and overall maintenance. Smart integrations in manufacturing also allows workers to be less hands-on in the manufacturing process, improving safety and reducing the risk of injuries on the floor.
Predictive Maintenance
Critical to industrial and systems engineers is AI’s use in predictive maintenance. Predictive maintenance helps organizations make adjustments and maintain systems before wear issues become so severe that they lead to downtime. However, predicting when systems will require maintenance ahead of time can be a difficult undertaking.
AI and machine learning have the ability to analyze vast datasets and identify patterns that traditional software might miss, leading to more accurate predictions and, ultimately, less downtime. For some systems like power delivery grids and internet infrastructure, this can be critical.
“Companies are now even more interested in knowing how to handle disruption and uncertainties in these complicated systems, and make these systems more reliable and resilient. These are the things that industrial and systems engineers deal with every day.”
– Weihong ‘Grace’ Guo, PhD, Assistant Professor, Department of Industrial and Systems Engineering
Robotics and Autonomous Systems
Artificial intelligence has changed the way we approach robotics. By designing effective models with ethical development in mind, engineers can create highly autonomous systems with less effort than traditional robotics, which requires painstaking pre-programming for every scenario. AI’s capabilities have enabled robots to “make decisions” and operate autonomously in certain contexts, utilizing advanced perception that enables them to interact meaningfully with the environment around them.
How Has AI Impacted Engineering?
The impacts of AI across various industries can be difficult to overstate. These applications have led to improvements in:
- Efficiency
- Safety
- Forecasting frameworks
- Maintenance scheduling
- Decision-making
- Automation
AI has had a particularly strong impact on industrial, systems, mechanical, and aerospace engineering processes, which aim to safely, efficiently, and effectively manufacture components, parts, and machinery that drives everyday life. Improving engineering processes using AI impacts everyone, making not just technology more effective, but making life better for engineers and the public.
Key AI Technologies Driving Engineering Innovation
Machine Learning
Machine learning (ML) is what allows algorithms, computers, and machines to “learn” and perform autonomous tasks. ML models are highly advanced algorithmic softwares that can independently analyze information, generate insights, and continuously improve by processing data.
Engineers who take mechanical and aerospace engineering courses will learn some specific applications of ML that are very impactful in these areas. In mechanical engineering specifically, machine learning can make particular improvements by:
- Optimizing manufacturing
- Streamlining the design process
- Improving forecasting models
- Enabling predictive maintenance
Advanced Simulations
While engineers have been using simulations for decades, we’re starting to see advanced simulations enabled by AI and machine learning as a broad field of technological advancement. With this technology, engineers can simulate:
- Events
- Environments
- Objects
- Processes
By utilizing AI-trained simulations, engineers are able to better predict how certain systems might perform under various conditions or within different environments. This improves safety, efficiency, and effectiveness during the engineering process. And, the more we train AI models using simulations, the more accurate and effective the models become.
Digital Twins
A digital twin is a specialized and highly advanced simulation environment. It accurately reflects not only a physical object (the “twin” in question) like a complicated engine or complex building structure, but also its environment, other objects, and real-time data that enhances the model.
This approach allows for highly realistic digital modeling that supports research and development insights, as well as efficiency in production. For systems engineers, digital twins offer continuous value by improving efficiency, prototyping, and simulation throughout the end-to-end engineering process.
Generative Design
A relatively new capability of computer-aided design (CAD), generative design uses AI to autonomously generate design options within the framework and constraints defined by the engineer. This allows engineers to rapidly generate a wide variety of design possibilities that meet the criteria of the project. Then, the engineer can choose which design is best given their budget, needs, and technical manufacturing capabilities.
Generative design can enable engineers to quickly reach design stages that traditionally take many iterations and a significant amount of time. It also allows engineers to:
- Optimize designs across disciplines
- Test iterative examples
- Incorporate complex geometry
- Provide human input through specifications and refinement
- Explore innovations that they may not have thought of
The Evolving Skill Set for Engineers
AI Proficiency
Having general proficiency in AI tools and methods is becoming an increasingly critical need for engineering employers in various engineering areas, including civil, industrial, mechanical, electric, systems, and aerospace.
Organizations are keen to incorporate AI into their engineering workflows, even creating dedicated positions for AI engineers. For more traditionally minded engineers, adding AI skills to your toolkit will provide you with more expansive opportunities and make you more competitive in the workforce.
Data Analytics
Data analytics has played an increasingly visible and important role for organizational success and technological advancements. In fact, AI and machine learning are increasingly simplifying data analytics processes and tasks. Engineers can benefit greatly from data science and data analytics knowledge, as these areas help them to:
- Make data-backed decisions
- Optimize processes effectively
- Predict and improve outcomes
- Simplify operations
- Manage risks and potential mechanical failures
- Track performance and maintenance needs
Computational Modeling
Computational modeling, another sub-area of advanced simulation, uses computer simulations to represent and solve engineering problems in a digital space. By using computational modeling, engineers can simulate systems and explore scenarios. This allows them to optimize designs and gain important insights into improving the various projects that engineers work on.
Automation Expertise
The next generation of engineers need to understand how to automate processes effectively — and to understand when something can’t be automated. Engineering is one of many industries that is increasingly incorporating automated processes, which improve efficiency and productivity in the engineering process.
By automating repetitive tasks that offer little value, engineers can spend more time on the areas that make a bigger impact on their organization and the world.
Career Outlook and Industry Trends
The integration of AI into the engineering field is, as technological advancements tend to do, leading to a massive shift in specialized engineering areas, job roles, and responsibilities. This offers new challenges for organizations — and new opportunities for engineers seeking to fill the skills gap.
According to a McKinsey Survey in 2024, many organizations are finding it challenging to fill AI-related roles, pointing to strong demand for skilled experts in these domains that will see a strong relevance in emerging job markets.
In addition, the market for engineers is very strong, in part due to technological advancements requiring engineers to have more skills in complex areas like AI. According to the U.S. Bureau of Labor Statistics, there are about 200,000 new job openings expected each year, with an average salary standing well above average at $97,310 for all engineering roles.
Engineers seeking to take advantage of the AI revolution can benefit greatly from a Master of Engineering degree, which prepares them with valuable knowledge and skills to not only use the most cutting-edge methodologies, but to be prepared for future changes in the industry.
“An advanced degree demonstrates to engineering firms that you have the ability to learn new things, adapt to new problems, and fundamentally change how things are done. A graduate degree will help you become a leader and advance your career in the long term.”
– Rajiv Malhotra, PhD, Associate Professor, Master of Engineering in Mechanical and Aerospace Engineering
Shape the Future of Engineering at Rutgers University
Unlock your potential with an online Master of Engineering at Rutgers University. Choose from two degrees in high-demand engineering areas, and take targeted core coursework supplemented by your choice of electives. With a flexible curriculum taught by industry experts, you’ll be ready to tackle complex challenges and advance your career in today’s data-driven world.
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Rutgers School of Engineering is a place of possibilities—for students seeking success, for researchers seeking to create new technologies, and for faculty members and industry leaders seeking to form powerful and productive partnerships.
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