By Andi Wang
Engineering teams are under increasing pressure to put AI to work, whether that means improving maintenance decisions, analyzing process data, or identifying quality issues. Yet many practitioners are being asked to use AI without formal training in machine learning or data science.
One challenge I’ve consistently observed while teaching machine learning to practicing engineers is that most aren’t asking how to build the next AI breakthrough. They’re asking a much more practical question: Given this data and this problem, what tool should I use?
Consider a common engineering scenario: using process parameters to predict product performance. The right solution depends on factors such as data quality, sample size and problem complexity. Yet practitioners often struggle to determine whether a straightforward regression model is sufficient or whether a more advanced approach is warranted. At the same time, the rise of large language models (LLMs) has created the impression that every problem requires the newest AI tool.
The reality is more nuanced. Effective AI adoption requires operational intelligence: an understanding of how different models learn, where they perform well and where they fall short. Practitioners need enough knowledge to evaluate options, interpret results and make informed decisions, not necessarily develop new algorithms from scratch.
Demystifying AI for Practicing Engineers
Turning every engineer into a machine learning expert is neither feasible nor necessary. The mathematical foundations of machine learning are deep and complex. Instead, education should focus on making key concepts accessible and actionable.
For example, when introducing model complexity, we often avoid mathematical formulas and instead compare two approaches to fitting a curve. One captures every detail in the data, while another focuses on broader trends. The comparison illustrates a fundamental lesson: models that are too simple may miss important patterns, while models that are too complex may fail to generalize. Engineers can understand this intuition without first mastering the underlying mathematics.
This foundation becomes increasingly important as organizations adopt newer AI tools. While generative AI and LLMs are powerful for working with text and unstructured information, they do not replace traditional machine learning methods. For many engineering challenges, such as predicting equipment failures, estimating demand or monitoring process performance, established techniques like regression and classification remain the better choice because they are often more interpretable, easier to validate and better aligned with the available data.
Bridging Theory and Industrial Practice
Understanding concepts is important, but applying them in realistic situations is what builds confidence. That’s why AI education for engineers should be grounded in real industrial applications rather than textbook exercises.
This philosophy has shaped recent updates to ISyE 521: Machine Learning in Engineering, a core course within UW-Madison’s Artificial Intelligence Graduate Certificate and the Master of Engineering: Data Analytics (MEDA). The course incorporates industrial case studies that reflect the types of challenges engineers and technical professionals encounter in their organizations. Rather than relying solely on generic datasets or academic exercises, students work through scenarios that mirror real engineering decisions and operational environments.
These examples help illustrate when a straightforward regression model is the right tool, when newer AI approaches provide additional value and how to evaluate the tradeoffs between them. Just as importantly, they demonstrate why successful AI adoption depends as much on selecting the right tool as it does on the tool itself.
Preparing Engineers for What’s Next
The goal is not simply to teach today’s tools. AI technologies will continue to evolve, and practitioners need the ability to evaluate new methods, understand their assumptions and select the right approach for the problem in front of them.
Engineers who understand the fundamentals of data, statistics, machine learning and AI are better positioned to adapt as the technology changes. When combined with practical experience drawn from real industrial applications, that foundation enables them to move beyond simply prompting AI systems and toward creating measurable value with them.
Organizations do not need every engineer to become an AI researcher. They need practitioners who understand the technology well enough to apply it thoughtfully, ask the right questions and make informed decisions. That combination of strong fundamentals and practical AI fluency is what transforms AI exposure into real operational value. For professionals looking to build those capabilities, programs such as UW-Madison’s Artificial Intelligence Graduate Certificate and the Master of Engineering: Data Analytics (MEDA) help develop both the technical foundations and decision-making skills needed to apply machine learning effectively in practice.