Andrei Simanenka – How AI Helps Engineers Make Better Decisions: A Field-Tested Perspective

Introduction 

I am an engineer, not an AI evangelist. 

In most of my job life, engineering decisions were made without artificial intelligence. They solved problems using measurements, calculations, disassembly and testing, and taking an active hand in the process: the outcome of these methods brought about personal responsibility. Because of this background, exaggerated claims about AI and its role in engineering make me careful.   

What interests me in AI today is not just automation for automation’s sake, and certainly not a promise of replacing engineers. Its real utility resides in something much more practical: providing expert engineers with a faster (and more accurate) way to arrive at decisions, even while the data is incomplete, with unknowns and real-world conditions hanging over us all. 

This article embodies my own history of AI as decision support in engineering practice – not theory, but practice, the application of AI.   

The Real Challenge in Engineering: Reducing Uncertainty 

Most engineering problems do not constitute difficulties because of their complexity. “Obvious” systems do not respond systematically: there is too little information; symptoms are misleading, and systems interlink in unintended ways to achieve a desired response.   

Traditionally, engineers reduce that uncertainty based on sequential testing: one hypothesis at a time. This is a valid method, but it is slow-moving – particularly when systems are colossal, interdependent, or time-sensitive.   

Based on my own experience, the most potent contribution AI has left is to help remove uncertainty earlier in the process of decision making.   

Where Machine Learning Can Help 

Machine learning is not toolkit to predict outcomes blindly. I apply it to bring light patterns that exist and are hard to quantify manually. 

In practice, machine learning models help identify: 

• recurring combinations of faults, 

• abnormal relationships between system parameters, 

• deviations which do not yet prompt conventional alarms. 

In traditional troubleshooting, these patterns often are not seen until after several failures. Machine learning makes it possible for engineers to detect them at an earlier stage by comparing current system data with large historical data sets.  

That does not replace engineering judgments. 

It simply helps an engineer get to the right question sooner. 

AI-Assisted Simulation and Digital Models 

Typical simulations are based on assumptions that are rarely stable under realistic operation. Systems age, loads change, conditions change, and operational patterns change.   

AI-supported simulations have value when they can be updated and corrected using operational data. In practice, this enables engineers to: 

• test design revisions before physical implementation, 

• evaluate edge cases without exposing real systems to risk, 

• reduce the gap between theoretical model results and real behavior. 

The upshot is not theoretically accurate, but fewer surprises, less rework, and more reliable results.   

Generative Design: Creativity vs. Discipline 

Generative design is frequently referred to as creative. Engineering-wise, discipline is its greatest asset.  

If constraints have been defined in the right way – loads, materials, tolerances, manufacturing limits – AI can explore combinations without bias or fatigue. Most proposed solutions are not feasible. Some are inefficient. 

But sometimes a solution does come to pass that is structurally valid, manufacturable, and undoubtedly better than a manually created version.   

This is not creativity. 

It is a systematic discovery that takes place much faster than a human individual can reasonably do. 

Natural Language AI as a Tool for Productivity 

Modern engineering practices require a large amount of documentation, standards, specifications, and reporting. This administrative work frequently takes longer than technical analysis. 

Natural language AI is useful here not so much for what it “writes,” but because it: 

• extracts relevant information from large technical documents, 

• compares requirements across standards, 

• compiles fragmented notes into coherent technical summaries. 

This minimizes cognitive overhead and allows engineers to focus on analysis and decision-making rather than formatting and searching.  

When AI Goes Awry – and Why Experience Matters 

AI systems fail when: 

• input data is incomplete or misleading, 

• constraints are not properly defined, 

• outputs are accepted without verification. 

I have come across AI-produced results that were mathematically consistent and physically wrong. 

This is why experienced still matters so much. AI accelerates decision-making, but it also speeds up mistakes when used in the absence of domain knowledge. Engineers are still required to validate results against physical reality and operational circumstances. 

AI does not take away responsibility. 

It reveals weak assumptions faster.  

More Than Individual Project Impact 

When used correctly, AI-assisted engineering brings about:   

• improved system reliability, 

• reduced downtime, 

• better energy efficiency, 

• lower maintenance and operational costs. 

Such effects scale across buildings and industrial facilities, transportation systems, and energy infrastructure. The national significance of AI in engineering is not so much about the technology itself – it is about how good engineers use it to make real systems more dependable. 

Conclusion 

Engineering judgment is not substituted by AI. 

From my practice, it increases clarity. It reduces uncertainty, helps engineers assess alternatives faster under constrained real-world conditions, and supports decisions made with greater confidence. 

The engineers who benefit most from AI tools are not beginners, but those with enough experience to recognize when AI output is useful – and when it is not. This balance, not automation alone, is what drives progress in engineering. 

Author Bio   

Andrei Simanenka is an engineer specializing in electromechanical systems, industrial equipment, HVAC, and complex technical diagnostics. He focuses on improving engineering decision-making, system reliability, stability, and operational performance through the direct application of artificial intelligence in real-world environments.