Artificial intelligence is changing engineering at a rapid pace. Yet Gopichand Katragadda says the core skills of engineering remain important.
Katragadda, founder of AI company Myelin Foundry, says AI has changed the scale and speed of engineering work. However, he argues that strong fundamentals still form the foundation of good engineering.
He divides engineering skills into three broad areas. They include theory and hands-on basics, component and systems thinking, and disciplined execution.
His comments come as AI tools become common across software development, manufacturing, automotive technology and industrial systems.
AI Should Not Replace Early Learning
Katragadda has urged young engineers to learn their domain before relying heavily on AI tools.
He says AI can support learning, but engineers first need to understand the problems they are trying to solve. Strong subject knowledge also helps professionals ask better questions and evaluate AI-generated answers.
According to Katragadda, the most useful AI prompts often come from deep experience and real knowledge of a subject. AI can amplify existing skills, but it does not replace the process of developing those skills.
This approach places emphasis on learning by doing. Engineers should remain close to the data, components and systems they work with.
Hands-On Experience Needs More Attention
Katragadda also pointed to a gap between theoretical knowledge and practical engineering.
He said Indian engineers have traditionally shown strength in mathematics and component-level engineering. However, he argued that more attention is needed on hands-on work, research and development, and understanding complete systems.
He used engineering design as an example. An engineer may understand how to design an individual component. But a complete product requires an understanding of how different components work together.
This difference becomes important when AI enters physical systems.
An AI model may provide an answer or identify a pattern. Engineers still need to understand how that output interacts with hardware, software, users and real-world conditions.
Systems Thinking Becomes More Important
Systems thinking is one of the key skills Katragadda highlighted.
It involves looking beyond individual components and understanding the relationship between different parts of a system.
For engineers, this can mean understanding hardware, software, data, operating conditions and potential points of failure.
Katragadda said engineers should ask fundamental questions before using AI. They need to understand what they are trying to solve and identify the critical points of failure. They should also examine what can improve on earlier engineering systems.
This approach can help engineers avoid treating AI as a shortcut.
Engineering Requires Disciplined Execution
Katragadda also highlighted execution as a major engineering skill.
He argued that engineers must learn how to develop products effectively while keeping commercial value in mind. The goal should not simply be to build something at the lowest possible cost.
Instead, engineers need to consider whether the final product works well and creates meaningful value.
Documentation is another part of this process.
Katragadda said engineers often remember information but do not document enough of it. This can create problems when teams change or when future engineers need to understand earlier decisions.
He suggested that AI itself could help engineers improve documentation.
AI Is Already Changing Real Engineering Work
Katragadda’s own company, Myelin Foundry, provides examples of how AI can support engineering applications.
The company has developed systems for areas including media, industrial Internet of Things and automotive technology.
One of its systems can reduce video bandwidth by about 40%. Another system can improve low-resolution video for modern displays.
The company has also demonstrated AI applications in automotive systems. One example involves a vehicle rear-view camera that improves visibility during night-time driving.
These applications show why engineering knowledge remains important. The solution involves more than an AI model.
Engineers need to understand the camera hardware, software, processing requirements and conditions at the edge where the system operates.
Engineers Must Understand Hardware and Software
AI is increasingly moving beyond traditional software applications.
It now operates in vehicles, factories, industrial equipment and other physical environments. Engineers therefore need to understand how different technologies interact.
Katragadda said engineers should be confident enough to work across hardware and software. They do not need to know everything from the beginning.
However, they should know how to study technical documents, identify problems and test whether an algorithm works on the actual device.
This approach places responsibility on the engineer to verify AI-generated results.
The ability to review and own the final engineering decision remains important.
AI Can Support Learning
Katragadda does not dismiss AI as a learning tool. Instead, he sees it as an aid that can support theoretical learning and guide engineers towards practical experience.
AI can help explain difficult concepts. It can point learners towards useful information and help them explore technical subjects.
However, the engineer still needs to build direct knowledge.
That means working with components, studying data and understanding how systems behave in real conditions.
The distinction is important because AI-generated information can still contain errors. Engineers need enough knowledge to identify those errors before they affect a real system.
From Components to Complete Products
Katragadda’s argument also reflects a wider shift in engineering education.
Traditional engineering education often divides problems into specific disciplines. Engineers may specialise in mechanical, electrical, software or other areas.
Modern AI systems often require collaboration across these fields.
An intelligent vehicle system, for example, can involve sensors, electronics, software, machine learning and mechanical components. Engineers need to understand how these areas connect.
Systems thinking can help bridge those disciplines.
Documentation and Knowledge Sharing
Another important area is engineering documentation.
As AI accelerates development, teams can produce more code, designs and technical material in less time. Without proper documentation, however, future teams may struggle to understand how a system works.
Katragadda believes engineers should make better use of AI for documentation.
Clear records can preserve technical knowledge and support collaboration. They can also help organisations maintain systems over longer periods.
This becomes particularly important for complex products that require continuous upgrades.
What Engineers Need in the AI Era
Katragadda’s message places AI within a broader engineering framework.
He does not present AI as a replacement for engineering fundamentals. Instead, he describes it as a tool that can increase what skilled engineers are able to accomplish.
For young engineers, this means building strong technical foundations first. Hands-on experience, systems thinking and disciplined execution remain important.
AI can then support those capabilities.
The changing technology landscape may increase the value of engineers who can connect theory with practical problem-solving.
Building Engineers for Real-World AI Systems
The rise of AI is changing engineering jobs and the tools professionals use. It is also changing the types of products engineers can build.
Katragadda’s emphasis on fundamentals, hands-on learning and systems thinking reflects the demands of this new environment.
Engineers will increasingly work with systems that combine AI with physical components and real-world data.
In such systems, knowing how to use an AI tool is only one part of the job. Engineers must also understand the problem, test the solution and take responsibility for the final system.
As AI continues to expand across industries, these skills are likely to remain central to engineering practice.