Many engineering students view Artificial Intelligence (AI) merely as a subject. Once a semester ends, they think, “AI is finished.” However, from an industry perspective, AI is a capability—the power to implement human-like thinking rapidly, extensively, and continuously. This is why companies are investing heavily in it. Students must understand AI not as “hype,” but as a mindset used to solve business problems.
Classroom vs. Boardroom
In a college classroom, AI is defined by definitions, algorithms, and exams. In a boardroom, AI is about decisions, risks, and responsibility. For example, Google Maps is not just a tool to show directions; it is an intelligent system that understands conditions and makes decisions. It doesn’t take exams, but it learns from past data, observes the present, and predicts the future. This is the gap seen in job interviews—students know the concepts but cannot explain them as a cohesive narrative.
Mindset is Key
Do not think that you cannot excel in AI without mastery of mathematics. While core research roles require deep math, most AI jobs are applied in nature. What matters is how you define a problem, how logically you think, and how you interpret data. Math is just a tool; the real key is the mindset. Companies look for candidates who can ask the right questions, identify data limitations, and translate a business problem into a technical one.
A Continuous Journey
Learning AI is like a marathon.
First Year: Develop coding logic and analytical thinking.
Middle Years: Gain a deep understanding of AI concepts.
Final Year: Master real-life problem solving through projects and internships. Just as Google Maps didn’t become a top platform overnight, an engineering career requires years of evolving and learning.
Your Thought, Their Tool
Generative AI tools have changed how students learn. Used correctly, they act as personal mentors; used wrongly, they weaken fundamental understanding. Just as you must learn math despite having a calculator, your original thought must remain central even when using AI. The goal should be understanding, not just copying assignments. The solution isn’t to distance yourself from AI, but to develop responsibility alongside it.
New Career Horizons
We are now entering the era of Agentic AI—systems that, when given a goal, decide the necessary steps and take action themselves. While traditional automation follows fixed rules, Agentic AI understands the situation to make choices. This evolution will create new roles like AI Orchestrators, AI Supervisors, and Human-AI Interaction Designers.
Opportunities for All
It is a myth that AI is only for Computer Science students or those who code. The AI ecosystem is vast. We need AI analysts, domain experts, consultants, and system integrators. Students from Mechanical, ECE, and Civil backgrounds are achieving great success by combining their domain expertise with AI. The industry doesn’t just want “pure code”; it wants a blend of domain knowledge and AI.
Jobs Will Not Vanish
Will AI take away jobs? Google Maps didn’t eliminate drivers; it enhanced their navigation intelligence. Similarly, jobs won’t disappear—they will transform. Future engineers must become professionals who work with AI. The journey of AI that started on the roads has now entered classrooms and careers. AI is not a threat; it is an opportunity—but only for those who are always prepared.
