Experiencing The Realities of Disruptive Technology

I still remember my first encounter with CAD software in the early 1990s. I had just learned to draft using  drafting table, T-squares, and mechanical pencils. And then here comes CAD, I was excited—and admittedly a little naive—about the possibilities. 

During my university years, I was required to take a compulsory drafting course, even though CAD had already become an industry standard. I spent time drafting by hand, carefully measuring every line and angle, despite knowing that digital tools had long since taken over professional workflows. After the learning how to draft my expectation of CAD was that it would be a tool that would instantly produce my designs for me, yes I had inflated expectations of a magical tool that would do all the work for me. I would think of an idea and like magic, the computer would take care of everything.

Of course, reality quickly set in. Instead of effortlessly generating perfect blueprints, I had to learn an entirely new way of thinking. CAD didn’t replace the design process; it transformed it. I still needed to have the ideas, understand the engineering principles, and apply them. Drawing the first line, starting with defining points then connecting them, was not a trivial task. CAD was a powerful tool, but it required skill, experience, and thoughtful direction to produce anything meaningful. It didn’t do the work for me—I had to learn how to use it effectively.

Fast forward to today, and I see a strikingly similar pattern emerging with the rise of AI—especially Generative AI (GenAI). There are a similar confused and inflated expectation that GenAI will simply generate perfect content, solve complex problems, and even replace human creativity. But just like CAD in the ‘90s, GenAI is not a magic wand. It’s a tool—one that still relies on human intelligence to be effective.

The misconception that AI will replace human ingenuity overlooks a crucial reality: AI doesn’t think, ideate, or problem-solve on its own. It relies on human direction. Just as I had to learn how to structure my designs for CAD, today’s professionals must learn how to craft effective prompts, refine outputs, and iterate toward meaningful results. Prompt engineering isn’t just about asking the right questions; it’s about framing problems, guiding AI to useful insights, and applying critical thinking to refine its output.

Take this blog post as an example. You can’t simply tell a GPT AI tool to “write a perfect blog post this specific comparison” and expect a meaningful result. I had to define the scope, structure the request, and continuously iterate to get content that aligns with what I, emphasis on "I", wanted to convey. These are my ideas and my experiences - I had to include these in the prompt. AI amplifies human creativity—it doesn’t replace it. The key here is that ChatGPT did make me more productive, I wrote this post in minutes versus a few hours, including the graphic.

Just as CAD revolutionized drafting and engineering without eliminating the need for skilled designers, GenAI is reshaping many industries making content creation, coding, and problem-solving—but it still requires expertise and direction. The future isn’t about replacing human intelligence but augmenting it—"humans in the loop." Those who understand how to harness AI and its applications will be the ones who truly unlock its potential, especially in manufacturing operations where precision, problem-solving, and adaptability are crucial. Technology evolves, but one thing remains constant: the value of human ingenuity. Whether it’s designing with CAD or leveraging GenAI, success comes not from the tool itself, but from how we apply it with expertise and intent.