Should you learn generative AI in 2025? No. Why? Because it’s not mainstream yet. But what about two years, five years, or 10 years from now? As of 2025, 35% of the companies worldwide have adopted AI.
And that’s not a big number, which means there’s a large chunk of companies that are still not using AI. So is it worth your time to learn generative AI? Maybe. But what about 10 years from now when the adoption rate is going to be 100% and every company in this world who touches technology is using AI? There are two use cases when it comes to learning generative AI.
Either you can learn as a consumer slash user of AI, aka prompt engineering. And the second way of learning is as a creator of AI, where you are actually developing AI technologies using machine learning, deep learning, and so on. In this article, I’m going to be focusing on number one, prompt engineering, and why as a tech and non-tech employee, you should be learning how to use generative AI.
First of all, if you don’t know what generative AI is, then you’re probably living under a rock. Think of generative AI as like a human assistant who is there to help you brainstorm ideas, proofread your essays, come up with a structure, help you write code, debug code, and much more. And I’m going to talk about two types of users in this article.
One is technical users, people who are in have programming job, people who work in tech, people who are interested in tech. And the second bucket is people who do not work in tech, they work in job families, such as recruiting, customer service, construction, and so on. So the first group that I want to talk about is tech audience and some use cases as to why you should learn prompt engineering.
So let’s take an example. There are two people, Jessica and Jack, they’re both software engineers, their company has adopted generative AI. Every month, Jessica and Jack go on call as their rotation goes, and they get assigned tickets that they have to solve.
Let’s say they’re both assigned three tickets each where they have to solve a customer’s problem using debugging code. Their company is one of 35% of the companies that are currently using generative AI in their day-to-day task. Jessica is assigned three tickets, Jack is assigned three tickets.
Jessica uses generative AI to debug the code and is able to solve two of the three tickets per month on average. Jack is a smart worker and he has tons of experience. He’s really good at what he does, but he doesn’t use generative AI.
He is able to solve 1.5 out of the three tickets on average. Now at the end of the year, when you tally both Jessica and Jack’s work, Jessica is able to solve 85 tickets in that year, and Jack is able to solve 56 tickets. Now both are amazing employees, but one is able to perform more in the same salary, same time frame, compared to the other.
And when leadership evaluates the performance at the end of the year, who is going to stand out? Jessica or Jack? What do you think? So the moral of the story is that if your company lets you use generative AI, you should start using it to your advantage to solve problems in a smarter way that saves you time and saves your company time. For job families such as data scientists, there are already so many tools out there. For example, rag has been taken off by storm.
And if you’re not convinced that you need to learn generative AI as a tech worker, you can watch videos on youtube where you can find more detail on things that you can actually do with generative AI today. Do your job whether you are a programmer, software engineer, or a data scientist. If you want to learn prompt engineering through structured platform, then Google recently launched Google AI Essentials.
This specifically focuses on turning you into a power user so you can incorporate prompt engineering in your day-to-day tasks. Google’s course is available on Coursera. The course teaches you four core things.
It talks about how to develop ideas and make more informed decisions with daily work and do things fast. It also teaches you how to write clear prompts to get the best answer. Remember, generative AI is as good as you are.
So if you write smart prompts, you’re going to get smart answers. So it’s very important for you to learn how to write clear structured prompts so you can get better answers. One of the things that I personally learned from using generative AI is that the first response is not going to be the response that you need to use.
You need to iterate on it to get the best response so you can give it enough context and get the best response possible from an LLM model, whether you are using ChatGPT or using Google Gemini. And this course specifically teaches you how to have that structure, how to do the iterations, and how to get best answers possible from LLM. The course is focused on Google Gemini because it’s created by Google, but the tricks that you’re going to learn can be applied to ChatGPT, Anthropic, or any of the other LLM models that is available out there.
It’s an intro level course, so if you’re not familiar, this is actually a great place to start. I’m linking the course in the description below. Now let’s talk about the non-tech audience and if they should be learning generative AI.
So when we’re talking about non-tech audience, it could be people who work, let’s say, in customer service, people such as who have physical jobs like construction, plumbing, medical professionals, and so on. These are the people who don’t have to deal with technology day-to-day basis. So the question is, does non-tech audience need to learn generative AI? Well, let’s actually take an example like we did for the tech audience and walk through a use case.
So again, let’s say we have two people, Jeffrey and Jennifer. Jeffrey and Jennifer work in customer service. Their company has incorporated generative AI, and Jeffrey has learned prompt engineering and he knows how to use generative AI in his day-to-day tasks, whereas Jennifer refuses to use generative AI and she believes she can provide the best customer support without relying on an LLM model.
Both of these people get 50 calls on average. I know this is a lot of customer service calls, but this is a typical number. While Jeffrey is solving customer problems, he uses generative AI to find solutions to the problems that customers regularly ask, while Jennifer, on the other hand, uses her experience and is able to solve a customer’s problem.
It takes Jeffrey seven hours, while it takes Jennifer eight hours. So it’s one hour of saving per day. One might say it’s not a lot, but when you add up for a full year, that ends up being a lot of time saving for Jeffrey, as well as for the company.
And essentially, Jeffrey is able to do more in the same time that both Jennifer and Jeffrey have. Anyways, this is a very simple example, but the idea that I want to get to, regardless of what job family you are in, whether you are in tech or non tech, generative AI potentially is on the path to become essential in your day to day job. And now you’re probably like plumbing plumbers don’t need to know generative AI.
Well, guess what, when they run into issues, I’ve had many plumbers that come to my house, they run into issues. And the first time they look at it, they’re like, we need to figure out what exactly is the problem. I have seen them do search and trying to figure out what if this is happening, what does it mean? Technically, they can also use generative AI if this is a known problem.
I don’t know if medicine is an example I want to use. But I’m sure there are use cases for people who work in medicine. Although I wouldn’t want to go to a doctor who is relying on LLM to give me an answer.
And I guess that’s a problem in its own and not something that I can solve. But the whole point in this article is that generative AI is important. Yes, it’s probably not as prevalent today, and you will be fine if you don’t learn it.
But a few years from now, when AI adoption continues to accelerate, and we get to a point where we have 100%, or even 95% adoption, the chances are you and I are going to end up at a place that is using generative AI into their day to day work. And before we get to that point, you can start preparing now so you can get ahead of the curve. And I know at this point, people are scared that generative AI is going to take over their jobs.
Believe me, generative AI will do some automation, which is very simple, but it can never replace a human. It is about time that we incorporate generative AI into our day to day work, make our life easier, so we are able to use our time in a more productive way. Let me know your thoughts in the comment.
Do you think we are on the path to 100% adoption of AI? And what are your thoughts on learning generative AI in 2025, 2026, and beyond? Alright, I hope you’re having a beautiful day, and I’ll see you in the next one. Bye!