In the tech world, people often use words like “AI” and “Machine Learning” like they mean the same thing, but they don’t. This mix-up causes problems and especially when businesses try to use Machine Learning in their operations.
Sure, AI sounds impressive. It conjures images of futuristic robots and advanced intelligence. But here is the truth: most of what we call AI today is not really that intelligent. It is mostly about doing math and guessing what might happen, rather than thinking like a person.
The problem arises when businesses buy into the hype without understanding what they are getting into. They hear “AI” and think it is a magic bullet that will solve all their problems. But the reality is far from it. Many Machine Learning projects never make it past the modeling phase, let alone into actual deployment where the value lies.
Take self-driving cars, for example. A few years ago, they were touted as the future of transportation, but now? They are more like this decade’s jetpack—cool in theory, but far from reality. Why? Because we did not realize how hard it would be to put these things into action.
And it is not just self-driving cars. Across industries, businesses struggle to deploy Machine Learning models because they lack the proper infrastructure or simply don’t understand the value.
But here is the deal: things can be different. If businesses plan well and know what Machine Learning can and can’t do, they can use these models successfully and get good results. It is not simple, but it can happen.
So let us stop making AI sound fancy and start thinking about what is important: using Machine Learning to actually help businesses in a practical way.
