Top 6 Biggest Business Myths About ML Projects

Tue, 15 Jul 2025 12:25:00 +0000
Machine Learning, ML project, ML project management

I imagine how things seemed in the minds of some clients I’ve worked with:

"We have the data, we know the problem – just build a model. We can even use AutoML. Once we have the model, we’re done. Let’s put it in production, and it’ll just work on its own for the next few years."

Unfortunately, the reality is more complex.

Machine learning is not a plugin for Excel. It’s not a plug-and-play component you can buy, install, and use for years without effort. It’s a living system that relies on data, changes over time, and needs constant improvement and updates. It requires patience, experimentation, and a good dose of humility about what we (still) don’t know.

This article is my attempt to explain what a machine learning project looks like from an engineer’s point of view. What problems clients usually face. It covers clients' problems, what breaks down, what surprises us, where the real risks are, and how to reduce them.

If you’re considering using AI in your product or planning to work with an ML team, this article might help you avoid common mistakes. Or at least help you understand why the model didn’t magically materialize after a week.

Myth 1: AI is good for everything

Interest in AI tools has skyrocketed with the rise of systems like ChatGPT and Midjourney. They work quickly, often deliver impressive results, and seem almost magical. It's no surprise that some business teams have started to see AI as a "universal solution."

But as a machine learning engineer, I often see clients with unrealistic expectations. They want a model that can predict everything, make no mistakes, work instantly, handle every scenario, and adapt independently. The problem is - such models don’t exist.

The role of a machine learning team isn’t just to build a model, but also to educate clients: where machine learning makes sense, and where it doesn’t. Where it can deliver real value, and where a classic data analysis or even a simple rule in the system would be a better fit.

Understanding these limitations is part of what makes an ML project successful. As technical partners, it’s our responsibility to highlight those boundaries and help find the best possible solution, even if it’s less flashy but much more realistic.

Myth 2: Just throw in the data and ML will do the rest

One of the most common beliefs about machine learning goes like this:

"We have data, so we feed it into a model, and it will learn and solve the problem."

Sounds logical, right? After all, data is the "fuel" for AI - so the sooner we pour it in, the sooner the engine runs. But sticking with the engine analogy, if we use low-quality or contaminated fuel, the engine breaks down and doesn’t go anywhere. That’s why the phrase "garbage in, garbage out" is widely used in ML. You can't expect good results if you train a model on poor-quality data.

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