Many professionals entering the world of AI make basic mistakes that end up hindering their growth. If you want to avoid stagnation and ensure your career takes off from the start, watch out for these three common pitfalls—and learn how to avoid them.
The first mistake is focusing only on theory without getting hands-on experience. Studying concepts is important, but AI requires practice. Those who spend months consuming content without applying their knowledge to real-world projects end up with superficial learning. The solution? Start building right away, even with simple projects. Try platforms like Kaggle, recreate basic algorithms, or contribute to open-source repositories. Practice is what turns theory into skill.
The second mistake is neglecting LinkedIn and GitHub. These platforms are your business cards in the tech industry. An outdated profile or an empty GitHub means missed opportunities. Invest time in keeping your LinkedIn professional, with well-described experiences and strategic connections. On GitHub, store your projects—even small ones—and make your learning process clear. Recruiters and peers in the field assess your potential through these tools—don’t overlook them.
The third and most dangerous mistake is trying to learn everything about AI at once. The field is vast, with branches like Machine Learning, Computer Vision, and NLP, and those who try to cover everything end up mastering nothing. Instead, choose an initial path, dive deep into it, and only then expand into other specializations. Mastering a specific topic will make you a more valuable professional than someone with only superficial knowledge of multiple areas.
If you’re just starting out, reassess your strategy. Applying knowledge, maintaining a strong professional profile, and focusing on a specialization will make all the difference in your growth. A career in AI is promising—but only for those who avoid these mistakes from the beginning.
