With the rapid advancement of AI and machine learning, we’re now generating and analyzing data at unprecedented speeds. While this opens exciting possibilities, it also raises an important concern: are we beginning to rely too heavily on algorithmic outputs, potentially sidelining the critical role of human insight, creativity, and intuition in research? Scientific discovery has always been driven not just by data, but by the questions we ask and the meaning we derive from patterns. How can researchers ensure that AI remains a tool to enhance, not replace, human understanding? What strategies or frameworks can we adopt to maintain the balance between data-driven automation and human-led inquiry?