In data science and AI, certain beliefs have become dogmatic truths. Among these, one stands tall: that deep learning and highly complex models hold the keys to all predictive power. Conferences brim with glossy presentations of neural networks, LinkedIn feeds echo with tales of ever-deeper architectures, and job postings list PyTorch and TensorFlow as bare necessities. Yet, amid this din, I wish to propose an unpopular — and, dare I say, heretical — opinion: In many real-world business cases, simpler models outperform their deep learning counterparts, and the blind pursuit of complexity often leads us astray.