The data deluge demands businesses leverage artificial intelligence (AI) and machine learning (ML) for swift adaptation. Deep learning (DL), a powerful ML technique, unlocks deeper insights but faces limitations hindering its widespread adoption in business analytics. This article explores the key challenges of DL and emphasizes its role as a complementary tool, not a replacement, for traditional ML models. Research shows that DL models in classification tasks can perform quite well on structured datasets like powerful gradient boosting models. On the other hand, in prediction tasks, DL appears to be weaker compared to traditional ML models. In addition to experimental research based on four usage cases in the industry, the article also provides a comprehensive discussion of these results, their practical implications, and a roadmap for future research.