Our fast-paced digital economy shaped by global competition requires
increased data-driven decision-making based on artificial intelligence (AI) and
machine learning (ML). The benefits of deep learning (DL) are manifold, but it
comes with limitations that have - so far - interfered with widespread industry
adoption. This paper explains why DL - despite its popularity - has
difficulties speeding up its adoption within business analytics. It is shown -
by a mixture of content analysis and empirical study - that the adoption of
deep learning is not only affected by computational complexity, lacking big
data architecture, lack of transparency (black-box), and skill shortage, but
also by the fact that DL does not outperform traditional ML models in the case
of structured datasets with fixed-length feature vectors. Deep learning should
be regarded as a powerful addition to the existing body of ML models instead of
a one size fits all solution.

该论文论述了深度学习虽然备受欢迎，但在商业分析领域普及存在困难，原因是计算复杂度、数据结构、缺乏透明度（黑匣子）和人才短缺等问题，而且在结构化数据集上无法优于传统的机器学习模型。因此，应该把深度学习视为现有机器学习模型的有力补充，而非通用解决方案。