🤖 AI Summary
Deep learning (DL) faces an “expectation–reality gap” in business analytics: it delivers no substantial performance gains over traditional machine learning (e.g., random forests, XGBoost) on structured data (fixed-length feature vectors), and its adoption is hindered by five key barriers—high computational complexity, poor model interpretability, lack of scalable big-data infrastructure, shortage of domain-specialized AI talent, and insufficient executive sponsorship.
Method: This study conducts the first systematic, empirical evaluation of DL’s effectiveness and interpretability on standard business-oriented structured datasets, integrating content analysis with multi-dimensional comparative experiments against established ML baselines.
Contribution/Results: We demonstrate that DL is not a universal replacement for traditional ML but rather a complementary enhancement tool. The work proposes a pragmatic, enterprise-focused AI adoption framework for model selection—bridging the cognitive gap between theoretical hype and industrial practice—and advances rational, sustainable AI deployment in commercial settings.
📝 Abstract
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.