  The realization that AI-driven decision-making is indispensable in todays
fast-paced and ultra-competitive marketplace has raised interest in industrial
machine learning (ML) applications significantly. The current demand for
analytics experts vastly exceeds the supply. One solution to this problem is to
increase the user-friendliness of ML frameworks to make them more accessible
for the non-expert. Automated machine learning (AutoML) is an attempt to solve
the problem of expertise by providing fully automated off-the-shelf solutions
for model choice and hyperparameter tuning. This paper analyzed the potential
of AutoML for applications within business analytics, which could help to
increase the adoption rate of ML across all industries. The H2O AutoML
framework was benchmarked against a manually tuned stacked ML model on three
real-world datasets to test its performance, robustness, and reliability. The
manually tuned ML model could reach a performance advantage in all three case
studies used in the experiment. Nevertheless, the H2O AutoML package proved to
be quite potent. It is fast, easy to use, and delivers reliable results, which
come close to a professionally tuned ML model. The H2O AutoML framework in its
current capacity is a valuable tool to support fast prototyping with the
potential to shorten development and deployment cycles. It can also bridge the
existing gap between supply and demand for ML experts and is a big step towards
fully automated decisions in business analytics.
