One of the biggest potential applications of machine learning systems is the mining of important efficiencies for business processes and operations. This field is still booming as machine learning evolves, and vendors offer companies more powerful tools to evaluate business scenarios.
In general, machine learning can provide efficiencies through examining a greater range of possibilities and choices, some of which may seem inefficient on their face. An excellent example is a process called simulated annealing that involves algorithms that produce results in some of the same ways that engineers cool metal after forging. In a sense, the system takes in the data and examines these inefficient paths or outcomes to find whether, if combined, altered or manipulated in any way, they can actually produce a more efficient result. Simulated annealing is just one of many ways that data scientists can create complex models that can root out deeper efficient options.