Companies are usually overloaded with data that they may not know how to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to “keep learning” as the data increase. In this context, our research question emerges: what AI-based methods, in the literature, could be used to automatize business processes and support the decision-making processes of companies? To fill this gap, in this paper, we performed a review of the literature to identify these techniques. We ensured the usage of methods since they allowed reproducibility and extensions. We applied our search string in the Scopus and Web of Science databases and discovered 21 relevant papers pertaining to our question. In these papers, we identified methods that automated tasks and helped analysts make assertive decisions when designing, extending, or reengineering business processes. The authors applied diverse AI techniques, such as K-means, Bayesian networks, and swarm intelligence. Our analysis provides statistics about the techniques and problems being tackled and point to possible future directions.

We report on the characterization of a recently introduced dual-wavelength pumping scheme for thulium-doped fiber amplifiers using 800 and 1050 nm. Using a counterpropagating pump configuration, 180 mW of total pump power yielded 27-dB small-signal gain and less than 5-dB noise figure. Furthermore, using optical frequency domain reflectometry, the distributed gain in this configuration was evaluated, allowing for optimization of the doped fiber length.