Intelligent automation is a class of IT automation tools that utilize machine learning (ML) and artificial intelligence (AI). ML relies on algorithms to predict outcomes based on data. The algorithms identify trends, commonalities, and correlations between variables, using statistical analysis to predict outcomes and future events. Then, as the program continues to run, the algorithms further improve their predictions based on subsequent datasets. Any program, application, or system that can autonomously make decisions and take actions based on ML, has AI. Intelligent automation refers to any IT automation tool that can improve its own processes and outcomes, optimize IT resources, and improve efficiency by analyzing data and augmenting human decision-making.

In our fast-paced world, organizations require flexible IT infrastructures that can quickly adapt to dynamic, real-time demands. With ActiveBatch, users can easily optimize the distribution of workloads to improve the likelihood of on-time, successful job completions while reducing idle machines by leveraging machine learning and predictive analysis. Users can assign custom Dynamic Queue Characteristics, instructing ActiveBatch to evaluate multiple servers before submitting Jobs to the servers on which they will run. For example, execution machines can be monitored and evaluated at runtime for characteristics like available disk space, registry values, or the presence or absence of a particular application. Dynamic Queue Characteristics are useful because it is difficult, time-consuming, and tedious for IT personnel to manually search hundreds (or thousands) of servers with specific characteristics to successfully run tasks.