Companies increasingly rely on their AI investments to solve complex business problems. As your organization grows in its development and use of AI-based applications, so too does your need for scaling your data labeling to produce high-quality training data. One of the key competencies that must be acquired in order to successfully deploy AI applications, is how to evaluate and work with data labeling workforces. The landscape is evolving quickly and businesses that can adapt quickly are likely going to be the ones that gain an outsized advantage in this domain. In this guide, we will cover some of the key factors to consider when evaluating different data labeling services to reduce costs, increase agility and ensure data quality across your labeling operations.