Infor has incorporated Coleman which is an enterprise-grade, industry-specific AI platform for Infor CloudSuite applications, into the company’s Talent Science solutions. Coleman, a pervasive platform that operates below an application’s surface, mines data and uses powerful machine learning to improve processes such as inventory management, transportation routing, and predictive maintenance, and now human capital management. Specifically, the application will be deployed within Infor Talent Science in the areas of role-based profile development.
Infor Talent Science is a patented, cloud-based Predictive Talent Analytics solution that helps users put the right people in the right positions to achieve business objectives. Infor Talent Science is utilizing Coleman and its AI capabilities to further maximize the predictability of the profile creation process.
“We’ve proven that the single assessment concept provides a significant strategic advantage for businesses today. It enables HR professionals to look across talent acquisition and talent management through one lens. Now, we are using the latest in AI to maximize the predictability of our performance profiles and drive further benefit into our customers’ hands,” said Tarik Taman, GM, IMEA, Infor.
Coleman will be enhancing Infor Talent Science in a variety of ways. First, Coleman leverages machine learning to enhance the predictive models used in the Performance Profile creation system. Instead of creating a predictive model through an organization’s data alone, now Coleman leverages the tens of millions job candidates in the Infor Talent Science database to create a model that is more accurately calibrated to the global workforce – while still maintaining the high level of validity and legal defensibility critical to making HR decisions. Additionally, Infor Talent Science is now utilizing Coleman to create an enhanced system that increases the precision and validity of the prediction models themselves. Each time a model is created, Coleman leverages AI and machine learning to increase the predictability of the model. If the system identifies a strategy for improving validity, it will then dynamically modify the model creating better circumstances for success.