What is skills inference?

skills inference (noun)

The process of using artificial intelligence to analyze employee data in order to quantify skills proficiency and identify areas for improvement.

To retrain employees, companies need precise insight into workforce skills, including those that are missing but essential for future success.

A report from the MIT Center for Information Systems Research details Johnson & Johnson’s 2020 effort to improve digital expertise among 4,000 technologists before expanding to other units the following year. The company’s skills inference process had three steps:

1. Skills taxonomy. J&J identified 41 “future-ready” skills, like master data management and robotic process automation, and grouped them into 11 capabilities.
2. Skills evidence. The company selected the appropriate sources of employee data for skills inference, such as recruiting databases and learning management systems.
3. Skills assessment. J&J trained a machine learning model to measure each technologist’s proficiency in each future-ready skill, and workers rated themselves using the same scale. To gain worker buy-in and mitigate bias in self-reporting, the process was used only for skills development, not performance evaluation.

The process proved to be powerful. Employee use of J&J’s professional development ecosystem increased by 20% after the first round of skills inference. And executives gained access to heat-map data about skills proficiency by geographic region and business line.

How companies can use AI to find and close skills gaps