Predictive analytics (PA), a type of people analytic, is a practice that takes a set of various statistical tactics that analyze historical data and outcomes. These techniques then try to create a formula, or algorithm, that best mimics these historical outcomes. This information is then used as the basis for predicting future outcomes. You can find a fun example of PA in the movie Moneyball, where Oakland Athletics’ General Manager Billy Beane leveraged analytics to evaluate his potential roster and identify statistics that were highly predictive of how many runs a player would score. By gaining insights like these, organizations can increase the likelihood of making better business decisions, particularly when faced with conditions of uncertainty. However, only two percent of organizations are at the highest level of people analytics maturity–making PA a source of competitive edge. As organizations continue to prepare for the future of work amid uncertainty, this article outlines a few questions–such as what work can / should be automated or augmented or what skills and capabilities may need to change–that can help frame how PA can provide insights about the future and inform decision-making. The article also offers a few ideas on shifting the analytical lens from the past (lagging indicators) to the future (leading indicators).