The most notable advancement within the realm of automated preventative maintenance are the advancements in predictive maintenance powered by artificial intelligence (AI). Instead of depending upon strictly scheduled maintenance designed around rigid intervals based on book values (e.g., x hours, x cycles), we are now utilizing real-time monitoring through continuously available, streaming telemetry to monitor mechanical data, diagnose all potential mechanical issues, and take proper corrective actions long before they become serious failures (i.e., time to crash). This advances our approach to risk by shifting from a reactive business model (i.e., performing a corrective action only when a problem is detected) to a proactive business model (i.e., using real-time data to track and model the health of the mechanical system regardless of the occurrence of a detectible problem). This is also significant in that it allows us to make informed, data-driven decisions rather than second-guessing or assuming (e.g., legacy maintenance intervals) and thereby decreases potential for human error and/or missing wear and tear. Safety, whether through software or air travel, is not necessarily about eliminating all risks but rather about having systems in place to help you to foresee trouble before it arrives. The difference in providing safety is whether or not you maintain visibility of the rapidly changing environment in which you operate.