Given the plethora of threats to coral reef ecosystems, marine biologists have been racing to both document reef decline and devise conservation solutions that could thwart coral extinction or partially restore ecosystem function. In most cases, local-scale stressors are not adequately addressed prior to initiating these projects, not because researchers do not appreciate their impact but because in many cases it is not possible to do so on a timescale that is commensurate with coral rescue. What this means, though, is that many coral reef conservation initiatives are doomed to fail from the start, notably many restoration projects that seek to grow corals for later outplanting on the reef; if temperatures continue to rise, and seawater quality continues to deteriorate, only those projects that stress-harden, genetically modify, or, more generally, modulate the underlying biology of the farmed corals have any hope of success. Ideally, a manager would collect some rudimentary data on the habitat in question, then use statistical tools to determine the conservation approach that has the highest probability of success given what is known about the habitat in question. This can be achieved with machine-learning, a modeling approach based on artificial intelligence (AI), though the marine biology field has generally been slow to leverage breakthroughs in such “big data” analytics that have revolutionized other disciplines, such as advertising. I discuss a “coral rescue” flow-chart and show how an AI could be trained to robustly project the conservation approach with the highest probability of success (e.g., maximum coral cover, longest coral colony lifespan, etc.).