Globally there are too few park rangers to prevent the illegal trade of wildlife across borders, or poaching. In response, Spatial Monitoring and Reporting Tool (SMART) was created by a coalition of conservation organizations to take historical data and create geospatial mapping tools that enable more efficient deployment of rangers.

SMART had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching. Then a new analytic tool, the Protection Assistant for Wildlife Security (PAWS), was created to use artificial intelligence (AI) and machine learning (ML) to try to predict where poachers would be likely to strike.

Jonathan Palmer, Executive Director of Conservation Technology for the Wildlife Conservation Society, already had a good data analytics tool to help park rangers manage their patrols. Would adding an AI- and ML-based tool improve outcomes or introduce new problems?

Harvard Business School senior lecturer Brian Trelstad discusses the importance of focusing on the use case when determining the value of adding a complex technology solution in his case, “SMART: AI and Machine Learning for Wildlife Conservation.”