We’ve seen how using a “good enough” solution for personalization, search and recommendations (S&R) is limiting and potentially damaging to the viewer’s user experience. And we’ve discussed how solutions that allow for continuous change and optimization lead to more robust engagement and lower churn rates. Now let’s take a deeper look into the different types of changes being made and how they reflect the various on-going needs of the business, as well as the limits of relying solely on machine learning (ML) and artificial intelligence (AI) models. The limits of machine learning When it comes to improving personalization, search and recommendations, there is a belief that ML and AI models are fully capable of handling all use cases in a way that requires no human intervention. Our experience shows that this isn’t the case. Companies have business objectives that are often either at-odds with model results or need to work…