This chapter covers
- The iterative process of machine learning
- Unsupervised and supervised learning
- Time series analysis and trend detection
- Personalization through recommendations
While AI is often hyped as a “new” technology, all of us have been consuming it for years and on a daily basis—think Google search, your (imperfect) spam filter, or the entertainment recommendations you get and follow on Netflix or YouTube. Often, we forget about the AI that powers these applications because it runs in the background and doesn’t bother us with too many mistakes. Predictive AI is at work in these applications—a class of algorithms that distill valuable insights from large data quantities. For example, they bring structure into unstructured data, classify data points into meaningful categories, and uncover patterns and relationships that are invisible to humans.
Many companies today skip directly to generative AI, overlooking predictive AI as the critical foundation for data-driven decision making and operations. They sit on a wealth of data but fail to activate it for their business, relying on static knowledge, individual past experiences, and subjective gut feeling. By contrast, a data-driven organization uses large-scale data about its operations, stakeholders, and the larger market context, adding confidence and objectivity to its decisions and actions.