In this lecture Zach dives into end-to-end AI applications, discusses what AI excels at, the roadblocks that hinder its value, and strategies to overcome those challenges. He shares that around 60–70% of AI projects fail, slightly better than the 80% failure rate often cited for data science projects. Zach explores key AI use cases—prediction, detection, and optimization—while emphasizing that not every problem requires large language models.