Oh the classic AI expectation vs. reality gap! When you're pitching AI to stakeholders, it's all clean algorithms and elegant solutions—just wave the magic wand and voilà! But once that same model hits production and faces real-world data? Suddenly your sophisticated neural network is dual-wielding guns in fuzzy slippers trying to make sense of edge cases nobody anticipated. Every ML engineer knows that feeling when your beautifully trained model that worked flawlessly in the controlled environment starts hallucinating the moment it encounters production traffic. No amount of hyperparameter tuning can save you from the chaos that ensues when your AI meets actual users!