AI models often excel on benchmarks but falter in the real world, where variability, noise, and domain shift redefine the problem itself. Through case studies in X-ray baggage inspection, under-vehicle scanning, and video analytics—including the challenge of rare event detection—this talk examines why even mature technologies like OCR and face recognition rarely sustain their reported accuracies outside controlled settings. It also reflects on the growing use—and misuse—of tools like ChatGPT in solution design. The goal is to highlight a simple truth: genuine progress in AI demands confronting the world as it is, not as our datasets imagine it.