Artificial Intelligence (AI), which has been in development since its inception at the Dartmouth Workshop in 1956, has experienced cycles of enthusiasm and stagnation, known as "AI winters." Currently, the field is in an "AI spring," characterized by high investor interest and numerous applications such as visual and voice recognition and predictive analytics, primarily driven by deep learning techniques. While progress has been remarkable in "narrow AI" which focuses on specific tasks, advancements towards "general AI" and "super AI" remain elusive, with experts like Andrew Ng advising against premature concerns about superintelligence. The integration of AI into business operations is akin to the widespread adoption of databases in the 1980s, with potential applications ranging from customer service enhancements to medical diagnostics and autonomous vehicles. For startups and established enterprises, the focus should be on leveraging existing AI techniques to unlock data value, improve products, and maintain competitiveness, while considering AI as a counterbalance to human biases. As organizations transition towards being AI-native, the quality and utilization of data will become key differentiators, much like the evolution of web and mobile-native companies in previous technological shifts.