Generative AI is increasingly integrated across industries, necessitating robust evaluation frameworks that go beyond traditional metrics like accuracy to include alignment with human goals and nuanced real-world tasks. In a webinar by Encord and Weights & Biases, experts discussed the evolving demands of AI evaluation, emphasizing the need for continuous, programmatic, and human-in-the-loop feedback systems. Traditional static evaluations often fail to keep pace with rapidly evolving models, creating risks in complex environments such as healthcare or customer-facing applications. The discussion highlighted the importance of incorporating human oversight to catch subtle errors and biases that programmatic checks might miss, advocating for a rethinking of AI evaluation as a core infrastructure component. This approach ensures AI systems are not only accurate but also safe, aligned, and trustworthy, thus reducing product risk and enabling the development of future-ready AI solutions.