As organizations increasingly adopt artificial intelligence (AI) for operational efficiency and innovation, they face the challenge of managing skyrocketing costs, particularly in multicloud environments where AI's computational intensity can drive up expenses. AI observability, which monitors IT system performance and costs, is a strategy that can help balance these benefits and costs. It enables organizations to understand the return on investment of their AI initiatives by tracking resource utilization throughout the AI lifecycle and offering insights for cost optimization. By integrating AI observability with FinOps—a cloud management philosophy—businesses can control costs by leveraging cloud and edge-based AI approaches, containerization, and continuous performance monitoring of AI models. Additionally, optimizing AI models, managing the AI lifecycle proactively, and using generative AI alongside other technologies like predictive and causal AI are essential strategies for achieving cost-effective AI operations. As AI adoption continues to rise, organizations must focus on cost-effective AI optimization to thrive in the AI-enabled era.