Enterprises aiming to adopt AI effectively must develop evaluation systems that account for real-world complexities and specific business needs, rather than solely relying on public benchmark scores. While benchmarks provide an initial measure of a model's capabilities, they often fail to predict performance in practical applications due to their focus on peak performance and general intelligence rather than specific skills necessary for particular deployments. Companies should create customized evaluation processes that include targeted test sets, human evaluators, and continuous iteration to ensure models meet their unique requirements. In particular, the rise of agentic AI, which involves adaptable, multi-step processes, necessitates new evaluation strategies that go beyond traditional benchmarks to capture how these systems operate in dynamic, real-world conditions. Therefore, successful AI deployment involves a layered approach, integrating broad benchmark assessments with private, business-specific evaluations to ensure models are reliable, accurate, and aligned with enterprise goals.