Enterprises face a difficult choice between maintaining legacy mainframes or undertaking risky big-bang migrations. Google Cloud offers an alternative iterative modernization strategy powered by AI and the cloud. This approach acknowledges that mainframe modernization involves more than just code conversion, encompassing complex dependencies, data formats, transaction monitors, workflows, and proprietary interfaces. Google Cloud's solution combines its Gemini models with specialized mainframe modernization products across four pillars: assessment, modernization, de-risking, and data migration.The Mainframe Assessment Tool (MAT) uses AI for reverse-engineering legacy applications, providing insights into dependencies, business rules, documentation, and domain discovery. Modernization offers flexible paths, including rewriting applications for innovation or performing deterministic, like-to-like modernization to preserve behavior. Google Cloud's Dual Run process ensures safety by running workloads simultaneously on both mainframe and cloud environments, comparing outputs to validate equivalence. The Mainframe Connector facilitates data migration to various Google Cloud services, enabling offloading of processing and unlocking siloed data. This comprehensive approach addresses real-world mainframe modernization challenges by understanding existing processes, modernizing applications, de-risking before go-live, and modernizing data. Google Cloud invites enterprises to test this AI-accelerated approach through pilot programs starting with automated codebase assessment.