Artificial intelligence (AI) and machine learning (ML) are transforming industries by enabling automation, optimization, and co-creation of business value. However, the deployment of ML models into production ecosystems and enterprise-wide information systems remains a significant challenge, partly due to the lack of standardized frameworks addressing the full ML lifecycle. While existing lifecycle models focus heavily on pre-deployment phases like data preparation and model training, critical post-deployment tasks such as monitoring, maintenance, and governance are often overlooked. To address this gap, we conduct a systematic mapping study to analyze existing ML lifecycle frameworks, identifying their limitations in post-deployment coverage. Building on these findings, we propose a research design to create a deployment-aware ML lifecycle, integrating empirical insights from multiple case studies across industries. This comprehensive framework aims to support practitioners in managing the complexities of real-world ML deployments and ensuring the sustainability of ML systems within dynamic organizational contexts.