Many data professionals struggle to demonstrate the impact of data science on business outcomes, largely due to difficulties in deploying models into production swiftly. The current state of data science operations is not conducive to agility, a necessity for competitiveness in modern business. To address this, Gartner has introduced XOPs, an approach that enhances collaboration across various operational disciplines like DevOps, DataOps, MLOps, and AIOps, aiming to streamline processes and expedite the delivery of production-ready models. This is crucial as data scientists, particularly in large organizations, face bottlenecks in operationalizing models, which can lead to frustration and a shift in priorities. Solutions such as those offered by Datagran focus on reducing technological duplication, fostering automation, and enabling rapid prototyping and iteration, which are essential for maintaining focus on primary business goals. By facilitating a more agile data science practice, XOPs can help teams deliver models quickly to business applications, thereby proving their business value more effectively.