How do you set up machine learning projects to be successful, and what are the systems and processes you need to put in place to get the most out of your AI platform? This talk will describe the key patterns many organizations have followed to start shipping ML at scale. The most successful machine learning applications are directly tied to improving metrics that matter for your business, but how do you identify and prioritize those projects? How do you power your new AI platform when you can't get access to training data or organizational resources? We'll describe how to make a clear business case for AI projects, build a prioritized roadmap, and make foundational investments in areas like data pipelines, tracking, analytics, monitoring, and data quality. With the right conditions in place, you will see outsized impact across your company as new data products and ML models are rapidly deployed at scale.