Developing artificial intelligence-powered (AI) applications is the targeted focus of major R&D investments across the technology world and the Weather Enterprise. However, the potential for these tools to improve weather analyses or forecasts depends on one’s perspective; veterans of the weather enterprise are rightfully skeptical of AI as a “magic bullet" to improve forecasts, given the sophistication of our current tools. AI-powered applications do have the potential to augment many practical aspects of meteorology, ranging from improving the automation of publicly-consumable weather data products, to both analyzing large volumes of real-time data and helping to refine forecasts. But to accomplish this, the community will both need to identify gaps in our analysis and forecasting ecosystems, as well as invest in software and data engineering to effectively connect our vast atmospheric data and model archives to machine learning software.