Rally tackled the challenges of win-loss analysis using AI to uncover why deals succeed or fail. Traditional analysis often falls short due to the complexity of deal contexts, buyer honesty, and the time-intensive nature of conducting interviews. Rally partnered with its CMO, Juliette Kopecky, to create a three-phase AI workflow that enhances this process. The first phase involved using AI to analyze recorded sales calls instead of scheduling interviews. By leveraging Gong’s AI capabilities, Rally found that AI could efficiently summarize and extract structured insights from unstructured data. In the second phase, Rally used AI to identify trends across numerous deals, focusing on customer pain points, desired product features, and process steps. By aggregating summaries and building a custom GPT model for analysis, Rally discovered that AI could quickly highlight repeatable patterns and actionable insights, which they could then integrate into their sales strategies. The third phase aimed to predict outcomes and recommend actions based on past data. Although still in testing, this phase seeks to assess deal risks and suggest next steps based on AI analysis of early calls in the sales process. Rally concluded that a hybrid approach combining AI's speed and data analysis with human insights creates the best outcomes. AI enhances the win-loss process by allowing teams to analyze more data frequently and prepare for interviews with better context. However, the depth of human-led conversations remains essential for understanding complex customer journeys. The article encourages other companies to adopt this AI-driven analysis for both won and lost deals to inform their strategies effectively.