Summary: The episode explores using AI to assist with effort estimation in agile sprints. AI can turn each user story into observable signals (relative size, technical complexity, uncertainty, dependencies, risks) to predict effort in hours, days, or story points, aiming to reduce bias, speed up planning, and enable data-driven improvement. It stresses starting from existing data (delivered stories, past estimates, task durations, defects/rework) and keeping data clean and consistent. Three practical AI approaches are discussed: AI as a planning assistant, a learning model with human calibration, and a hybrid method that decomposes stories into tasks for consolidated estimates. A five-step action plan is provided: prepare the backlog and units, collect/clean data, define features, run a short pilot, and measure/calibrate. The text emphasizes data governance, privacy, and a culture of continuous improvement, noting that breaking stories into tasks helps AI capture micro-efforts and reveal hidden dependencies. Realistic expectations are that AI reduces variability and fosters informed discussion rather than delivering perfect predictions. Practical examples include decomposing a product page story into subtasks and using lightweight AI-guided planning during sprint meetings. The closing encourages experimentation, thoughtful review in retrospectives, and avoiding reliance on a single estimate. Key takeaways: - AI-assisted estimation uses signals like size, complexity, uncertainty, dependencies, and risk to predict effort. - Start with clean, historical data and track metrics such as average error, bias, and cross-team variability. - Approaches include AI as an assistant, a learn-from-history model with human calibration, or a hybrid that decomposes stories into tasks. - A practical 5-step plan: prepare backlog, clean data, define features, run a 3–5 story pilot, measure and adjust. - Success relies on data governance, privacy, human judgment, and a culture of continuous improvement; breaking down work helps AI capture micro-efforts and hidden dependencies. - Realistic use includes reduced planning variability and better sprint commitment; monitor for over-optimism and adjust accordingly. - AI-guided planning during sprint meetings can provide context-rich estimates that teams validate. Remeber you can contact me at [email protected]