Agentic AI for Story Point & Effort Estimation
See details at https://iALM.ai
Story point estimation was intended to be a lightweight, collaborative practice. Yet for most teams, it has become one of the most inconsistent and time-consuming parts of agile planning. Debates drag on, numbers vary wildly between team members, and techniques like Fibonacci sequences, T shirt sizes, and power of 2 games often introduce more subjectivity than clarity. The result estimates that feel more like guesses than meaningful indicators of effort or complexity.
This webinar presents a modern alternative: AI assisted story point estimation that is precise, transparent, and grounded in a structured analytical process. Using the AI Assistant embedded in the iALM platform, the estimation is no longer based on intuition or memory. Instead, the AI Assistant breaks the user story down into concrete tasks and sub tasks, evaluates the complexity of each, and calculates a story point value for each sub-task. So, the calculated user story value is derived from the actual work required. This produces a level of accuracy and consistency that manual estimation simply cannot match.
The team still retains full control to review and adjust the estimate, but the AI generated value is already far more reliable than traditional methods because it is rooted in a detailed, scenario driven decomposition of the story itself-not subjective impressions.
Just as importantly, the AI driven workflow exposes the underlying factors that influence the estimate. Teams gain visibility into hidden work, edge cases, and complexity drivers that often go unnoticed during manual estimation. Instead of debating numbers, the conversation shifts to understanding scope, clarifying assumptions, and aligning on what "done" truly means.
By the end of this session, you'll see how AI assisted estimation can dramatically improve planning accuracy, reduce friction in refinement sessions, and provide a transparent, repeatable alternative to the guessing games that have long plagued agile teams.