Backlog refinement
While managing three data platform products, the engineering team faced significant challenges and was still mastering the AWS architecture.
In evaluating the team’s maturity, they encountered:
1. Quick Command with user story templates
Templates guaranteed a consistent format for user stories, making them easier for the development team to understand and execute. PMs could select the methodology that best fit their context when creating a template.
2. Quick Command for clear acceptance criteria
Clear acceptance criteria guided development and ensured that products met functional, usability, performance, and security standards.
This workflow is supported by the AI Agent PM_BacklogBooster—a powerful tool that helps teams organize, prioritize, and detail their user stories, keeping backlogs up-to-date and aligned with project goals.
The hypothesis was validated within two squads, where the team already worked as SMEs/Delivery Managers in Professional Services.
Armed with these results, the team launched a Propositional Consulting initiative, focusing on squads with high Cycle Times (especially in refinement). Following that, new improvement journeys were started using the same framework.
StackSpot AI allowed backlogs to be systematically centralized, boosting visibility and transparency. Product Managers also shifted to a more tactical and strategic role.
Technical impacts
reduction in the average time needed to refine each user story
increase in the average number of stories delivered per month
improvement in the quality of story writing
Adopting StackSpot AI enhanced user story quality based on the following aspects:
• Acceptance criteria: defined precisely what was required for story completion.
• BCP count (for Functional Stories): measured the complexity and effort of the story.
• Description: provided a concise, clear summary.
• Estimated duration: calculated an estimate of completion time.
• Score: provided an overall evaluation considering all the above aspects.
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