Backlog refinement
While facing significant challenges in managing three data platform products, the entire engineering team was still navigating the learning curve of AWS architecture.
In assessing the team’s maturity, they encountered:
• Initial detailing of features carried out by Product Managers: They start by clearly defining the needs, target audience, and expected outcomes with the help of StackSpot.
• Refine and distribute tasks: The platform facilitates task distribution. Each data engineer, supported by StackSpot, is in charge of refining the features into detailed user persona stories, ensuring that all technical and business aspects are taken into account.
1. Quick Command with user story templates
These templates ensure user stories follow a consistent format, making it easier for the development team to understand and implement them. To create a template, PMs can select the methodology that best suits their context.
2. Quick Command to define clear acceptance criteria
Acceptance criteria not only guide development but also ensure the product meets functional requirements, usability, performance, and security standards.
This process is currently supported by IA Agent PM_BacklogBooster. This powerful tool helps development teams organize, prioritize, and detail their tasks and user stories, making sure that the backlog remains up-to-date and aligned with the project’s objectives.
The hypothesis was validated within two squads, where the team already worked as SMEs/Delivery Managers in Professional Services.
With the results in hand, the team started a Propositive Consulting process, analyzing teams with high Cycle Times (mainly in terms of refinement time). Subsequently, other improvement journeys were launched using the same framework.
Leveraging StackSpot AI, the backlog was systemically centralized, enhancing visibility and transparency. Additionally, Product Managers adopted a more tactical and strategic approach.
Technical impacts
88%
reduction in the average time required to refine each user story
increase in the team’s average number of stories delivered per month
21%
improvement in the quality of story writing
To assess the quality of a user story, the following criteria were considered:
• Acceptance criteria: clearly define what is required for the story to be complete.
• BCP count (for Functional Stories): measures the complexity and effort needed to implement the story.
• Description: provides a clear and concise summary of the story’s content.
• Estimated duration: an estimate of how long the team believes it will take to complete the story.
• Score: an overall assessment of the story, considering all the aforementioned criteria.
Impact on the business
The time required for these tasks has been reduced, allowing professionals to focus on development activities that add greater value to the business. .
DEVELOPER PLATFORM
AI MULTI-AGENTS