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In your opinion automated user stories estimation, used through eg. Machine Learning (ML) algorithms, would have a positive impact on the planning and estimation process?
A) No. Estimation can't be automated, as the teams should get acquainted with all the details in a given story.
B) No. The use of ML/AI algorithms for estimation is unreliable
C) Yes. The use of ML/AI algorithms can speed up/support the estimation process, especially when there is a large discrepancy in views of varied team members or a large number of stories to be estimated.
D) Yes. A good algorithm can replace team estimation. Thus, more time can be dedicated for dealing scope of the stories and detail planning
Make a comment and give me feedback what do you think about that.
What the value of estimations activities (sprint planning, backlog refinement, etc.) ?
IMHO, the ouput is more about the discussion with all the dev team than the numbers.
There are some techniques to make the discussion more efficient. But do we really want to replace team interactions with an AI?
Hi @Sedera Randria, thank you for your comment.
First of all, I am thinking about improving the process of estimating stories, which in large Nexus projects (where 10 teams work together) can take 2-3 hours after the teams are divided into groups (each group of teams estimates different stories).
This type of exercise is carried out during the backlog refinement, for stories already defined in PB or in planning, if in the meantime the client has submitted new user stories that he would like to be implemented first.
In summary, it is not about reducing the interaction between teams, but about reducing the time needed to estimate stories.
It seems that in the estimation process, both the development team and the ML / AI algorithm will do exactly the same thing, i.e. make a decision about the size of the story based on experience.