That’s a really interesting point — I think AI’s role in sprint planning is less about replacing human judgment and more about enhancing the quality of team discussions.
In most Agile environments, the hardest part of estimation isn’t the math — it’s aligning different perspectives and historical biases.
AI can help here by surfacing context from past sprints, such as how long similar stories took, what dependencies existed, or even patterns in estimation drift.
When used this way, AI acts like a mirror for the team’s own data, giving everyone better grounding before the conversation starts.
The real gains I’ve seen come from:
Reducing noise in discussions (fewer debates about “gut feeling” estimates)
Improving consistency across teams over time
Helping new members understand the team’s velocity model faster
That said, it’s crucial to keep AI’s role assistive, not authoritative.
Teams still need the final say — otherwise, estimation turns into automation, and that’s not Agile anymore.
I’d say the sweet spot is where AI brings insight, but people bring context and judgment.
