The Eternal Dilemma: Running Without Tripping
If you work in agility, you know the challenge of walking that tightrope between delivering quickly and delivering well. And no matter how many frameworks with elegant names we adopt — Scrum, Kanban, SAFe — the same ghosts keep reappearing sprint after sprint:
- Estimates slipping through our fingers.
- Documentation becoming obsolete by day three.
- Endless hours spent on repetitive, monotonous tasks.
- Decisions made more on gut feeling than on data.
Sound familiar?
Now imagine you had someone (or something…) that analyses your history, detects patterns, warns you before things go off track — and never gets tired.
Correct: that “someone” is artificial intelligence. And no, we’re not talking about futuristic robots. We mean tools that already exist and can make a real difference today.
The Estimation Struggle… and How AI Can Save the Day
Let’s be honest: estimation is one of the toughest challenges in agile.
We play Planning Poker, debate whether it’s a 3 or a 5, glance around, hesitate… and often get it wrong anyway.
AI isn’t here to rob us of those debates (which can be fun — and useful). But it can provide a more solid foundation. Think of it like an ex with an excellent memory: “Careful, last time this was trickier than you think.” That kind of insight is priceless.
What Can AI Do for Us?
- Review your full history: past tasks, teams, timelines, work types — everything.
- Suggest data-driven estimates: based on facts, not intuition or “gut feel”.
- Spot hidden patterns: tasks that are always underestimated, recurring stakeholder issues, deviations caused by lack of context.
- Keep learning sprint after sprint: it doesn’t get bored, take holidays, or switch teams.
The result? Fewer frustrations, fewer surprises, and far more meaningful refinement conversations.
A Real Case: A Backend Team and an AI with Sharp Eyes
The Scenario:
A fintech backend team. Seven members. Jira. Two-week sprints.
The Problem:
Tasks estimated at 5 points often turned out to be more like 8. The result? Pressure, mismatched expectations, and the feeling things were out of control.
The Approach:
They switched on AI estimation in Jira — but only after doing their homework:
- Improved story definitions.
- Tagged tasks correctly.
- Took retrospectives seriously.
What Did AI Deliver?
- Analysed six months of work.
- Flagged database migrations as consistently underestimated.
- Identified that tasks picked up by new joiners deviated by almost 40%.
- Triggered alerts such as:
“Warning: this looks like a 3-pointer, but similar tasks have taken 5.”
The Impact (after just 2 sprints):
- Estimation deviations dropped by 30%.
- Workload was better balanced.
- The team focused on what mattered, not just what was urgent.
- Most importantly, they were able to breathe again.
Want to See It in Action?
If all this sounds great but you’re a “see to believe” person, check out these examples on YouTube:
- Automated Story Point Estimation with Jira Rovo AI: in just two minutes, an AI-generated presenter explains how automated task estimation works.
- Creating Stories with Atlassian Intelligence: a seven-minute walkthrough (by a human!) showing how to split epics, structure tasks, and accelerate backlog creation with AI.
AI Doesn’t Replace — It Accompanies
No one wants artificial intelligence making decisions for us. But we do want it to help us make better ones. To cut the noise. To raise the flag before it’s too late. To show us what we don’t yet see.
Because agility isn’t just about speed. It’s about clarity. Focus. And improving every day with a cool head — and now, with a little help from AI.
And you? Are you already using AI in your agile team?
Do you have a tool that’s helping you? Curious to try?
