The Forecast
A monthly roundup of what's happening in agile delivery, estimation, and PM tooling — with our take on each. Newest issue first.
Jira is building native Monte Carlo forecasting
Atlassian's community boards confirm Jira is rolling out probabilistic "Monte Carlo charts" directly into the product — resampling historical throughput to forecast completion likelihoods, the same underlying technique this blog has argued for since the first post here.
Our take: this is a genuine validation of the whole premise, not competitive pressure to worry about. A native Jira feature forecasting internal sprint completion is a different product from a client-facing forecast with a white-labeled portal and an accountability log — see our full comparison — but it's worth watching, and worth revisiting that comparison once the feature actually ships.
The story points debate isn't settling — it's hardening into two camps
Recent industry coverage shows roughly 78% of Scrum teams still use story points, while a growing body of practitioner writing is pushing back harder than usual on cross-team point comparisons specifically, calling them fundamentally misleading.
Our take: the debate keeps missing the actual fix, which isn't "stop pointing" — it's "stop asking points to predict a date." See our post on why cycle time, not points, belongs in a forecast.
AI-driven predictive analytics is the fastest-growing part of the PM software market
Market research is now putting the AI-enabled project management segment at roughly 40% CAGR — several times faster than the PM software market overall (itself growing at a healthy ~15%). Predictive analytics and "outcome-based tracking" are named as the specific drivers.
Our take: a lot of what gets marketed as "AI forecasting" in this wave is pattern-matching dressed up in AI language, not a disclosed, checkable method. The bar we'd hold any of it to: does it show a real distribution with a stated confidence level, or just a single number with more confidence attached than it's earned?
2026 Kanban guidance keeps circling back to the same four metrics
A wave of updated Kanban metrics guides this year all converge on the same four: cycle time, throughput, WIP, and flow efficiency — with WIP limits specifically called out as one of the highest-leverage changes a team can make to its own delivery speed.
Our take: good, boring, correct advice — and exactly the four charts we think are worth actually reading together, not glancing at individually.
That's issue 1. Have something we should cover next time? Reply or send it to theo@ptahcast.com.