Monte Carlo forecasting for Azure DevOps delivery planning
Delivery leaders searching for Azure DevOps Monte Carlo forecasting usually have one urgent question: when are we likely to finish? Agile Analytics uses your historical throughput to generate probability-based delivery ranges directly inside Azure DevOps.
Why buyers search for this
- Stakeholders want delivery dates but single-point estimates are not trusted
- Teams need a forecasting view that reflects real throughput variation
- Forecasting is often trapped in spreadsheets or separate analytics tools
What this page should help them do
- Generate probability-based delivery ranges using your completed work history
- Use P50, P85, and P95 dates to set risk-aware expectations
- Keep forecast reviews close to the backlog and team data they depend on
See Forecasting features
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Open page →Read the Monte Carlo guide
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Open page →Questions buyers usually ask next
Who is Monte Carlo forecasting most useful for?
Engineering managers, PMO leaders, program managers, and delivery leads who need to communicate date confidence instead of a single optimistic forecast.
What makes it better than average velocity planning?
Average velocity hides variability. Monte Carlo forecasting makes uncertainty explicit, which leads to better stakeholder conversations and fewer brittle promises.