Detect project risks with AI

AI risk detection reads a project’s current state, its schedule, task statuses, dependencies, and workload, and proposes concrete risks: things like a critical-path task with no assignee, a milestone with everything bunched in the final week, or one person carrying half the plan.

Run an analysis

1. Open the project and go to its Risks view.
2. Run the AI analysis. Each proposed risk comes with a severity, a category, and a short explanation of why it was flagged.
3. Accept the risks worth tracking; they become normal project risks you can manage, assign, and resolve. Dismiss the ones that do not apply; dismissals stay on record.

Tip

Risks you accept live alongside manually-created risks and feed the project’s health indicators. A risk that materializes into a real problem can be promoted to the issue log from the Risks view.

Risks your agents record by hand

Not every risk comes from an analysis. When a connected agent spots a risk during its work, it records it as a real risk-register entry (description, severity, category, and an optional mitigation) instead of burying it in a comment. Hand-recorded risks work on every plan.

The two paths behave differently, and the difference matters:

• Recording a risk is additive. It never touches the risks already on the register.
• Running an AI analysis refreshes the detected set. It replaces the open, non-dismissed risks with the new findings, so dismiss or resolve what you’ve handled before re-running.

FAQs

Does the analysis change my project? No. The analysis only proposes; nothing becomes a tracked risk until you accept it.

How is this different from project health (RAG) status? Health status is a quick computed indicator. Risk detection produces specific, explainable items you can act on, each with its own severity and owner.

Which plan includes risk detection? Risk detection is part of the advanced AI features, available on the Business plan and above.

Best practices

1. Run analysis after major changes. New tasks, schedule shifts, member adds. Fresh analysis catches new risks before they materialize.
2. Accept selectively; dismiss irrelevant. Don’t accept every suggested risk. The dismiss feedback improves future suggestions.
3. Promote materialized risks to issues. The risk→issue handoff is a first-class pattern. See Track issues in the issue log.
4. Re-run weekly during execution. Risk landscape changes weekly. Weekly cadence keeps the radar fresh.
5. Review with the team. Don’t act on AI risks unilaterally. The team knows context the AI doesn’t.

Troubleshooting / common pitfalls

• Analysis returned no risks. Either project is healthy or analysis lacks context. Verify project has tasks, dates, and assignees.
• Same risk suggested again after dismiss. Dismiss is per-suggestion; recurring patterns may surface a semantically similar risk.
• Risk severity feels off. AI’s severity is an estimate. Edit after accepting if needed.
• Analysis timed out. Large projects (1000+ tasks) may exceed time limit. Filter to subset.
• Risk category unexpected. AI categorizes by detected pattern. Edit category after accepting.

How this combines with other features

• Risks + Issues. Promote risks to issues when they materialize. See Track issues.
• Risks + Project health. Risks feed RAG health computation.
• Risks + EVM. Critical risks affect SPI/CPI predictions. See Read the EVM dashboard.
• Risks + Workflows. Workflow can notify on new risk acceptance.

Coming from another tool

Tool	Mapping
Microsoft Project Online risk register	Direct concept
ServiceNow PPM risk module	Direct
Custom risk spreadsheet	Onplana automates the detection
Custom Excel risk matrix	Direct
Jira risk plugin	Direct

Related

• Track issues, promote risks to issues
• Read the EVM dashboard, risk impact on cost/schedule
• Chat with Onplana AI, ad-hoc risk questions