Use AI to surface project risks, build risk registers, and write mitigation plans that actually hold up in review.
Risk management is where AI genuinely earns its keep in project management. It has seen thousands of projects fail. Ask it what could go wrong on yours and it will surface things your team missed — because it doesn't have the same blind spots.
You are a senior project risk manager. Analyze this project and identify risks.
Project summary:
[paste project scope and plan]
Identify risks in these categories:
1. Technical risks (dependencies, integrations, performance)
2. Resource risks (availability, expertise, turnover)
3. Schedule risks (critical path, external dependencies)
4. Stakeholder risks (alignment, scope creep, decision delays)
5. External risks (vendor, regulatory, market)
For each risk:
- Risk: [description]
- Trigger: [what event would cause this]
- Probability: High/Medium/Low
- Impact: High/Medium/Low
- Risk Score: P x I (HH/HM/HL/MH/MM/ML/LH/LM/LL)
- Owner: [who should watch this]Based on the risks identified, build a risk register prioritized by risk score.
For each risk in the register include:
| ID | Risk | Category | Probability | Impact | Score | Mitigation | Contingency | Owner | Status |
Mitigation = what we do to reduce probability or impact
Contingency = what we do if the risk actually happens
Prioritize: HH and HM risks first. Only include LL risks in an appendix.Write specific, actionable mitigation plans for these high-priority risks:
[paste HH and HM risks from register]
For each risk, provide:
- 3 specific mitigation actions with owners and deadlines
- The key indicator that tells us the risk is materializing (early warning sign)
- The escalation path if mitigation fails