AI projects break several assumptions traditional risk management is built on: requirements shift, model performance is probabilistic, and data quality can invalidate months of work. Modern risk management adapts without abandoning discipline.
What's Different About AI Projects
- Model performance varies with data drift
- Ethical and regulatory risks can appear late
- Third-party model changes affect your delivery
Risk Categories to Track
- Data quality and availability
- Model performance and drift
- Regulatory, ethical, and reputational
- Vendor and integration
- Operational readiness
Cadence & Ownership
Review the top risks weekly with a named owner per risk. Escalate anything unresolved for more than two review cycles.
Practical Mitigations
- Establish a monitoring baseline before go-live
- Build a rollback plan for every model update
- Document data provenance and model versions
Watch and Learn
Curated video walkthroughs to reinforce what you've just read.
Master Your Proctored Exam Setup
Preparation habits for PM certifications covering risk.
Frequently Asked Questions
Do we need a separate AI risk register?+
Not necessarily — but AI-specific categories should be explicit inside the project's main register.
How often should we retrain models?+
Based on monitored drift, not a fixed calendar. Set thresholds, don't guess.
Who owns AI ethics risk?+
Ownership sits with the project sponsor, informed by legal, data, and product leads.
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