Operational Risk
What is Operational Risk?
Operational Risk is the range that an AI system will fail because the processes, people, and/or infrastructure around it break down. Not because the model itself is flawed.
This includes failures in:
- Workflows
- Real-Time monitoring
- Human oversight
- Infrastructure
- Skills and staffing
- Vendor dependencies
- Day-to-day operations
GRC Report notes that operational risks "emerge across the entire AI lifecycle" and require continuous oversight to maintain reliability.
OCD Tech defines operational risk simply as "risks from system failures" - like when the power goes out and everything stops.
What does Operational Risk Do?
Operational Risk shows up when:
- AI systems fail in production
- No one is monitoring model drift or performance
- Teams lack the necessary skills to maintain the system
- Ownership is unclear and issues fall through the cracks
- Infrastructure outages disrupt AI workflows
- Third-party vendors introduce vulnerabilities
- Processes break down across the AI lifecycle
GRC Report highlights that skill shortages, governance gaps, and fragmented accountability amplify operational failures.
AEANET emphasizes that model drift ( a major operational risk) silently degrades performance over time.
Why it Matters for Executives?
Operational Risk can lead to:
- AI systems failing at critical moments
- Incorrect business decisions
- Compliance violations
- Customer impact and service disruptions
- Increased costs and downtime
- Inability to scale AI reliably
- Loss of trust in AI across the organization
This is one of the most common, expensive, and preventable AI risks.
Transparency Disclosure
All images and videos on this site were AI generated and/or are Getty licensed images that may have been AI generated. AI was also used to edit the content descriptions.
