Different tasks need different reliability thresholds
A human reviews every output before use.
AI handles routine cases while people review exceptions.
Testing, monitoring, permissions and fallback controls are essential.
Very high average accuracy may still be insufficient if rare failures are catastrophic.
Reliability is not one number
An AI system can be very strong on one benchmark and weak on a different real-world workflow. Reliability depends on the task, the information available, the model, the tools it can use and the controls around it. A useful deployment therefore needs task-specific testing rather than a general claim that the model is “accurate.”
Progress can also expose harder tasks. As models become more capable, organisations ask them to handle longer workflows, more ambiguous decisions and more consequential actions. That means the definition of “good enough” moves with the use case.
Design for mistakes instead of assuming they disappear
Good AI systems use verification, permissions, monitoring and fallback paths. Low-risk tasks may need only human review. Higher-risk workflows can require restricted tools, approval before external actions, source checking and clear escalation when confidence is low. The objective is not to pretend errors will reach zero, but to make failures detectable, containable and recoverable.
Research references
Stanford AI Index 2026 — Technical Performance
METR — Time Horizons