A system can perform well under earlier conditions yet struggle after the environment changes. Useful adaptation requires recognizing which assumptions remain valid and which need to be revised using current evidence.
Drift should be distinguished from random variation and other failures. Monitoring and evaluation need representative observations so that updates respond to meaningful changes without discarding useful knowledge unnecessarily.
ELI5
Imagine learning a set of rules for a place and later discovering that the way things work there has changed.
For example, a system that learned which signals predict customer demand may need updating when buying behavior changes.
Does every performance drop prove concept drift?
No. Data errors, system changes and ordinary variation can also reduce performance and need investigation.
Why preserve some earlier knowledge?
Not every pattern changes, so adaptation should update stale assumptions while retaining information that still applies.
