" Adaptive sampling " is a dynamic strategy where the selection of future samples depends on the results of previous samples. AI excels in this area by continuously analyzing incoming data and automatically focusing the " sample " on areas where risks or anomalies are surfacing (e.g., a fraud detection bot increasing its scrutiny on a specific merchant after one suspicious transaction). The ISACA AAIA™ framework recognizes adaptive sampling as a significant advancement over static methods like systematic (Option C) or statistical (Option B) sampling because it uses real-time intelligence to improve audit coverage and resource allocation.