Fair - I'm not personally familiar with state of the art in UI QA automation, but I know theres been various screen recording type tools available for a decade+ with mixed success.

I come more from a "big data" background, and have dealt with CTOs who think "can't we just use AI?" is the answer to data quality checking multi-PB data lakes with 1000s of unique datasets from 100s of vendors. That is - they don't want to staff a data quality team, they think you can just magic it all away.

The answer was always - sure, but you are fixated on the easy part - anomaly detection. Actual data analysis on what broke, when, how, why, and escalating to data provider was always 95% of the work. Someone needs to look at the exhaust, and there will be exhaust every single day.. so you can kill your dev teams productivity or actually staff an operations team responsible for the tickets the thing spits out.