The measurement problem at the heart of enterprise AI
Most failed AI deployments in large institutions are diagnosed as engineering problems; the model drifted, the pipeline broke, the data was dirty. From experience, I'd argue they are more often measurement problems wearing an engineering costume.
When an anomaly-detection model starts flagging the wrong incidents, there is a natural instinct to retrain it. But the deeper question is the one econometricians ask first: are we measuring the thing we think we're measuring? A data-quality score that rises while customer complaints also rise may seem as a quality score; masking a proxy issue that has quietly decoupled from the outcome it was meant to track. No amount of retraining fixes a construct-validity problem.
This is the Intersection I work In. A decade in econometrics and over 12+ in the industry taught me to be suspicious of any number before trusting it; to ask what it identifies, what confounds it, and what it silently omits. A decade building production data systems taught me that institutions reward the number that ships, as opposed to the number that's right. Overtime. regulated finance has been forcing those two instincts together, because when a model informs a capital decision or a supervisory return, quote like oh, "it passed the unit tests" is not an acceptable account of why you believe it.
The practical implication for anyone building AI in a regulated institution: invest as much in the measurement layer as in the model layer. Define what each metric identifies before you optimise it. Treat every automated signal as an estimator with bias and variance, not as ground truth. And build the human-in-the-loop checkpoint as the place where construct validity gets defended not merely as a compliance requirement
In sum, I deem this as a path to adopt unforgivably for any seriously data/AI-driven entity because the institutions that will dominate the next decade of applied be the ones who know, with rigour, what their numbers actually mean.