From metric to model: a five-stage pipeline for building and sustaining valid AI predictive systems in consumer neuroscience

The application of machine learning to consumer neuroscience has made it possible, in principle, to predict consumer responses from media assets alone. However, the pursuit of model sophistication has outpaced attention to a foundational constraint: the quality of any predictive artificial intelligence (AI) system is bounded by the quality of the neurophysiological and biometric data used to train it, and by the rigor of the modeling procedures applied to those data. This article argues that persistent problems of low metric reliability, poor cross-metric consistency, and the broader replication crisis have left the field building predictive systems on an insecure measurement foundation. Anchored in the framework for neurometric validation established by Ramsøy (2019), the paper offers a conceptual synthesis and agenda-setting framework for the AI era: a five-stage pipeline comprising (1) Metric Mapping, the theory-driven identification and pre-screening of candidate neurophysiological and biometric measures; (2) Psychometric Validation, the formal assessment of each metric’s sensitivity, specificity, validity, and reliability; (3) Discriminatory Ability, an assessment of whether a metric produces meaningful score distributions across real-world commercial stimuli; (4) Principled Data Scaling and Model Development, covering both the large-scale data collection and the modeling and validation procedures required for robust prediction; and (5) Model Governance and Drift Prevention, the ongoing monitoring, retraining, and calibration required to maintain predictive validity over time. The individual components draw on established practice in psychometrics, measurement science, and applied machine learning; the contribution lies in integrating them into a coherent, domain-specific architecture for consumer neuroscience, and in making explicit a criterion—Discriminatory Ability—that is widely assumed but rarely tested in applied advertising research. The pipeline is offered not as a validated standard but as a structured research agenda and a basis for future work on standardization, certification, and governance, with the aim of shifting the field from a model-centric toward a validation-centric culture.