Military AI Models Drift in Performance but Deployed Systems Lack Monitoring

Machine learning models degrade over time as the real world diverges from training data — a phenomenon called model drift. In commercial settings, companies monitor model performance continuously and retrain when accuracy drops. In military deployments, AI systems are often fielded to austere environments — forward operating bases, ships at sea, deployed aircraft — where there is no connection to a monitoring dashboard, no MLOps pipeline, and no easy way to push model updates. This means a computer vision model that was 95% accurate in testing could silently degrade to 80% accuracy in the field as the adversary changes tactics, seasons change the visual environment, or sensor degradation alters image quality. The operators on the ground have no way to know the model's confidence has dropped. They continue to trust its outputs because it was certified before deployment, not realizing that certification is a point-in-time snapshot, not a guarantee of ongoing performance. The structural reason is that military acquisition treats AI software like traditional hardware: you test it, certify it, field it, and forget it until the next upgrade cycle (typically 2-5 years). The commercial MLOps practices of continuous monitoring, A/B testing, and automated retraining have no equivalent in the military procurement process. The DoD's Chief Digital and AI Office (CDAO) has published guidance on MLOps, but the field units that actually deploy these systems lack the bandwidth, expertise, and infrastructure to implement it. There is no 'AI maintenance crew' equivalent to the mechanics who maintain physical equipment.