#AI maintenance#AI monitoring#MLOps#AI operations#AI strategy#Model retraining
Frequently asked questions
- How often should we monitor a deployed AI system?
- Layer it. Daily checks (5-10 minutes) for system health, usage volume, and critical errors. Weekly reviews (30-60 minutes) for usage trends, accuracy, speed, error rates, and low-confidence predictions. Monthly reviews (2-4 hours) for satisfaction, business impact, and data drift. Quarterly reviews (1-2 days) for strategic alignment, ROI validation, and retraining decisions.
- When should we retrain our AI model?
- There are three triggers. Performance degradation - when accuracy drops below your acceptable threshold (e.g., from 90% to 80%), typically every 1-3 months. Business change - new products, processes, or markets the AI hasn't seen. Scheduled refresh - a regular cadence (quarterly or semi-annually) even when performance looks stable, so you don't fall behind.
- Why do most deployed AI systems fail?
- Three reasons, almost always together. Data drift - the data flowing into the AI today no longer matches what it was trained on. Model decay - performance degrades as user expectations and business processes evolve. User abandonment - initial excitement fades, no one owns the AI's success, and users quietly go back to the old way. Deployment isn't the finish line - it's the starting line.
