AI is valuable when it reduces repetitive tasks, improves consistency, or finds rare events in large datasets. The key is to focus on workflows where the cost of human labeling or inspection is unsustainable at scale.

Where ML pays off

• Source classification and deblending in crowded fields.
• Anomaly detection for transient or rare phenomena.
• Cross-matching multi-wavelength catalogs with uncertainty-aware models.

Where caution is needed

Models can learn survey-specific biases, fail under distribution shifts, or produce uncalibrated probabilities. The fastest wins come from well-curated training data and rigorous validation on held-out fields.

A practical checklist

Define a clear failure mode, track calibration metrics, and keep a human-in-the-loop stage for high-impact results. That hybrid approach scales and stays scientifically defensible.