How to make predictive AI models resilient to market disruptions?

I’m currently exploring predictive AI capabilities for a mid-sized manufacturing client, and we’re seeing challenges around model reliability during unexpected market changes (e.g., COVID-like shocks, raw material volatility, supply chain delays).
Would appreciate thoughts from AI engineers, data scientists, or enterprise IT managers who’ve implemented AI in production.
System context:

Tech stack: Python, TensorFlow, Azure ML

Industry: Manufacturing + Supply Chain

Models used: Time-series forecasting, demand prediction

Use case: Inventory management and logistics planning

Thanks in advance for the insights. Hoping this can serve as a helpful discussion for others in similar spaces.