Catchy title. Empirical models, while useful, can't explain the how or why of a system. Having machine-learning (err, AI) models is great, but there's no idea of what will happen with edge cases or novel situations. Such models can provide data which augments experimental data.
Theories, ideally, can be used to propose new experiments. Data-only models can only interpolate.