Predictive work often stops at a promising validation score. The harder part is placing that score into a process someone will use and maintain.
A model needs a decision it is allowed to influence. Without that, teams collect predictions that nobody acts on, then conclude that machine learning did not help.
It also needs an operating home: an API, a dashboard, or a workflow step, plus a person who reviews performance when the data shifts. Drift is normal. Silence around drift is the problem.
Start with a baseline the business already understands. If a simple rule or last-period forecast is good enough, that is useful information. If the model is better, the improvement should be visible in the same operational terms.