I manage a team that directly uses ML to create software services that our customers pay money to use. This is relatively rare. Most ML applications are internal - your customer is your own company. In this case, you should think of yourself as an in-house ML consultancy for your company. You need to reliably create value for the company, and be able to measure it.

In my opinion, there are three business "tiers" of ML.

1. Process automation. You're turning a defined business process currently done by a human into something automated, with some custom rule logic, of which some of it may be via ML-trained models. The easiest, because the criteria are well-defined and everyone knows what success looks like.

2. Data Mining/Analysis/Insight. Your company sits on some unexploited set of data, and you want to generate useful business insight from it. ML models can help make sense of it. This takes the traditional business intelligence function to the next level. Harder, because you may need to educate the company on what ML offers. They may not even realize what types of new questions ML can answer.

3. Customer-facing automated-decision services. This is the most demanding application from a business perspective, but not necessarily from a technical perspective. The standards for quality, stability, accuracy, should be much, much higher. If it's customer-facing, it can't mess up, or people will stop trusting it. The customer may be internal or external.