Overfitting is a quantifiable problem. If you're not doing robust data segregation and CV you're not even engaging in elementary ML practices.
To use a real world example, financial models on mortgage backed securities were the root cause of the financial crisis, because they were based on decades of mortgages that were fundamentally different than the ones they were actually trying to model. Even if someone was constructing a model by training on data from say, 1957-1996, and validating using 1997-2006, they would have failed to accurately predict the collapse because the underlying factors that caused the recession (the housing bubble, prevalence of adjustable rate mortgages, lack of verification in applications) were essentially unseen in the decades of data prior to that.
Validation protects against overfitting only to a certain degree, and only to the extent that the underlying data generating phenomena don't ever change, which, in the real world, is generally a terrible assumption.
It's not about outliers. Let's say you're at a startup and you fit some model to your first 30 customers. It works great for your next 10 customers, but fails dramatically for your first enterprise client. Why? Because the enterprise client was fundamentally different from your previous 40 customers. If you fit your model on a population in which the relationship looks one way, then try to apply your model to a population with a different relationship, it will fail.
Machine learning and statistics are both application of the same principles of probability and information theory. They work (for the most part) by modeling the world capturing the relationships between random variables. A random variable can be any natural process that we can't express in precise terms, so we express it in probabilistic terms.
This is the same principle underlying the premise that "past results do not guarantee future success." The relationships between random variables in the world that affect success in anything -- stock market performance, legal outcomes, etc. -- might not be the same tomorrow as they are today.
And that's not even a matter of overfitting. That's just your ever-present real-world threat of having all your modeling work invalidated by forces outside your control. Overfitting happens when you, the data scientist, fit your model to random noise in the training data. An overfitted model will have bad generalization performance on held-out samples, even from the same population. It's not always easy or possible to detect overfitting, especially with small training sets.
Or at least understand that you're entering a new market and budget appropriately for development. Usually, if you're switching from between prosumer -> enterprise, you are very, very lucky if the sum total of changes you need to make is training a new machine learning model. To start out with, you usually need to get used to sales cycles that take 6-18 months, hiring a dedicated sales guy to manage the relationship, and handling custom development requests.
Hopefully you also listen to them.
Even if you KNOW that your model is not-wrong in the right direction and within acceptable orders of magnitude, how do you fit the parameters for that structural model? You need some kind of data, even if you're just using anecdata to pick magic constants.
Fortunately models like these are often testable across many contexts, amenable to metastudies, available for calibration, etc.
When building a model, you divide your data into two parts, the training set and the testing set. The training set is usually larger (~80% of your original data set, although this can vary), and is used to fit your model. Then, you use the remaining data you set aside for the testing set by using your model to generate predictions for that data, and comparing it to the actual values for that data.
You can then compare the accuracy of the model for the training and testing sets to get an idea if your model generalizes well to the real world. If, for example, you find that your model has an accuracy of 95% on the training data, but 60% on your testing data, that means your model is overly tuned into features of the data used to build the model that may not actually be helpful for prediction in the real world.