I think every data scientist has a story about a time when someone from product or business with relative power demanded they find a specific pre-determined answer in the data. Data scientists generally push back. How this goes depends on the quality of the org and leadership.
When you want to use a new algorithm that you don't deeply understand, the best approach is to implement it yourself to learn how it works, and then use a library to benefit from robust code.
Here's one article showing this with neural networks in Python: towardsdatascience.com/how-to-build-y…
When you say "hiring junior data scientists is a risk" I hear "we don't have competent management of our data science team".
Sadly, I've heard this a few times this week.
AI is not inscrutable magic -- it's math and data and computer programming, made by regular humans.
People who make AI are not unicorns. They are just people who like math and data and computer programming.
Bad management is so endemic in tech companies that weak managers often don't even know they are weak, and power dynamics often mean they have no motivation to improve.