The first version is not the decision
A capable developer can produce a working AI feature quickly. What follows — evaluation, monitoring, prompt maintenance, model changes, knowledge upkeep — is the actual commitment, and it is what the decision should be made on.
In-house wins on proximity to the workflow
The hardest part is usually understanding the process being automated and the undocumented rules inside it. People who already know the business start ahead, and they are the ones who can extract the rules nobody wrote down.
The scarce knowledge is how your business decides things, not how to call a model.
Outside help wins on the mistakes already made
Evaluation harnesses, guardrails, retrieval design and permission enforcement are patterns rather than inventions. Paying for the second attempt at those is cheaper than making the first one yourself.
The hybrid is usually right
Bring in help to establish the foundations and ship the first feature, with your team involved throughout, then run it internally. The condition is that handover means what it should — code, credentials, corrections and documentation.
Ask the maintenance question out loud
Who runs the evaluation, who updates the knowledge, who is called when a provider changes the model. If those answers do not exist, no amount of build quality helps — the ownership question again.
Whoever builds it, keep the assets
The evaluation set and accumulated corrections are the durable value, not the code. A supplier who keeps those has locked you in far more effectively than any contract clause — the question to ask any AI vendor.