Artificial intelligence and machine learning are no longer specialised infrastructure. A product team can add retrieval, classification or generation to an existing application without hiring a research group — but the integration decisions matter more than the model choice.
The first question is where inference runs. Latency-sensitive features belong close to the request path with aggressive caching; batch enrichment belongs in a queue where a slow model costs nothing user-facing.
The second is how you handle failure. Models are probabilistic and remote APIs are unreliable — every AI feature needs a defined fallback that keeps the product usable when the model is wrong or unavailable.
The third is evaluation. Ship a small labelled set alongside the feature and score every prompt or model change against it. Without that, quality changes are invisible until a customer reports them.
