The strongest AI features are designed around a job users already need done. Adding a model first and searching for a use case later often produces unpredictable costs and unclear value.
Define an outcome before selecting a model
Examples include summarizing a large document, classifying incoming requests, or helping a person draft a response. Define the acceptable output and what a user should do when it is wrong.
Treat reliability as part of the interface
Latency, provider errors, missing context, and inconsistent outputs are product states, not merely backend concerns. Plan visible progress, retry rules, and meaningful fallback behavior.
Measure the total operational cost
Include API usage, retries, monitoring, evaluation, and human review. A cheap individual request can still become expensive when the workflow frequently repeats or fails.
Keep boundaries around sensitive information
Decide which data may be sent to an external provider, how long it is retained, who can access results, and which outputs require human verification before use.