AI features fail quietly when they solve a slide-deck problem instead of a user problem. Before choosing a model, name the job: draft faster, retrieve policy answers, classify tickets, summarize long records, or suggest next actions.
A useful first filter is whether the task already has a human workflow with known inputs and an acceptable error cost. If wrong answers create legal, medical, or financial risk, you need stronger guardrails, human review, or a narrower scope. If wrong answers are easy to spot and correct, you can iterate more openly.
Prototype with retrieval and evaluation before polish. Ground responses in documents your organization already trusts. Log prompts, sources, and outcomes so you can compare versions. Without evaluation, you cannot tell whether a newer model is actually better for your users.
Integrate where attention already exists. An assistant buried on a separate page gets ignored. An assistive control beside the form, inbox, or editor where work happens gets used, and generates the feedback you need.
Ship with cost and latency budgets. AI features that feel magical in a demo and sluggish in production lose trust quickly. Set response-time targets and fallbacks so the product remains usable when the model path is slow or unavailable.