How to integrate AI into an existing application

Adding AI to an existing product works best when you treat it like any other feature: a specific job, measured, shipped in small steps. Here is the path we use.

1. Pick a job, not a technology

List the tasks your users or team repeat most: answering the same questions, reading documents, filling forms, writing first drafts, searching for information. Rank them by time spent and by how costly a mistake would be. Start where time saved is high and risk is low.

2. Check the data

AI is only as good as what it can see. Where does the knowledge live — help articles, tickets, PDFs, a database? Is it current? Who is allowed to see what? Permissions must carry into the AI feature, or it will leak information between users.

3. Build an evaluation set first

Collect 50–200 real examples with the answer you'd expect. This becomes your test suite: every prompt change, model change or retrieval tweak is scored against it. Without it you are tuning by vibes.

4. Choose the simplest architecture that passes

• Prompting for drafting and rewriting.
• Retrieval (RAG) when answers must come from your own content, with citations.
• Tool use / agents when the AI must look things up or take actions in other systems.

5. Add guardrails and a human where it matters

Anything that sends money, emails customers or changes records should be proposed by the AI and approved by a person — at least at first. Log every action.

6. Ship to a small group, then measure

Roll out to one team or a slice of users. Track accuracy, time saved, cost per request and how often people override the AI. Expand when the numbers hold.

7. Keep watching after launch

Models, data and user behaviour change. Re-run the evaluation set regularly and alert on drops.

Want help choosing the first use case? See AI development or book a free assessment.