Adding AI to an existing product: a low-risk approach

Choose the right first use case

The best first AI feature solves a task your users already do repeatedly, where the input is text or documents and the output is easy to check. Good candidates appear in most products once you look for them.

• Drafting replies to customer messages or support tickets for staff to review.
• Summarising long records, call notes, reports or conversations.
• Extracting fields from invoices, forms, CVs or other documents into structured data.
• Classifying or routing incoming requests, leads or tickets.
• Searching your own documentation or knowledge base in natural language.
• Generating first drafts of descriptions, emails or reports from existing data.

Avoid the high-risk starting points

Some uses are tempting but risky as a first step: fully autonomous agents that take actions without review, features that make decisions about money, health or legal matters, and public chatbots that can say anything on your behalf. These can all be built responsibly, but they need stronger guardrails, more testing and clearer evidence that the simpler version works first.

A useful rule: the more expensive or embarrassing a mistake would be, the more human review and testing the feature needs before it runs on its own.

Also be wary of adding AI simply because competitors have. A feature that does not save users time or improve an outcome they care about will be ignored, however clever it is. Start from a real, recurring pain point in your product, and the AI becomes a tool for solving it rather than a feature looking for a purpose.

Design it as assistance, not automation

For the first version, let the AI suggest and let a person decide. A support agent sees a drafted reply and edits it before sending. An operations user sees extracted invoice fields highlighted for confirmation. This approach catches errors, builds trust with your team and generates valuable feedback about where the model performs well and where it struggles.

Track how often suggestions are accepted unchanged, edited or rejected. That gives you a practical quality measure and tells you when a feature is reliable enough to automate further.

Test on your own data before launch

Demos with made-up examples always look impressive. Real data is messier: unusual formats, mixed languages, missing fields, ambiguous requests. Collect a set of real, representative examples from your product, with the correct answers, and test against them. Include the awkward cases deliberately.

Decide in advance what good enough means. For extraction, it might be the share of fields that are correct. For drafted replies, it might be how often a reviewer would send the draft with only light edits. Agreeing on this before testing stops the decision about launch from becoming a matter of opinion.

This test set becomes a long-term asset. Whenever you change the prompt, the model or the provider, rerun it to confirm quality has not dropped. It turns AI development from guesswork into something you can measure.

Plan cost, privacy and reliability

A few practical decisions separate a dependable AI feature from a fragile experiment.

• Estimate cost per request and per user from your test runs, and set usage limits to prevent surprises.
• Send only the data the model needs, check the provider's data retention and training terms, and inform users where required.
• Handle failures gracefully: timeouts, errors and low-confidence answers should fall back to the normal workflow.
• Log inputs, outputs and user corrections, with appropriate privacy controls, so issues can be investigated.
• Keep the AI logic behind your own API so you can switch models or providers without rewriting the product.

Roll out gradually

Release the feature to internal users or a small group of customers first, behind a feature flag. Watch acceptance rates, cost and feedback. Fix the common failure cases, update your test set and then widen access. Once the first feature is stable and trusted, the same foundation of API layer, test set, logging and cost controls makes the second feature much faster and safer to add.