What “practical AI” means in a real product

Most requests we get for AI features start with the technology rather than the task: we should add AI somewhere. The version that works starts from the opposite end — a specific piece of repetitive work someone on the team does every day, and a question about whether software can take part of it.

Good candidates look the same

The tasks worth automating tend to be high volume, low stakes, and easy to check. Answering the same twenty support questions. Turning a messy free-text form into structured fields. Summarising a long thread so a human can decide faster. Routing incoming requests to the right queue. In each case a wrong answer is visible and cheap to correct.

The poor candidates are the mirror image: low volume, high stakes, hard to verify. Final pricing decisions, medical or legal conclusions, anything you would not want to explain to a customer as it came out of a machine.

Scope it as narrowly as you can stand

A support chatbot that answers questions from your own documentation, and says it does not know for anything else, is genuinely useful. A general assistant on your site that will answer any question is a liability. We define the sources the feature may use, the questions it should refuse, and what happens when it has no answer — usually a clean handover to email or a real person.

Keep a human in the loop where it matters

Practical AI features usually draft rather than decide. The system proposes the reply, the classification or the summary; a person approves it, at least until the accuracy is measured over real cases. That review step is not a temporary crutch. For anything customer-facing, it is often the design.

Measure something before and after

Before building, note the current number: how many messages a week, how long intake takes, how many requests get misrouted. Without that, nobody can tell whether the feature helped, and the discussion turns into taste. With it, you can decide honestly whether to expand the scope or remove the feature.

Budget for the unglamorous parts

Model calls cost money, fail sometimes, and get slower under load. A real implementation needs error handling, rate limits, logging of what was sent and returned, and a fallback path when the provider is down. That plumbing is most of the work, and it is what separates a feature you can rely on from a demo.

Used this way, AI is not the story of the product. It is one component that quietly removes work — which is exactly what a good component does.