Adding AI features to a product your customers already use

Start from a task your users already do

The strongest first AI feature removes effort from a task your users perform often inside your product today. Look at where they spend time: writing the same kind of note, searching for a record, copying information between screens, reading a long document to find one fact, deciding which item to handle first.

Use your own evidence. Product analytics show the slow screens. Support tickets show the confusing ones. A few hours spent watching users work shows the rest. Avoid starting from a capability and looking for somewhere to put it. That route tends to produce a chat box on the dashboard that few people open twice.

• Done often, by many users
• Slow or tedious today
• Output the user can check quickly
• A mistake is cheap to catch and correct
• The data it needs is already in the product

Keep users inside the product they already use

Put the feature where the task happens. A suggested reply belongs in the reply box. A summary belongs at the top of the record. Extracted fields belong in the form, prefilled and editable. Each extra step a user must take to reach the feature, such as opening another tab or pasting text into a separate tool, reduces use.

Make the output a draft the user controls. They accept, edit or dismiss it, and the product records which. The healthcare AI modules we build for EHR product owners and independent hospital chains follow this rule: they are plug-and-play, customised per client, and added inside the system people already use. Clinical decisions stay with qualified clinicians.

Evaluate before launch

Build a test set before you build the feature. Collect real examples of the task from your product, handled under your privacy rules, and write down what a good output looks like for each. Include the awkward cases: empty records, very long ones, mixed languages, hostile or nonsensical input.

Agree the pass criteria with someone who knows the domain, then run every prompt or model change against the set. Without this, each change is judged by whoever tried three examples that afternoon. Follow with a limited release to internal users or a small group of customers, and read real outputs every day during that period.

• Real examples, including edge cases
• Written criteria for a good output
• Review of a sample by a domain expert
• An automatic rerun on every change
• A staged release before general availability

Know your cost and latency before you commit

Every AI call costs money and takes time, and both grow with use. Before launch, estimate how often the feature will be triggered per user, how much text goes in and comes out each time, and what that means at your current and expected user numbers. A feature that fires on every page load has a very different bill from one the user asks for.

Latency shapes the design. If a response is slow, users need to see progress, or the work should happen in the background and be ready when they arrive. Smaller models, caching of repeated requests and shorter inputs all help. Decide early how the cost fits your pricing and plans, because it is hard to take a free feature away.

• Calls per user per day, estimated
• Input and output size per call
• Cost at current and expected scale
• Response time users will accept for this task
• A decision on which plans include the feature

Design for the model being wrong

The model will be wrong some of the time, so design the wrong case first. Show where the output came from when you can, such as the source passage beside a summary. Keep a human confirmation step before anything is sent to a customer, saved to an important record, or acted on. Let users correct the output in place and report a bad one with a single click.

Plan for the service failing too. If the model is slow or unavailable, the underlying task must still work the way it did before the feature existed. Add limits on input size and request rate, and filter outputs for content your product should never display.

• Visible sources where possible
• Human confirmation before consequential actions
• Editing in place and one-click feedback
• The original workflow intact when AI is unavailable
• Input limits and output filters

Maintain it after launch

An AI feature needs ongoing attention in a way a normal screen does not. Model providers update and retire versions. Your users' data and habits shift. Prompts that worked at launch drift out of step with the product around them.

Assign an owner. Track acceptance, edits, dismissals and reported errors, along with cost and response time. Review a sample of real outputs on a schedule. Add every reported failure to the test set, and rerun the set before any model or prompt change goes live. Pin model versions so that behaviour changes when you choose, and budget engineering time for this work each quarter.

• A named owner
• Usage and quality measures on a dashboard
• Scheduled review of real outputs
• A growing test set
• Pinned model versions and planned upgrades

What to do next

List the five tasks your users repeat most, and score each against the criteria above. Pick one, collect fifty real examples, and write down what good looks like before any build starts. If you want engineers who enhance existing products to work alongside your team, contact us.