“We should add AI” can lead to a useful product. It can also lead to another button customers try once and forget.
Before choosing a model or designing a chat window, name the job you want to improve. A customer might need to turn a long document into a draft, find a relevant answer, or organize a messy set of information. Each is a different product problem.
The strongest starting point is a task with a recognizable finish line. You should be able to show the result to a customer and ask whether it helped.
1. Choose the task before the interface
Write down what customers currently do, what makes it frustrating, and what a useful result would look like. Be specific about where the result goes next.
For example, a document tool could suggest a category, extract a few fields for review, or draft a summary. Those features need different checks. A chatbot that attempts all three may be harder to explain and evaluate than a focused button inside the existing workflow.
Use this sentence: “The feature helps ___ turn ___ into ___, so they can ___.” If you cannot complete it in plain English, narrow the idea.
Also ask whether a simpler rule, search filter, or form improvement would do the job. The aim is to improve the experience customers pay for.
2. Agree on what a good answer looks like
A response can sound confident and still be wrong. Define the checks that matter for this particular task before you judge a demonstration.
For a summary, you might care about missing decisions and invented facts. For data extraction, you might care about whether a field matches the original document and whether uncertain values are flagged. For search, you might care about whether the answer points to a relevant source.
Start a small, representative test collection with expected outcomes. Include incomplete inputs, awkward formatting, and cases where the correct behavior is to ask for help. OpenAI recommends task-specific evaluations and repeat testing as an application changes. Read its evaluation guidance.
These examples become a shared definition of readiness. A team can discuss specific failures instead of arguing about whether the demo feels impressive.
3. Decide what happens when the system is uncertain
Draw the whole workflow, including the uncomfortable parts. What happens when a file is unreadable, a request takes too long, or the answer does not meet your checks?
A sensible first release might let a person review a suggested result before saving it. Another might show supporting material and offer a direct route to a human. An unavailable AI service should have a clear user-facing state, rather than an endless spinner.
Be especially careful when an AI feature can change data or act outside the app. Decide which actions require review, which can be reversed, and who is accountable if something goes wrong.
Write those choices into the scope. They are part of the customer experience and the engineering work.
4. Estimate the cost of a completed task
The price of one model request is only one ingredient. Your feature may need several requests, document processing, storage, or retries before a customer gets a useful result.
Ask for a simple usage model: expected active customers, tasks per customer, typical input size, and the steps involved in each task. Include a heavy-use scenario as well as a typical one.
OpenAI’s production guidance recommends projecting traffic, interaction frequency, and data volume when estimating operating cost. It also describes reducing input or output usage and testing suitable smaller models. See the production guidance.
Keep the commercial question visible: does the feature create enough value to justify its delivery and support costs? A cheap answer that the customer has to redo may be an expensive experience.
5. Keep the first release easy to understand
Choose one entry point, one clear result, and a visible next action. Tell customers what information the feature needs and what they should check before relying on its output.
Avoid promising that the feature is always accurate or fully autonomous. Describe what it actually does. If review is required, make that review practical: show the original information beside the suggestion, highlight missing fields, or let the user reject the result without losing their work.
For data handling, document what leaves the app, which providers receive it, how access is controlled, and how long it is retained. Check the relevant service settings and agreements before making privacy claims. “Powered by AI” is not a data-handling policy.
6. Learn from real use after launch
Measure whether customers complete the task, accept the result, edit it, or abandon it. Look for recurring reasons the feature fails. Keep monitoring costs and response times alongside product usage.
Review meaningful examples with someone who understands the customer’s work. Anthropic’s evaluation guidance distinguishes code-based, human, and model-based grading; the right combination depends on what you need to judge. Read the testing guidance.
Keep a record of the model, prompt, and workflow version associated with a result. When you change the system, rerun your checks before assuming the new version is better.
A useful brief for your first AI feature
- Customer and task
- Input and expected result
- Examples that count as success or failure
- Review, retry, and fallback behavior
- Data access and retention requirements
- Expected usage and an operating budget
- The product outcome you will measure
Bring that brief to an AI integration conversation. It gives the team something concrete to estimate and test, and gives you a way to decide whether the feature deserves a place in your app.
