When a team first explores generative AI, the list of possible features can grow quickly: summarize everything, add a chatbot, automate support, generate reports, and connect every internal tool. Starting with all of that makes it hard to learn what users need—or whether the technology is helping.
A better first feature is narrow enough that the team can describe its user, input, expected output, and failure conditions in a few sentences.
Find a repeated, bounded task
Look for work that happens often and has a recognizable shape: drafting a first response, extracting fields from a known document type, summarizing a meeting for a specific audience, or finding a relevant policy section. The user should be able to review the result without handing over an irreversible decision.
Avoid beginning with broad goals like “make everyone more productive.” They may be worthwhile ambitions, but they do not tell the team what to build or how to judge the outcome.
Define the before and after
Understand the current workflow. How long does it take? How often is the result corrected? Where do people lose context or repeat work? These observations create a baseline. Then choose a measurable target, such as reducing time spent on a first draft while keeping review quality at least as high.
Do not rely on a single satisfaction score. Track whether the output is useful, how often people edit or discard it, how long the full workflow takes, and what the feature costs to operate.
Keep a person in control
A good first release can offer a suggested answer, draft, or summary that a user checks before using. Make it easy to edit, regenerate, or reject the output. Explain what information the system used and what it could not verify.
This is not just a safety measure. Human review provides valuable feedback about missing context, confusing language, and errors the team should include in its evaluation examples.
Make the system observable
Record model and prompt versions, latency, validation results, and safe references to the task—not unnecessary confidential content. Review representative successes and failures with users. Test changes against a repeatable set of examples before rolling them out.
A small feature with a clear feedback loop teaches the team more than a broad demo. If it creates value, expand from evidence: add a neighboring task, improve integrations, or reduce manual steps. Let what you learn shape the next capability instead of assuming the first experiment has to become the entire product.