Start with the decision, not the model
Artificial intelligence is an umbrella term, not a single implementation. Machine learning uses patterns learned from data to make predictions or generate outputs. A conventional program follows rules its developers specify directly. A learned system can handle situations that are difficult to describe with rules, but its mistakes may also be less predictable.
For a product team, the first question is practical: what decision should become easier? Sorting support messages, suggesting page copy, and detecting unusual account activity are different problems. Calling all three an AI assistant hides the different evidence, permissions, and failure handling each one needs.
Technical foundation: Google: What is machine learning?
Prediction and generation solve different jobs
A classifier assigns a label, such as billing or technical support. A generative model produces content, such as a suggested response. Neither output should automatically become a business decision. A billing label can route a ticket; it should not authorize a refund. A fluent response can help an agent write; it should not invent the customer's contract.
Consider a theme marketplace. Search ranking may help customers find suitable designs, while a writing assistant helps sellers explain features. Product availability and purchase entitlements should still come from the database. The most useful architecture often combines ordinary application logic with a small, clearly bounded model task.
Build a baseline before adding complexity
Write down the non-AI alternative. A searchable FAQ, a form with better labels, or a handful of routing rules might already solve the problem. Measure where that baseline fails using representative examples. Include ambiguous requests, incomplete information, and cases where doing nothing is the right answer.
Next, compare a model-assisted version against that baseline. Record whether the person completes the task, how often they correct the result, and how long recovery takes. A beautiful response is not necessarily a useful response. If users must inspect every sentence as carefully as writing it themselves, the benefit may be smaller than the demo suggests.
Design for the cost of being wrong
Not all errors deserve the same treatment. An awkward headline is reversible. Sending private information to the wrong customer is not. Classify outcomes by impact, then choose controls accordingly: editable drafts for writing, source links for factual answers, and explicit authorization for actions that change important state.
Give the feature an owner and a stop condition. A pilot might remain available only to a small internal group until a review set meets the agreed criteria. That is an illustrative rollout strategy, not a universal threshold. The right evidence depends on your users and the consequences of a mistake.
A first-feature checklist
- Name one user task and its current non-AI baseline.
- Separate generated suggestions from authoritative records.
- Collect examples of success, failure, and legitimate abstention.
- Provide a manual route when the model cannot help.
- Evaluate completed tasks and correction effort, not just attractive output.
