Where should generative AI go in your product? Practical use cases

Use cases that tend to pay off

The most reliable applications assist a person rather than replace a decision. Drafting a first version from approved source material, summarizing long documents, extracting structured fields from messy text, and answering questions over your own knowledge base all share a useful trait: a human can quickly check the result.

These tasks work because the cost of a mistake is low and easy to catch, and because the model is doing tedious work rather than exercising final judgment. That combination is where generative AI most often repays the effort of building it.

• Drafting from approved source material for review
• Summarizing long or scattered content
• Extracting structured data from unstructured text
• Assisted search and question answering over your own documents

When a simpler tool is the better answer

Not every problem needs a model. Exact calculations, rule-based decisions, and lookups are better served by conventional code, search, or templates, which are cheaper, faster, and predictable. Using a language model for arithmetic or for a decision that must be exactly right adds risk without adding value.

A good design often combines both: deterministic logic for the parts that must be correct, and a model only for the part that genuinely benefits from language understanding. Choose the model because it helps the task, not because it is expected.

Scope one assisted task, then evaluate

Replace a broad ambition such as add AI to the product with a specific workflow: suggest a reply from our help center for an agent to approve. A narrow task gives you clear inputs, a clear definition of a good result, and someone accountable for the output.

Before polishing a demo, gather a set of ordinary requests, hard edge cases, and examples the system should decline. Agree how results will be judged. An impressive answer to three prompts tells you very little about routine behavior.

Design guardrails from the start

Decide which outputs can be used directly and which need review, especially where the system can change records, send messages, or trigger other actions. Define approval boundaries and a way to recover before connecting those actions, not after.

Make it clear to users when they are seeing generated content and give them a simple way to correct or escalate it. Respect the permissions of the underlying sources, and keep retention of any sensitive input to what the task genuinely needs.

Measure against the current process

Compare the assisted workflow with how the task is done today. Useful measures include how much a person has to correct the output, how often the task is completed successfully, response time, and cost per completed task. Record the limitations next to the numbers.

Treat launch as the start of evaluation rather than the finish. Inputs, sources, and model behavior change over time, so decide in advance who reviews quality and what would trigger a pause or a fallback.