# Finding Good Use Cases — AI Strategy, Ethics and Governance

Source: https://www.geekswithgeeks.com/en/ai-strategy-governance/strat-use-cases

> Look for repeatable, text- or data-heavy tasks with clear success criteria and tolerable errors.

## Where AI fits well

Good candidates share traits: the task is **frequent and repetitive**, involves **language or patterns in data**, has **a clear way to check quality**, and mistakes are **cheap to catch and fix** (a human reviews the draft). Examples: drafting replies, summarising meetings, classifying tickets, extracting fields from documents, searching internal knowledge, code assistance. Poorer candidates are one-off tasks, decisions where an error is severe and hard to detect, and anything where no one can say what "correct" means.

## A use-case canvas

Fill one in per idea. It forces the questions that decide whether a pilot is worth starting.

```text
Problem        Support agents spend 3 min per ticket finding policy text
Users          40 support agents, 1,200 tickets/day
AI role        draft a reply from policy + ticket; agent edits and sends
Success metric handle time -25%, customer satisfaction not lower than today
Data           policy wiki (clean), 2 years of resolved tickets (needs PII removal)
Risk           wrong policy quoted -> human review mandatory; no auto-send
Owner          Head of Customer Care
```

## Prefer "assist" over "replace" first

Drafts reviewed by a person are safer, faster to ship and easier to measure than fully automatic decisions. Move towards automation only after the data shows the quality is consistently high.

**Quiz:** Which task is a strong first AI use case?

- [x] Drafting routine support replies that a human reviews
- [ ] Fully automatic final approval of large loans
- [ ] A one-time unrepeatable decision
- [ ] A task with no way to check quality

*Answer:* Drafting routine support replies that a human reviews. Frequent, checkable, low-severity tasks with a human in the loop are the safest place to learn.