Look at tasks, not just job titles.

Break your work into concrete tasks before deciding where AI might help.

Write down a real week.

Start with work you actually did. List tasks such as preparing a report, reviewing an application, reconciling records, explaining a decision or responding to a customer. A job title alone hides the differences between those tasks.

Describe the inputs and the result.

For each task, write what goes in and what a useful result looks like. Include the judgment needed: exceptions, missing information, relationships, local context or consequences.

  • What information is available at the start?
  • What does a good result need to preserve?
  • Who checks it, and how?
  • What happens when it is wrong?

Look for a small experiment.

Choose a task with a bounded input and an output you can inspect. Keep sensitive information out of an unapproved tool. Use a permitted example or a clearly labeled synthetic one.

Compare the assisted process with the way you currently do it. Include the time spent checking and correcting the result. Fast generation is only one part of the work.

Keep a task map, not a prediction.

This exercise helps you identify where to learn and experiment. It does not estimate the probability that your job will disappear, and it does not replace knowledge of your workplace.

Use this note: Task / input / expected output / judgment required / cost of error / possible experiment.

Choose one capability to strengthen.

Your next move might be learning a tool, improving verification, documenting a process or developing deeper subject knowledge. Choose something that makes your work more useful, then revisit the map after trying it.

An introductory TQO guide. This is a suggested practice, not a report of a test Tee has personally run.

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