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Module
Module 3 of 4Lesson 1 of 6~10 min

Choose the task to hand over and frame the deliverable

Handing everything over to AI makes no sense, and picking the right task is half the job. This lesson helps you find a task that is worth handing over, decide how far AI should go on it and state in one sentence the result you expect, so you can tell later whether it delivered.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will be able to choose a task to hand to AI, decide how to work with it (automate, assist, delegate) and write the deliverable statement in one sentence.

Topics covered

  • delegating to AI
  • scoping
  • automate or assist
  • deliverable

Where it fits

The five-step method

How do I get from a real need to a useful deliverable, in a way I can repeat?

Lessons in this module

  1. Choose the task to hand over and frame the deliverable (this lesson)
  2. The five-step method, end to end
  3. Describe a complete request
  4. Choose the right tool for the deliverable
  5. Check the result and improve it
  6. Workshop: apply the method to your case

What you will learn in the course

This lesson is part of the course Put AI to work in your business

  • Explain, on a concrete case, why an AI assistant makes things up, forgets or misses recent information, and decide how to use it accordingly.
  • Sort data before giving it to an AI tool (allowed, anonymize first, forbidden) and configure the tool to match.
  • Choose which tasks to hand to AI and how to work with it (automate, assist, delegate) based on value, risk and the data involved.
  • Write a complete request (task, context, examples, format, constraints) and improve it over at least two documented iterations.
  • Pick the right tool for a deliverable and justify the choice on data, cost, licensing and integration.
  • Check AI output against an explicit checklist (facts, figures, sources, tone, compliance) before using it.
  • Turn a successful use into a reusable workflow with human checkpoints, and set the team's rules of use in an AI policy.

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