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AI in Human Work – Abilities, Applications, and Value Creation

Lesson 4: The AI Value Test – Time, Quality, Risk, and Reuse

Lesson Objectives

By the end of this lesson, students should be able to:

  • Evaluate AI workflows using a practical value model.
  • Identify fake productivity and hidden review cost.
  • Capture evidence for course marketing claims.

Lesson Content

A workflow is valuable when it improves at least one of four outcomes:

  • Time: faster first draft, faster research triage, faster QA, faster planning, faster documentation.
  • Quality: better structure, more complete review, clearer writing, stronger examples, better options.
  • Risk: fewer missed requirements, better verification, clearer approval gates, safer data handling.
  • Reuse: templates, checklists, prompt libraries, SOPs, rubrics, reusable code, repeatable decision memos.

The trap is fake productivity. If AI creates a draft in two minutes but review takes an hour because the draft is vague, wrong, or unusable, the workflow did not save time. Paid training must teach how to reduce rework: better context, clearer constraints, examples, staged prompting, test cases, and quality rubrics.

Marketing claims should be supported by evidence. Instead of saying "save hours with AI," a stronger claim is: "In this lesson you produce a one-page decision brief, source checklist, and review rubric from raw notes in under 45 minutes."

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