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80-10-10 Rule for AI: A Practical Way to Delegate Without More Busywork

The 80-10-10 rule is a practical AI workflow heuristic, not a proven productivity ratio. Set a clear outcome, delegate structured work you can check, and own the final review.

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The 80-10-10 rule is a workflow heuristic: decide what a good result looks like, let AI help with suitable middle-stage work, then review the result yourself. It is not a research-validated ratio or a promise that AI will save a particular amount of time. Its value is the discipline it encourages: set a clear target, delegate only work you can check, and keep responsibility for the final decision.

What does the 80-10-10 rule mean?

Asian Efficiency founder Thanh Pham describes a task in three parts: the first 10% is defining what good looks like, the middle 80% is work AI may be able to help with, and the final 10% is human review and refinement. These percentages are a way to think about a workflow, not measured shares that every task should follow.

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The starting point is the outcome, not the prompt. Before asking AI to do anything, name the result you need, who it is for, and what will count as finished. For example, a meeting brief might need a short summary, decisions, open questions, and follow-up tasks; anything requiring a person’s judgment should be flagged rather than silently decided by the tool. If the request is underspecified, ask what context the AI needs before it drafts.

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What work should you let AI do?

Consider delegating a portion of a task when it is repetitive or time-consuming, has a structure you can explain, and produces work you can check without starting over. Examples include:

  • Turning meeting notes into a draft list of follow-up tasks.
  • Grouping recurring themes in customer comments for a person to verify.
  • Drafting an agenda from a stated meeting goal and relevant notes.
  • Organizing research into sections that you can review against the original material.

Keep work with you when it is faster to do manually, depends heavily on personal preference or judgment, or needs to sound distinctly like you. AI can produce polished wording without producing a useful or accurate answer; fluency alone is not a reason to delegate.

How do you use the rule on a real task?

  1. Choose one recurring task. Pick a task such as preparing for a meeting, writing a project update, reviewing feedback, or planning a week.
  2. Define the outcome. Write one sentence describing what you need and the audience. Add concrete completion criteria, such as the sections a brief must contain or the decisions an update must make clear.
  3. Delegate a checkable portion. Ask AI to do the structured middle work, and tell it to identify uncertainty or points that require human judgment rather than inventing an answer.
  4. Review and refine. Check the facts, missing context, voice, and usefulness. Decide yourself whether the result is ready to send or rely on.
  5. Judge the result. Consider whether the task moved forward, how much correction it needed, and whether you trusted the output enough to use it. Counting prompts or AI-generated text does not show that the workflow helped.

How can you tell whether AI actually helped?

Assess the task against its intended outcome, not against how much activity the tool produced. A useful trial asks whether the result advanced the work, how much effort or correction was still required, and whether accuracy and context held up during review. These are practical evaluation questions, not published measurements of the rule’s effect.

Pham’s article and its related episode do not cite a study or attributable statistic quantifying productivity gains from this ratio. They support treating 80-10-10 as a way to structure delegation, not as evidence that a task objectively divides into those percentages or that the method guarantees time savings. The article’s guiding point is that more completed tasks are not necessarily the goal; a clearer result and a way to judge progress matter more.

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What responsibility stays with the human?

The person using AI remains responsible for deciding whether its output is accurate, complete, appropriate for the context, and ready to use. Review is not a ceremonial last step: it is where you catch unsupported claims, missing details, a mismatched voice, or a result that looks finished but does not serve the original goal. If you cannot check the work, do not treat it as safely delegated.

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