What is the 30% rule for AI?
The '30% rule' is an informal rule of thumb, not an official standard, and it's used two main ways: AI can automate about 70% of a task while the final 30%, judgement, quality control, relationships, stays human; or the other way round, keep humans doing at least the critical 30%. Both versions carry the same advice: use AI for the bulk work, keep humans on the parts where being wrong is expensive.
Unlike Pareto's 80/20, the '30% rule for AI' circulates as a rule of thumb with no single, formal source, and it exists in a few overlapping versions. Knowing them all is useful, because the underlying advice is consistent and sound.
The most common version: AI can do about 70% of a task, and the remaining 30% needs a human. AI drafts the email; you make it sound like you and check the claims. AI generates fifty ad variations; you kill the forty that are off-brand. This maps to how the technology behaves: excellent at volume and pattern, unreliable at judgement, taste, and truth.
A second version flips the emphasis for jobs and businesses: expect around 30% of the tasks in most roles to automate, while the core of the role stays human. This tracks with what workforce studies from the big consultancies have found: task-level automation is broad, full-job replacement is rarer.
The practical version for a small business: let AI handle the repeatable 70% of your marketing production, covering drafts, variations, scheduling, and summaries. Hold the human 30% tight: anything a customer sees unedited, anything that makes a claim, anything involving your reputation, and any decision about strategy or money. The 30% is where trust lives, and trust is the whole game for a local business.
The rule's real value is as a design question: for each process you hand to AI, decide up front which part stays human and why, rather than automating end-to-end and hoping. That one habit accounts for most of the difference between the AI projects that work and the majority that fail without anyone noticing.
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