AI automation, and the difference between a task and a decision

AI automation is reliable on tasks whose result can be checked immediately and unreliable on decisions whose consequences arrive later, so the useful line is not how hard the work is but how quickly you would notice it being done wrong.

AI automation connects a model to the things you already use and lets it act rather than only answer. That is a genuine step up and it changes what can go wrong.

The line is checkability, not difficulty

Summarising a document, extracting fields, routing a message, reformatting data: all easy to verify, and a mistake announces itself within seconds. Deciding what to buy, what to reply, or what to escalate: consequences arrive later, often after several more steps have been built on top. The second category is not harder for the model, it is harder for you to supervise, and that is the property that matters.

What automating a decision actually costs

Not the mistake itself, which is usually recoverable. The cost is that a wrong decision is acted on, and then acted on again, and by the time it surfaces there is a chain of consequences to unpick. Automation multiplies whatever it is given, including confidence, which is why a system that is right most of the time can be worse than one that asks.

What to give an automation instead of a decision

A description. An automation that reports what changed, flags what looks unusual and stops there is enormously useful and cannot compound a mistake. The moment it recommends rather than describes, it has crossed into the category you cannot check quickly, and it is worth crossing that line deliberately rather than by accident.

The rule worth applying before you wire anything up

Ask how long it takes to notice this being wrong. If the answer is seconds, automate it. If the answer is a day, put a human in the loop. If the answer is you would not notice, do not build it yet, because you are constructing something with no feedback and there is no version of that which improves over time.

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Describe something and watch it get built.

Open the builder, or start from a working app and change it.

Common questions

What should you automate with AI?

Tasks whose result can be checked immediately, such as summarising, extracting fields, routing a message or reformatting data. Decisions whose consequences arrive later are harder to supervise, and that is the property that matters, not how difficult the work is.

Why is automating decisions risky?

A wrong decision is acted on, then acted on again, and by the time it surfaces there is a chain of consequences to unpick. Automation multiplies whatever it is given, including confidence.

How do you decide whether to automate something?

Ask how long it would take to notice it being wrong. Seconds, automate it. A day, put a human in the loop. If you would not notice at all, do not build it yet, because it has no feedback and cannot improve.