In 30 seconds

AI is worth considering when a task involves interpreting language or documents, happens often, and can be reviewed. It is a poor fit when the output must always be exact, errors are hard to catch, or the workflow itself is not understood.

Decide against the task, not against the technology.

Why this comes up

Once AI is available, the temptation is to use it everywhere. But many tasks are handled better and more cheaply by a fixed rule, a form, or leaving a capable person to do them. Deciding well is what separates useful projects from expensive demonstrations.

AI becomes more interesting when the task is

  • language-heavy or document-heavy, so interpretation is the hard part
  • repetitive and high-volume, so the effort pays back
  • currently time-consuming for people
  • tolerant of some uncertainty, with a person able to catch mistakes
  • easy to review, so a wrong answer is visible
  • supported by information the workflow can actually reach

The more of these are true, the more likely AI adds real value.

AI becomes less attractive when

  • the output must always be exact, with no acceptable variation
  • errors are difficult to detect
  • errors are expensive or dangerous
  • the task rarely repeats, so the setup never pays back
  • the required data is not available
  • the workflow itself is not understood
  • responsibility for the result cannot be assigned
  • no one can judge whether the result is good

Any of these should give you pause. Several together usually mean AI is the wrong tool, at least for now.

Treat it as guidance, not a score

You can weigh these factors, but resist turning them into a fake precise score. They are a way to structure a judgment, not a formula that knows more than it does. The AI suitability checklist helps you work through them.

Ask the deciding questions

  1. Can the decision be written as a stable rule? If yes, prefer deterministic automation.
  2. Does the task need interpreting variable language or documents? That is where AI earns its place.
  3. What happens when the interpretation is wrong, and can someone catch it?
  4. Does the system need to act, or is a draft enough?
  5. Is the underlying workflow actually understood?

What this does not solve

Deciding that AI fits does not tell you how to build it safely. Fit is necessary, not sufficient. You still have to decompose the task, design the authority, and test it.

Good enough to move on when

  • You can name where interpretation, not rules, is the hard part.
  • You know errors can be detected and by whom.
  • The underlying workflow is understood.
  • You can justify AI over a simpler mechanism, or you have chosen the simpler one.