Most businesses are being told they need an AI strategy. What they need first is a way to tell which of their tasks AI will do well, which it will do badly, and which should be left to ordinary software.

A useful test

Ask this about any task: would you hand it to a capable new member of staff with written instructions, and check their work?

If yes, AI may be able to help. If the task needs perfect accuracy with no checking, or depends on knowledge that is written down nowhere, it probably cannot, at least not yet.

Where it works well

  • Drafting replies to common questions, for a person to approve.
  • Summarising long documents, reports or conversations.
  • Sorting incoming requests and sending them to the right team.
  • Pulling names, dates and amounts out of documents.
  • Searching your own material and answering from it.
  • First drafts of content, translations and code.

What these have in common is language, volume and a tolerance for review. A draft that is nine-tenths right still saves most of the work.

Where it does not

  • Calculations and exact figures. Ordinary software does these perfectly and should.
  • Decisions with legal, medical or safety consequences, with nobody reviewing them.
  • Tasks where nobody can say what a good answer looks like.
  • Processes that already run on clear rules and work.

Replacing a reliable rule-based process with AI trades certainty for flexibility you may not need.

Keep a person in the loop

The most dependable arrangement is AI that prepares and a person who decides. The system drafts, sorts or suggests. Someone with responsibility approves.

As confidence grows, the routine cases can be allowed through on their own, with the unusual ones still sent to a person. That boundary should be moved deliberately, on evidence.

What it really costs

  • Connecting it to the systems where the work happens.
  • Preparing the documents and data it will read.
  • Testing it against real examples before launch.
  • Watching quality, speed and spend afterwards.

The model itself is often the smallest part of the bill. The work around it is where projects succeed or stall.

Start small

Pick one task that a team does every day and finds tedious. Measure how long it takes now. Introduce AI for that task alone, measure again, and ask the team whether it helped.

A single proven use is worth more than a strategy document. It tells you what works in your business, with your data, and it earns the trust that the next step depends on.

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