Before an AI assistant gets access to your work, test its boundaries

A convincing AI demonstration can hide a surprisingly difficult operational question: what happens when the assistant misunderstands an instruction? Before connecting it to a live inbox or customer database, write down one task in plain language. Preparing a draft reply is different from sending it. Summarising an invoice is different from approving a payment. Make that distinction explicit before comparing vendors.

Build a test set that includes awkward cases

Use examples your team can check: an incomplete request, two customers with similar names, an outdated price and a document containing misleading instructions. Record what a correct answer should contain and when the tool should ask for help. A collection of easy prompts tells you little about performance when information is missing. Keep confidential material out of an unapproved trial.

Limit what the assistant can change

Give the tool the minimum access needed for the pilot. Where an action affects another person, costs money or changes a record, define the human approval point. The operational checklist should cover credentials, logs, error reporting and a way to stop execution. A supervisor should be able to explain which actions occurred, rather than reconstructing them from a chat transcript.

Measure the whole job

Compare time spent preparing, checking and correcting the output with the previous process. A fast draft may still be expensive if it requires substantial review. Discuss the results with the people doing the work, including the cases they chose not to delegate. Expand only after the team can describe both the useful behaviour and the limits in repeatable terms.

Research context

NIST provides a voluntary framework for organizations to examine and manage AI risks. It is a useful reference for asking who is accountable for a system and how its behaviour will be assessed. Read the NIST AI Risk Management Framework for the source material.

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