AI in Business / Lesson AI08

When Should You Use AI and When Shouldn't You?

Use AI when it improves a clearly defined task, uses information you're allowed to use, produces results you can verify and has manageable consequences if it's wrong. Don't use it just because it's available. Sometimes a checklist, a standard automation or a person with clear authority is the better answer, especially where safety, staff or money are involved.

By the end, you should be able to

  • Compare AI, simple automation and a process fix
  • Evaluate whether errors can be checked
  • Choose a test proportionate to the consequences

The four-part suitability test

  1. Is there a defined problem? If the team doesn't know why work is delayed, buying an AI tool is premature. Measure the process first.
  2. Is the information appropriate? Use approved, necessary data. Sensitive or confidential material can make an otherwise simple task unsuitable for a particular tool.
  3. Can you check the result? Someone must be able to detect wrong facts, weak reasoning and unsupported recommendations before they matter.
  4. What is the consequence of error? A poor internal brainstorming idea is easy to discard. A wrong medical conclusion, unsafe instruction or unauthorised payment can be far more serious.

A task can pass the first three questions and still require strong controls because the consequences are high.

AI, automation or a process fix?

Situation Often the better starting choice
No one knows who should reply Assign ownership and escalation rules
Send a reminder after a fixed period Ordinary rules-based automation
Prepare alternative wording for an approved message AI draft with human review
Interpret unusual, incomplete customer requests Human review, potentially AI-supported
Authorise refunds or approve high-value payments Human approval and controlled systems
Identify patterns in appropriately prepared data AI or conventional analysis, with verification

This is not a ban on AI in higher-risk work. It is a reminder that stronger evidence, specialist review and accountable oversight are needed.

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