Artificial intelligence can save time and improve decisions, but only when it is applied to the right kind of task. Many disappointing AI projects fail not because the technology is weak, but because it was pointed at the wrong problem.
Tasks where AI tends to help
- Reading and summarising text: extracting key points from reports, emails or meeting notes.
- Classifying and routing: sorting incoming requests, documents or tickets into categories.
- Extracting information: pulling fields such as names, dates and amounts from forms and invoices.
- Drafting: producing a first version of a reply, description or report for a person to review.
- Searching knowledge: answering questions from your own documents and policies.
- Spotting patterns: highlighting unusual transactions or readings for a person to investigate.
These tasks share a pattern: they are repetitive, involve language or messy data, and benefit from a fast first pass.
Warning signs of a poor fit
- The task needs a guaranteed correct answer every time, with no human review.
- The rules are simple and stable, in which case ordinary software is cheaper and more reliable.
- There is not enough relevant, accessible information for the system to work with.
- Mistakes would be costly and hard to detect.
- Nobody owns the process the AI would support.
Keep people in the loop
For most business uses, AI works best as an assistant. It prepares, suggests or flags, and a person decides. Design the workflow so reviewers can see the source information, correct mistakes easily and report problems.
Evaluate before you build
- Collect a realistic sample of the inputs, including difficult cases.
- Define what a good output looks like and how you will measure it.
- Test a prototype against the sample and review the errors carefully.
- Estimate the cost per task at your expected volume.
- Decide what accuracy is acceptable, given human review.
Consider privacy and data handling
Understand where data is sent, how it is stored and whether it may be used by the provider. Avoid sending sensitive personal or confidential information to services without appropriate agreements and safeguards.
Measure the real outcome
The goal is not “using AI”. It is less time spent on repetitive work, faster responses or better decisions. Measure those outcomes, and be willing to stop if the improvement does not justify the cost and complexity.
