Where AI Adds Real Value in a Business
AI is genuinely useful, but not everywhere. The projects that pay off tend to share a few traits, and spotting them early saves you from expensive experiments that go nowhere.
This article describes the kinds of problems where AI reliably earns its keep, and the warning signs of a poor fit.
Good Candidates
- High-volume, repetitive tasks that drain staff time.
- Work involving messy text, images or speech.
- Tasks where a quick draft saves more time than it costs to review.
- Problems with plenty of historical examples to learn from.
Poor Candidates
AI struggles where mistakes are costly and hard to spot, where you have little data, or where the rules are clear enough that ordinary software is more reliable.
How We Assess an Idea
- Define the task and how you would measure success.
- Check whether the data needed actually exists.
- Estimate the cost of an AI error versus the saving.
- Run a small pilot before committing to a full build.
| Use case | Typical benefit | Risk level |
|---|---|---|
| Drafting routine replies | Time saved | Low |
| Sorting incoming enquiries | Faster routing | Low |
| Final pricing decisions | Limited | High |
| Legal or medical advice | Limited | Very high |
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.