AI in business: from an idea to a controlled pilot

01

Not every problem needs AI

AI can help with unstructured text, documents, classification, contextual search and drafting work for review. Stable rule-based data flows may be cheaper and easier to control with conventional automation.

First ask which decision or task needs to become faster, more accurate or less expensive. Compare technologies only after the outcome is clear.

02

How should the first use case be selected?

A good pilot has a limited scope, representative examples and an outcome that can be evaluated.

• classifying or summarising documents for human review;
• searching an approved knowledge base;
• drafting a response, report or description;
• detecting missing or inconsistent information;
• supporting an employee without making an autonomous high-impact decision.

03

Data, access and human oversight

Define the types of data, permissions and vendor terms before testing. Confidential, personal or commercially sensitive information should not be sent to arbitrary public tools.

Decide who approves outputs, how errors are recorded and when the system must refuse or escalate. Human oversight must be part of the actual workflow.

04

When is a pilot successful?

Set criteria before testing: accuracy, completeness, processing time, cost per case, correction rate and escalation rate.

A good pilot may still lead to a decision not to deploy. It can reveal that data, scope or a simpler alternative needs attention, protecting the organisation’s budget.