Use generative AI in business with a responsible method
A useful generative AI experiment begins with a bounded task and a measurable benefit. It also defines what information may be used, who checks the result and what happens when the system is wrong.
What to remember.
- Start with a reversible, low-risk task.
- Classify data before entering it into any service.
- Evaluate quality with real examples and human review.
Choose a specific use case
Select a repetitive task where assistance could save time without making an irreversible decision. Define the current process, expected benefit, unacceptable failure and the person accountable for the final result.
Set data boundaries
Identify personal, confidential, copyrighted and strategically sensitive information. Check contractual settings, retention, model-training options and access controls before any real data is used. Provide anonymised examples when possible.
Build a representative evaluation set
Test ordinary, difficult and adversarial examples. Score factual accuracy, completeness, tone, traceability and the time required for correction. A convincing demonstration is not evidence of reliable performance.
Keep human responsibility visible
Specify who reviews outputs and which decisions cannot be delegated. Give reviewers the original sources and enough time to challenge the result rather than simply approve fluent text.
Document prompts, versions and failures
Keep a lightweight record of the task, model or service, important settings, validation rules and known limits. Monitor cost, error patterns and changes to supplier terms as part of normal operations.
Expand only after evidence
Compare the experiment with the previous method. Scale when quality, risk and total effort are understood, not because the prototype felt impressive. Stop or redesign the use case when verification removes the expected benefit.