Almost every organisation has experimented with generative AI. Far fewer have turned experiments into measurable business value. The difference is rarely the model. It is the discipline around use case selection, data, integration and governance.
Choosing use cases that pay back
The best early use cases combine high volume, clear quality criteria and accessible knowledge. Examples include drafting responses for service agents, summarising long documents, generating knowledge articles from resolved tickets and assisting sales teams with proposal content.
Grounding answers in your knowledge
Retrieval augmented generation connects a model to your approved documents so that answers reflect your policies, products and terminology. It also allows responses to cite their sources, which builds user trust.
Managing risk
- Use enterprise services with data protection terms, so your data is not used to train public models.
- Apply role based access so the assistant only retrieves what the user is allowed to see.
- Evaluate outputs against test sets before release and monitor them afterwards.
- Publish an acceptable use policy and train people to use AI well.
Measuring value
Agree a baseline before launch: time per task, quality scores, customer satisfaction or cost per case. Measure the same indicators after rollout and report them openly.
Explore our Generative AI solutions or speak to us about an AI Use Case Discovery Workshop.
