Almost every company has already tried generative AI. Someone opened ChatGPT, was impressed, and stopped there. What separates those who took a nice photograph from those who reap results is not the model, it is choosing the right use cases. Generative AI does not create value simply by being switched on; it creates value when it fits an expensive, repetitive process that tolerates review.
We selected five cases that, in the real experience of companies, show measurable impact. They all share the same pattern: high volume, repetitive task, context available as text, and a person validating at the end.
1. Internal assistants over documentation (RAG)
An assistant that answers questions based on the manuals, policies and procedures of the company itself saves hours of searching. The technique is called RAG (Retrieval-Augmented Generation): the model retrieves the relevant documents and answers grounded in them, citing the sources. It is one of the fastest-return cases because it tackles a daily, cross-cutting pain.
2. Triage and summarization of support requests
Reading, categorizing and routing hundreds of emails or tickets a day is mechanical work. A model classifies by topic and urgency, summarizes the essentials and suggests a draft reply. The team no longer starts from scratch and instead reviews and sends, so the average time per request drops visibly.
3. Assisted code generation (SQL, DAX, Python)
For data teams, AI speeds up writing queries, measures and scripts. It does not replace the analyst, but it removes the friction of starting from a blank page and remembering syntax. The productivity gain is real and is measured in tasks completed per day.
4. Structured extraction from invoices and contracts
Moving data from documents into systems is expensive and error-prone when done by hand. A model extracts fields (values, dates, parties, terms) in a structured way, ready for validation. Here the return comes from reducing hours of manual entry and costly errors.
5. Support for drafting proposals and reports
With the knowledge and tone of the company itself, AI prepares first versions of proposals, replies and reports. The person keeps editorial control, but starts from 70 percent done instead of zero.
What these five cases have in common
Notice the common denominator: in all of them, AI accelerates a person rather than replacing them, and human review is always present. That design is what makes the return fast and the risk low.
- Choose a problem with real pain and a clear metric (time, cost, errors).
- Build a small prototype with real data and measure against that metric.
- Design human review and limits before scaling.
In practice: imagine a customer support team that receives 400 messages a day. By introducing an assistant that suggests a category and a draft reply from the internal documentation, the average time per request can fall to a third, without taking the final decision away from the person.
Generative AI will not replace strategy or business knowledge. But applied to the right cases, it frees teams from mechanical work so they can focus on what matters. And in your company: which process would be the first to benefit from an assistant doing the heavy lifting?
