Generative AI in Business: Where to Start Without Burning Your Budget

Generative AI stopped being an experiment: it's already drafting support replies, summarizing documents, and qualifying leads in production, in companies of every size. The problem isn't the technology — it's where to start without spending a year and an entire budget on a pilot that goes nowhere.

Start with the use case, not the tool

The right question isn't "which AI model do we use?" but "which repetitive language task consumes the most hours?" The first candidates are almost always the same: answering frequent customer questions, summarizing long documents, extracting data from emails or PDFs, and drafting first versions.

Your data is the advantage, not the model

Language models are a commodity: all your competitors have access to the same ones. The difference lies in connecting them to your knowledge: your catalog, your policies, your case history. An assistant trained on your company's real documentation answers accurately; a generic one makes things up.

Design the human handoff from the start

The best system isn't the one that answers everything, but the one that knows when not to answer. Successful implementations define clear rules: which inquiries the AI handles alone, which require review, and which go straight to a person.

Measure from day one

• Resolution rate: how many inquiries does the AI close without human intervention?
• Time saved: hours of manual work eliminated per week.
• Quality: do the answers need correction? How often?

Without these metrics, you won't know if the project works, and you won't be able to justify the next investment. With them, the conversation with leadership changes from "this sounds interesting" to "this saves us X hours a month."