Generative AI: Where the Money Really Is

In 2023, everyone put a chatbot on their website. In 2024, everyone realized nobody uses a chatbot for anything other than asking to "talk to a human".

The hype bubble burst. Now we're in the phase of boring utility. And it's in boring utility that fortunes are made.

Backstage AI > Stage AI

While the market was obsessed with generating pretty images on Midjourney, our corporate clients had real, messy problems:

• "I have 50,000 invoices in PDF and need to know how much I spent on ICMS."
• "I need to read 200 rental contracts and know which ones expire in March."
• "My tech support answers the same 'how do I reset my password' question 500 times a day."

Generative AI shines here. Not in "creativity", but in transforming unstructured data (text, image, audio) into structured data (JSON, SQL, Excel).

Real Case: The "Infinite Intern"

We recently implemented a system for a law firm. The problem: reading Official Gazettes searching for client names.

Before: 4 paralegals reading PDFs all day. High cost, high human error, infinite tedium.
After: A Python script calling the Gemini 1.5 Pro API. It reads the PDF, extracts the case, qualifies urgency, summarizes the matter and drafts the client notification.

The real strategy is not "give employees ChatGPT". It's putting a model where your most expensive specialist is spending the day copying and pasting data.

Stop Playing, Start Automating

If your AI strategy is "give employees ChatGPT", you don't have a strategy. You have a toy.

The real strategy is: identify where your most expensive specialists are losing time copying and pasting data, and put a language model in the middle.

Also read

• Generative AI ROI Beyond the Hype
• Autonomous Agents: Software That Decides on Its Own
• The Spotify Case and the End of Manual Code with AI