AI in Production: What We Learned Implementing LLMs in 30 Companies
From euphoria to real ROI: the patterns, pitfalls, and shortcuts that differentiate AI projects delivering value from those that become abandoned demos.
Vinícius Athayde
Sistemas de IA para operações
The Bubble Has Passed
In 2023, it was impossible to talk to a director without hearing "we're going to make a chatbot with GPT." In 2025, the question is different: will this turn into revenue or remain a pet project?
After 30 implementations, we've identified the patterns.
What Works
1. Invisible Automation
AI behind existing processes, lead qualification, ticket classification, call summarization. The user doesn't even know there's an LLM involved.
2. Verticalized Copilots
Not "ChatGPT for business." A specific agent, trained in the context of the operation, integrated with CRM/ERP. Solves 1 problem, solves it well.
3. Semi-structured Analysis at Scale
Reading 10,000 reviews, transcribing 1,000 calls, classifying open-ended responses. Work that was once unfeasible becomes trivial.
What Doesn't Work (Yet)
- Complete replacement of human customer service in complex cases.
- Autonomous content generation without editorial supervision.
- Critical financial decisions without human-in-the-loop.
The Three Most Common Mistakes
- Starting with the technology, not the problem. "Let's use AI" is not a proper brief.
- Underestimating the cost of context. Connecting internal data is 80% of the project.
- Ignoring the feedback loop. Without a correction mechanism, the model becomes a trapped cat.
The Golden Rule
If you can't measure the agent's ROI in 90 days, you're making a demo, not a product.