The number nobody wants to hear
According to Gartner, 85% of AI projects never reach production. They don't fail because the technology doesn't work. They fail because nobody thought about production from the start.
At Synaptik we see the same pattern in every company that contacts us after a failed attempt. The agent works in the demo, the team applauds, and three months later it's still a prototype nobody uses.
The three reasons that keep repeating
1. Infinite scope, zero delivery
The project starts with "let's automate all customer service." Nobody defines what "all" means. The team spends weeks adding features nobody asked for. The result: a system that does 20 things poorly instead of one thing well.
The alternative: an agent that solves one specific use case. Responds to leads on WhatsApp. Classifies tickets. Books appointments. One thing, in production, generating real data.
2. Zero integration with the real workflow
The agent lives in a test environment that looks nothing like the daily reality of the business. It uses sample data. It's not connected to the CRM, the calendar, or the channel where customers actually write.
When it's time to connect it to reality, 40 problems appear that nobody anticipated. Incompatible data formats, permissions, rate limits, messages that don't fit the expected flow.
The alternative: connect the agent to the real channel from week one. With real data, real users, real problems. Bugs surface when they should, not three months later.
3. Nobody measures, nobody iterates
The agent launches and the team moves to the next project. Nobody reviews the conversations. Nobody measures how many leads are lost, how many responses are wrong, how many users drop off.
Without metrics, there's no iteration. Without iteration, the agent degrades. In two months it's no longer useful.
The alternative: a dashboard that shows what's happening in real time. Conversations reviewed every week. Prompts adjusted. An improvement cycle that doesn't stop.
What works
Projects that reach production have three things in common:
- Narrow, clear scope from day one
- Integration with the real channel in the first week
- Metrics and weekly review from launch
It's not more complicated than that. But it requires discipline and someone who's seen how things break in production.