Why most AI projects fail before they start
The real reasons AI implementations stall, and what to fix before writing a single line of code.
Most companies treat AI as a software problem. It isn't. It's a data and process problem wrapped in software.
When I talk to founders and CTOs about their stalled AI initiatives, the pattern is almost always the same. They hired great ML engineers, they bought the GPUs (or the credits), and they have a clear business goal.
But the data is a mess.
The Data Reality
Real-world data is not Kaggle data. It's messy, it's sparse, and it's often biased in ways that are invisible until you try to train a model on it.
"Garbage in, garbage out" is a cliché for a reason. But in AI, it's more like "Garbage in, confident hallucination out."
The Infrastructure Gap
The second reason projects fail is infrastructure. Not the model serving infrastructure, but the data engineering infrastructure.
If you can't reliably move data from your production database to your training environment, version it, and track lineage, you aren't doing AI. You're doing manual data science.
Conclusion
Fix your data pipelines first. Then worry about the model architecture.