Today’s AI landscape is characterized by a gap. While it is often relatively easy to reach the proof of concept (PoC) stage, getting from a PoC to a reliable production system is often much more challenging than teams expect. As a result, by some industry estimates, nearly 80% of enterprise AI projects never make it out of the lab.
The problem isn’t a data quality or infrastructure issue, but rather a architectural positioning one. If teams over-engineer complex models before knowing what can go wrong in production, they are creating more problems to solve, while increasing the total cost