ByTony Bradley,
Senior Contributor.
Enterprises are racing to adopt AI, but most run into the same problem once they move past early experiments: progress gets slower, not faster.
Technology often isn’t the primary issue. The real drag comes from the organizational systems wrapped around it — security reviews, legal checks, compliance requirements, cost controls, and development workflows that weren’t built for the speed of modern AI.
Business leaders want results. Developers want access to the best open-source and commercial models. Teams want to experiment without being blocked by uncertainty about data handling, licensing, or infrastructure. Yet each step introduces new questions about risk and governance. A model may outperform everything else a company has tested, but if no one can explain what data it was trained on, how it’s licensed, or what it will cost to run at scale, it won’t get far.
