Here's what's slowing down your AI strategy and how to fix it

A significant development in AI deployment is the creation of highly accurate customer churn prediction models, such as one developed by a research team achieving 90% accuracy, which remains unused due to slow risk review processes within enterprises. This highlights a critical velocity gap where AI research advances rapidly, driven by open-source innovations and model churn, while enterprise adoption lags because of cumbersome governance, risk management, and compliance procedures that delay deployment and stifle productivity. The broader implications reveal that despite the rapid pace of AI innovationfueled by exponential increases in training compute and model complexityenterprise adoption struggles with integrating these