Numerous AI initiatives encounter setbacks when businesses need to pay more attention to practical challenges prior to deployment. These obstacles frequently impede the company’s ability to utilize AI models’ capabilities in their enterprise software fully.  Here are five common reasons behind the failure of AI projects: Lack of Data Management and Enterprise-wide Data Strategy Ineffective data management can significantly undermine the efficiency of AI applications. This issue leads to creating machine learning models that need...