Five Practical Challenges in Enterprise AI / ML
By dotData
Updated for 2022 According to a recent Gartner blog about analytics and BI solutions, only 20% of analytical insights will deliver business outcomes through 2022. Another article by VentureBeat AI reported that 87% of data science projects never make it into production. And a global survey by Dimensional Research concluded that 78% of their AI/ML projects stall at some stage before deployment. Even in 2022, as many as 68% of data scientists admit to abandoning 40% to 80% of their Data Science projects. These results indicate an exceptionally high failure rate across analytics, data science, and machine learning projects. There are many reasons why so many projects fail to meet their business objectives. In this blog, we look at the top practical challenges that enterprise AI projects face and how you can mitigate them: Start with business problems you need to solveWhile AI is an incredibly powerful technology, it is…