AI is an evolving technology and it's somewhat true that AI projects have a high risk of failure attached to it. Last year, many big sites predicted that major

AI is an evolving technology and it's somewhat true that AI projects have a high risk of failure attached to it. Last year, many big sites predicted that major data science projects would face failure in the future. According to a report, 87% of ongoing projects will fail in delivering the desired results. What I suggest is that it is imperative to do continuous in-depth research on a particular use case and the supported models before starting working on it. Data Science technologies are much improved and advanced now compared to 10 years ago but there is a lot more to improve when it comes to meeting end-user expectations and real-life implementation of an Enterprise AI project. AI operations and processes is one factor but there are many other reasons that lead to failure of data science projects. These include: - Absence of comprehension about AI tools and methodology. - Poor Data Quality - Not opting the right tool - Bad Strategy from top management. - Lack of investment in employees who know data very well We know that data science uses statistical concepts and theories that exist since ages and most of them have been successfully built by the numerical scientists from the big tech giants. What’s important for the organizations now is to understand the use case and hire the resources with the right set of skills. Many organizations are confused about the required skill set because the field of data science is new for the top management and they are just trapped by the hype created around. Most of them are naturally thinking of hiring a statistician having a PhD in statistics, which is not required most of the time. As per my experience, this is true only if the task in hand is to do research in advanced statistical models and algorithms. With the abundant supply of the off-the-shelf modeling tools and technologies in the market, 99% of the times organizations just require a well-equipped resource who is 1) skilled in the right tool that is compatible with existing technologies. 2) have good understanding of certain statistical models and techniques. This article has done an analysis on why major AI projects failed, and summaries it very well https://thinkml.ai/five-biggest-failures-of-ai-projects-reason-to-fail/ https://thinkml.ai/five-biggest-failures-of-ai-projects-reas...