The explosive growth in analytics, artificial intelligence, and cognitive computing has had a profound impact on organizations and businesses. It is even became much easier to find what is not impacted than what is impacted. These impacts have been categorized into areas such as organizations, work, and jobs, as well as potential unintended consequences. Consequently, mitigating such undesirable impact concerns for  AI-powered data science, that merged data science initiatives with new developments in AI, applications become interested topic. The study aimed at understanding the current status of AI-powered data science and identifying important factors that affect the meeting of motivations behind the aim. The main motivation behind this aim is the need to mitigate these undesirable impact concerns from multidisciplinary perspectives. The study adopted literatures review and thematic analysis as main research methods. The study’s findings showed that creation of data science department, people analytics; incomplete or inaccurate data and decision-making; privacy, biased or diminished human agency on decision-making- are major impact concerns for AI-powered approach applications. Also, a generalized human-mediated framework for AI and deep learning–based enterprise-level analytics solutions, synthetic data, complexity domains, master slave relationship were important factors that had effects meeting the motivations behind the aim.