The Capabilities of Organizations in Implementing Artificial Intelligence: In Light of Data Incomprehensibility and Dependency

Abstract

The proposed study intends to evaluate organizational capacity for handling problems, investigate methods for optimizing AI implementation, and analyze the effects of data incomprehensibility and dependence on AI implementation. This research deals in its ability to provide organizations, academics, and policymakers with recommendations and insights regarding the difficulties associated with data incompressibility and dependency when deploying artificial intelligence in massive volumes of data. Artificial intelligence is being employed in many different industries, and its influence is growing every day. With the development of AI, it is becoming increasingly crucial for us that the system be dependable and trustworthy. Additionally, it can be explained in detail how the company can use AI to solve problems and enhance currently available solutions, as well as how to comprehend the difficulties associated with applying AI, how to protect and use data, and how to make sure that the AI technologies being used are morally right and good for society. There are five Independent Variables including Technological Infrastructure for Complex Data Handling, Data Management and Analytics for Unstructured Data, Adaptive and Agile Data Systems, Data Quality and Integration Strategies, and Ethical and Regulatory Considerations in Complex Data Handling. One Dependent Variable includes the Efficiency of AI Implementation in Handling Incomprehensible and Dependent Data.