Exploring the Significance of AI through Chi-Square Testing in Business Decisions (Journal of Information Technology and Digital World, 2024)
A computer system capable of performing operations like speech recognition, visual perception, decision-making, and language translation would typically need human intellect. Thanks to artificial intelligence (AI), this is now feasible. AI, the general term for any intelligent computer program, includes machine learning as a subset. To put it another way, not all AI is machine learning, but all machine learning is AI, and so on. The study of machine learning (ML) is a burgeoning discipline with many promising directions for future advancement in various techniques and uses. This study explores the effects AI and the ML in the decision made in the business. It also provides insights into how ML as well as AI are changing the landscape of analysis in business. A thorough examination of the literature survey and case study analysis, and expert interviewing as part of the materials and methods. The study's population consisted of all business owners in Lagos, Nigeria. A sample size of 185 business owners was selected using the convenience sampling technique. The primary instrument for data collection was a self-structured questionnaire. Online data was gathered, cleaned, coded, and recorded. Statistical Package for Social Scientists (SPSS 25.0) was used to code and evaluate the data collected. Descriptive statistics (frequency and percentage) were employed to assess the quantitative data collected from students and teachers, while the Chi-square test was used for inferential statistics with a significance level set at 5%. The findings revealed that machine learning algorithms employed does not significantly improve business analysis, and Natural Language Processing (NLP) significantly improves business analysis. It also revealed that the integration of AI with robotics significantly influences business processes and operations, and the effectiveness of planning and decision-making models within AI systems significantly improves business contexts. The study concludes by emphasizing the necessity of cooperation between companies, legislators, and other stakeholders and offers suggestions for businesses wishing to implement AI.
Keywords: Artificial Intelligence; Business Analysis; Machine Learning; Descriptive Statistics; Chi-Square Test.