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Artificial Intelligence Adoption and Organizational Decision Quality: A Quantitative Analysis

Author(s) Dr. Vijayant Srivastava, Dr. Richa Srivastava
Country India
Abstract A game-changer in business operations and decision-making, artificial intelligence (AI) is quickly becoming an integral part of modern organisations. This research looks at how different industries' AI adoption rates correlate with the quality of organisational decisions. We used a quantitative strategy based on an explanatory and descriptive research methodology. One hundred twenty-two managers, executives, team leaders, and decision-makers were surveyed using a standardised questionnaire to gather primary data. Decision accuracy, timeliness, consistency, strategic effectiveness, problem-solving efficiency, and risk reduction were used to measure organisational decision quality, while AI utilisation, integration, analytics support, decision-support systems, and employee acceptance were used to measure AI adoption in the study. Descriptive statistics, Pearson correlation, multiple regression, and One-Way ANOVA were used to analyse the data. Organisational decision quality was shown to be strongly correlated with AI adoption (r = 0.721, p < 0.001) in the study. A large amount of the diversity in organisational decision quality can be explained by the fact that AI adoption is a strong predictor of it, according to regression analysis (R² = 0.520). The results of the analysis of variance also showed that companies with different degrees of AI deployment had significantly different decision quality. Organisations that use AI technologies make better judgements with more precision, efficiency, and strategic efficacy, according to the report. With the business landscape changing at such a quick pace, the results show that organisations need to include AI into their decision-making processes to stay competitive and perform better overall.
Keywords Artificial Intelligence, Organizational Decision Quality, Decision-Making, Business Analytics, Organizational Performance.
Published In Volume 7, Issue 9, September 2026
Published On 2026-09-29
DOI https://doi.org/10.70528/IJLRP.v7.i9.2442
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