Year: 2025
Type: article
Study design: Observational Study
Abstract: This research examines the potential of predictive Artificial Intelligence (AI) to identify customer sentiment in service industries, focusing on user perception, decision-making impact, and customer satisfaction. Drawing on data from 242 participants across different industries, the research examines whether business outcomes are significantly affected by AI-based sentiment analysis tools. The examination shows overall positive views of AI sentiment analysis, particularly with less experienced professionals, but the real impact on decision-making and customer satisfaction is moderate to low. Statistical indicators show that although AI is viewed as promising, its present implementation in organizational routines is still modest. The findings highlight a disconnect between potential and actual impact, and they indicate that companies may not yet be using the strategic potential of AI sentiment analysis tools to the fullest. The research ends with practitioner and researcher implications and prescribes the necessity for longitudinal and industry-focused future studies. Design/Methodology/Approach: The study utilized a quantitative cross-sectional survey design with data derived from 242 usable responses from different service-based industries. The participants were requested to rate their experiences and perceptions of AI sentiment analysis tools on a Likert scale from 1 to 6. Descriptive statistics were applied to determine the distribution of industry type and perception by levels of experience. Inferential testing, involving p-value determination and comparison of means of ranks, was used to ascertain the differences in perceived significance by experience groups. Skewness and kurtosis values were tested to examine the shape of the distribution, and correlation coefficients were used to investigate associations between AI sentiment analysis and organizational metrics, including customer satisfaction and decision-making processes. Findings: The results imply that views on AI sentiment analysis are overall positive, especially among less experienced professionals with less than one year of experience. Differences based on the level of experience were statistically significant, meaning that more experienced practitioners are more likely to be critical about the usability of the technology. Although there are positive attitudes, the real influence of AI sentiment analysis on decision-making and customer satisfaction is fairly low, with low correlation coefficients (from 0.069 to 0.164). Distribution analysis did not indicate a skew towards the left but did show a platykurtic curve, meaning there is a fairly even distribution of industry types with few outliers. These findings suggest that although AI sentiment tools are becoming more popular, their practical use in strategic situations is still underdeveloped. Originality/Value: This research provides unique contributions by exploring the perceptual and functional gaps in predictive AI use for sentiment analysis in service-oriented industries. It enhances the existing literature on AI adoption by specifically exploring the human and organizational aspects of AI integration. Differentiation by experience level gives us a refined picture of the role that direct exposure to actual business processes plays in shaping trust and adoption of new technologies. The research also points to areas in which AI remains to show compelling practical value, guiding future initiatives aimed at aligning AI tools with organizational objectives and performance measures.
DOI: 10.14419/xvdt8h16
Authors: Ms. P. Menaka, Dr. N. Bargavi
Institution: SRM Institute of Science and Technology
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415992570
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