Role of Predictive AI in Sentiment Analysis in Service-Based Businesses

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.