AI APPLICATIONS IN EMERGING TECH SECTORS: A REVIEW OF AI USE CASES ACROSS HEALTHCARE, RETAIL, AND CYBERSECURITY

Abstract

Organizations increasingly deploy AI use cases to improve decisions, yet many implementations underperform because data, people, and governance readiness are uneven and the pathway from readiness to outcomes is rarely quantified. This study tested a readiness to adoption intensity to outcomes model using a cross-sectional, case-based survey (Likert 1 to 5) across three enterprise case settings in real operational contexts (healthcare, retail, cybersecurity). From 500 invitations, 342 responses were received (68.4%), and 318 usable cases were retained (63.6%), split across healthcare (n=108), retail (n=110), and cybersecurity (n=100). Key variables were data readiness, human capability, governance readiness, AI adoption intensity, and performance outcomes, with organization size, role group, and years of AI exposure as controls. Analyses used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression (standardized β, R²), plus mediation interpretation via the combined regression pattern. Construct means were moderate-to-positive (data readiness 3.62, human capability 3.55, governance readiness 3.48, adoption intensity 3.58, outcomes 3.67), and cybersecurity reported the highest governance (3.60) and outcomes (3.73). Reliability was strong (α=0.82 to 0.90). Adoption intensity correlated with outcomes (r=0.62, p<.01) and with data readiness (r=0.54), human capability (r=0.49), and governance readiness (r=0.46), all p<.01. In regression, readiness explained substantial variance in adoption (R²=0.48), led by data readiness (β=0.33, p<.001), followed by human capability (β=0.24, p<.001) and governance readiness (β=0.19, p=.002). Outcomes were explained (R²=0.52) by adoption intensity (β=0.45, p<.001) and governance readiness (β=0.21, p=.004), with a smaller direct data effect (β=0.12, p=.041) and a non-significant direct human capability effect once adoption was included (p=.180), indicating that skills primarily improve outcomes by increasing routine AI use. Implications are that organizations should prioritize data integration and quality, invest in workforce capability to sustain adoption, and strengthen governance to translate AI deployments into measurable gains.