Abstract
Artificial intelligence (AI) is increasingly embedded in enterprise platforms, yet the ability to access an AI-enabled technology does not necessarily imply readiness to adopt it. This study examines whether digital skills readiness operates as a human-capital antecedent of AI-enabled enterprise technology adoption intention and whether technology readiness and performance expectancy transmit that relationship. The study follows the supplied IBM-group research plan and analyses the 385 populated cases in the accompanying master database. The questionnaire contains three-item measures for Digital Skills Readiness, Technology Readiness, Performance Expectancy and Adoption Intention, alongside additional adoption constructs. Because the supplied database contains 385 complete populated cases and 15 unused template rows, the analysis retains the 385 populated observations and applies an attention-check screen. The empirical strategy combines measurement diagnostics, descriptive analysis, correlation analysis, standardized composite path modeling and 5,000-resample bootstrap mediation analysis. The results show a positive association between Digital Skills Readiness and Technology Readiness (β = .392, p < .001), a positive association between Technology Readiness and Performance Expectancy (β = .195, p < .001), and positive effects of Digital Skills Readiness, Technology Readiness and Performance Expectancy on Adoption Intention (β = .174, .248 and .268, respectively; all p ≤ .001). The serial indirect effect from Digital Skills Readiness through Technology Readiness and Performance Expectancy to Adoption Intention is positive (indirect effect ≈ .020, 95% bootstrap CI [.008, .035]). The model explains 15.4% of variance in Technology Readiness, 4.6% in Performance Expectancy and 23.7% in Adoption Intention. However, the measurement diagnostics reveal weak internal consistency and convergent validity for several supplied three-item scales, including Digital Skills Readiness and Technology Readiness. Accordingly, the structural results should be interpreted as evidence from composite-score diagnostics rather than as a final confirmatory latent-variable model. The study contributes a capability-to-readiness-to-appraisal mechanism for understanding AI-enabled enterprise adoption in an emerging-market setting and identifies measurement refinement as a necessary next step before Q1 journal submission.