Title: Beyond Automation: A Framework for Strategic AI Augmentation in Professional Work
Authors: Dr. Arinaitwe Julius, Musiimenta Nancy
Volume: 10
Issue: 4
Pages: 190-200
Publication Date: 2026/04/28
Abstract:
The proliferation of artificial intelligence (AI) technologies in the contemporary workplace has engendered a paradigm shift from automation-centric narratives toward more nuanced frameworks of human-AI collaboration. This study examined the strategic augmentation of AI in professional work by investigating the determinants and outcomes of AI augmentation across six industry sectors in Uganda, drawing on a cross-sectional survey of 384 professional workers. Guided by a conceptual framework anchored in Technology Acceptance Model (TAM), Resource-Based View (RBV), and Cognitive Load Theory (CLT), the study sought to assess AI literacy levels, identify organizational and individual factors influencing AI augmentation adoption, and evaluate the relationship between AI augmentation and work productivity. A structured questionnaire was administered to a stratified random sample of professionals across healthcare, finance, legal, education, technology, and manufacturing sectors. Data were analysed using univariate descriptive statistics, bivariate Pearson correlation analysis, and Structural Equation Modelling (SEM) with AMOS 26. Descriptive findings revealed moderate-to-high AI literacy levels (M = 3.87, SD = 0.74) and AI augmentation indices (M = 3.72, SD = 0.81) across sectors, with the technology sector recording the highest mean augmentation score (M = 4.38). Bivariate correlation analysis established strong and statistically significant associations between AI literacy, technology acceptance, and work productivity (r = 0.612, p < .001 and r = 0.703, p < .001 respectively). SEM path analysis confirmed that AI literacy (? = 0.487, p < .001), technology acceptance (? = 0.425, p < .001), and organizational readiness (? = 0.312, p < .001) were significant predictors of AI augmentation, which in turn exerted the strongest direct effect on work productivity (? = 0.581, p < .001). Model fit indices confirmed excellent structural validity (CFI = 0.947; RMSEA = 0.048; SRMR = 0.054). These findings affirm the imperative for organisations to invest in AI literacy programmes, cultivate readiness cultures, and design human-centred AI integration strategies that transcend automation toward genuine professional augmentation.