This paper aims to conceptualize artificial intelligence (AI) implementation as a socio-technical capability outcome and test whether happiness management, complexity leadership and digital infrastructure act as complementary drivers of AI implementation in technology-intensive organizations through direct and mediated pathways.
A quantitative survey was administered using reflective measures for four constructs: AI implementation [AI_Imp], happiness management [Hap_Mgm], complexity leadership [Cpx_Lds] and digital infrastructure [Dig_Inf]). Data (n = 387 valid responses) were analyzed in SmartPLS 3.3 using bootstrapping to test direct/indirect effects and PLS-Predict to assess out-of-sample predictive relevance.
All hypotheses were supported. Cpx_Lds positively influenced AI_Imp and strongly predicted Dig_Inf; Hap_Mgn significantly predicted both AI_Imp and Cpx_Lds; and Dig_Inf positively contributed to AI_ Imp. The model explained substantial variance in AI_Imp (R² = 0.579) and showed high predictive relevance for AI_Imp (Q²_predict = 0.433), Cpx_Lds (0.485) and Dig_Inf (0.338).
Key research limitations/implications include: the cross-sectional design, which constrains causal inference and calls for longitudinal studies to capture capability development and potential feedback loops; reliance on self-reported measures, raising common-method bias concerns and motivating multi-source or mixed-method evidence (e.g. usage logs, AI maturity indicators, external assessments); and sectoral/regional sampling, which limits generalizability and suggests cross-sector and cross-country replications to test boundary conditions.
The results imply that organizations should treat AI implementation as a socio-technical capability rather than an IT rollout. Leaders should institutionalize happiness management (psychological safety, trust, supportive HRM and communication) to reduce resistance and sustain learning while developing complexity leadership to coordinate across functions and manage uncertainty. Complexity-capable leaders should then prioritize digital infrastructure readiness (data pipelines, interoperability, security, governance) to move from pilots to scalable deployment. Together, these levers provide a roadmap and diagnostic basis for AI-readiness interventions.
Socially, the model implies that scaling AI should be coupled with responsible AI governance, which includes transparency practices, perceived fairness and algorithmic accountability, to sustain legitimacy and prevent harmful outcomes. It also highlights the need to monitor downstream workforce impacts, linking AI implementation to job quality, burnout risk, retention and overall well-being, so productivity gains do not come at the expense of people. More broadly, emphasizing well-being and adaptive leadership supports more inclusive, human-centered adoption in fast-changing tech ecosystems and cross-border labor markets.
This study integrates well-being-oriented management with complexity leadership and infrastructure readiness in a unified capability-chain explanation of AI implementation, clarifying how human-centered systems build leadership capacity, enhance infrastructure readiness and jointly accelerate implementation, offering a mechanism-rich, predictive model relevant to Iberoamerican management research.
