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This study investigates how Artificial Intelligence (AI) adoption enhances software development productivity in Sri Lanka using the Technology Organization Environment (TOE) framework. A quantitative survey was conducted with 158 professionals occupying diverse roles in Sri Lankan software firms, exceeding the required minimum sample size of 157 derived from Cohen's power analysis for multiple predictors. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to examine relationships among six TOE based drivers, AI adoption, and productivity enhancement. The results show that Organizational Readiness, Relative Advantage, and Top Management Support have significant positive effects on AI adoption, while Technological Readiness, Competitive Pressure, and Service Delivery do not exhibit significant relationships. AI adoption strongly predicts productivity improvement (=0. 436, p and mediates the impact of organizational and technological factors on productivity outcomes. These findings indicate that productivity gains arise when organizational readiness and leadership support are translated into structured AI adoption strategies rather than ad hoc experimentation. The study extends TOE based adoption research to the context of AI supported software engineering in a developing economy and offers a strategic roadmap for managers to strengthen organizational readiness, leadership involvement, and targeted skill development for systematic AI driven productivity improvement.
Senanayake et al. (Thu,) studied this question.