<![CDATA[This paper explores how non-technical teams can be the form of organizational adoption and quantifiable results of the so-called no-code AI platforms. Through the sequential mixed-method design, 32 organizations in the six industries supplied data complemented by large-volume data sets such as the Stack Overflow Developer Survey (n = 73,268) and Kaggle Data Science Skills dataset (n = 25,973). Hierarchic clustering produced the following three cases of adopters: early adopters in marketing and operations, pragmatic adopters in customer service and HR, and conservative adopters in finance and legal with high adoption differences (37.82-fold asymptotic, p = 0.001). Regression analysis identified functional success predictors like, MarTech integrations of the marketing system-based system (= 0.43, P = 0.001) integration of the operations systems-based system (= 0.52, P = 0.001) and privacy protection-based HR system (= 0.56, P = 0.001). Productivity analysis showed that initial implementation cost decreased output by -7 percentage in the first month, but was compensated in 2-3 and 4-6 months on marketing/operation and other functions respectively. In twelve months, long-term returns amounted to 37 per cent marketing, 31 per cent operations and 26 customer service. Three clusters were verified by calculation of ROI: high ROI in marketing/operations (143%-217%), moderate ROI in customer service (87% -112%), delayed ROI in HR, finance, and legal (31% -64%). A tested implementation model has been constructed, which relies on the use of functional approaches, levels of governance, capability-building and integration methods with good predictive validity (R 2 = 0.71, error rate = 12%). The evidence shows that the democratization of AI can be achieved through strategic alignment, risk-sensitive governance, and role-specific training that would optimize the use of AI and its long-term organizational value.]]>
