Machine learning has been classified as a technology that can contribute to the optimization of the organizational operation and generate new business lines. However, it has not had the adoption that institutions such as CEPAL expect and the literature does not state whether the models for the adoption of current technologies explain the intention to use this specific technology, and especially, whether it is necessary to make any adjustments to be applied to a Colombian environment. Using a multi-methodological approach, from a systematic literature review, factors that could affect the adoption of machine learning were identified, which were contrasted with those indicated by experts from Chile, Colombia, and Spain through a Delphi method. As a result, eight factors were obtained, which were proposed in a research model that was validated through a survey applied to Colombian companies. Using multivariate statistics with structural equation modeling, it was found that facilitating conditions, ease of use, usefulness, executive support and data management are influential in adopting machine learning in a Colombian business environment. On the other hand, reliability and integrity, social influence, and price-value do not seem to influence the adoption phenomenon.