Abstract\r\nIn mid-1980, the importance of knowledge as an asset in the organization were discussed. In 1980 came the development of knowledge management systems that uses existing work in artificial intelligence and intelligent systems led to new concepts as knowledge engineering, knowledge acquisition, knowledge-based systems supplied to the world.\r\nKnowledge management (KM) has been regarded as an important role as a sustainable competitive advantage in organizations in these years. Studies mentioned that KM implementation is very costly and time consuming. It also needs changes in the organizational culture and structure, which requires considerable advanced planning. Hence, organizations must be able to estimate the success of KM implementation before embarking on it. The existing methods suffer from deficiencies such as execution time inefficiency, lack of flexibility, and lack of ability to present several scenarios. This study presents an integrated intelligent algorithm for estimating the success of KM implementation. The algorithm has two basic modules which are artificial neural network (ANN) and Analytical Hierarchy Process (AHP). In other word, ANN model has been designed and trained using AHP that results in estimating the success of KM implementation in a large bank. This has led to fewer calculations, faster and more accurate decision making, less complexity, and ability to analyze a lot of scenarios with only one or a few evaluators while the effect of the subjective opinion of one single evaluator will be avoided. The implementation results show that this model is significantly valid for measuring the success of knowledge management. This is the first study that uses present an integrated intelligent algorithm for estimating the success of KM implementation.

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