Method for predictive technology incident reduction, comprising: using an information processing apparatus comprising at least one computer processor: receiving a change record for a proposed change to a computer application or a computer network infrastructure ; analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; predicting a probability of failure and an impact of the proposed change using a model, wherein the model is selected by a learning engine based on a prior implemented change and a failure and an impact of the prior implemented change, or by providing test data to a plurality of models, and selecting the model based on the results of the test data; in response to a low predicted probability of failure , or a high predicted probability of failure with a low predicted impact : approving the proposed change; and implementing the proposed change; and in response to a high predicted probability of failure and a high predicted impact : rejecting the proposed change; monitoring an actual failure and an actual impact of the implemented proposed change; scoring the model based on the actual failure and the actual impact; and selecting a new model in response to the score being below a predetermined threshold. using an information processing apparatus comprising at least one computer processor: receiving a change record for a proposed change to a computer application or a computer network infrastructure ; analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; predicting a probability of failure and an impact of the proposed change using a model, wherein the model is selected by a learning engine based on a prior implemented change and a failure and an impact of the prior implemented change, or by providing test data to a plurality of models, and selecting the model based on the results of the test data; receiving a change record for a proposed change to a computer application or a computer network infrastructure ; analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; predicting a probability of failure and an impact of the proposed change using a model, wherein the model is selected by a learning engine based on a prior implemented change and a failure and an impact of the prior implemented change, or by providing test data to a plurality of models, and selecting the model based on the results of the test data; in response to a low predicted probability of failure , or a high predicted probability of failure with a low predicted impact : approving the proposed change; and implementing the proposed change; and approving the proposed change; and implementing the proposed change; and in response to a high predicted probability of failure and a high predicted impact : rejecting the proposed change; monitoring an actual failure and an actual impact of the implemented proposed change; scoring the model based on the actual failure and the actual impact; and selecting a new model in response to the score being below a predetermined threshold. <EOS>