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Humans make mistakes. Even when a strategy is per- fectly crafted to address a problem in hand, the implementation of such a strategy can still be plagued by execution errors if conducted by a human. The noise associated with human execution is one of the main contributors to the growth of the AI industry: autonomous arti- ficial agents are expected to execute the strategies that they are pro- grammed to implement without such noise. However, because the designers of such agents are human, errors may occur on the pro- gramming of such agents. This might lead to an AI agent that per- fectly executes the strategy it was programmed with, but the strategy is actually misaligned with the intended goals of the human who con- figured it, a problem of AI alignment. In this work, we explore, by means of an evolutionary game-theoretical model, how errors in the configuration of artificial agents (or in the choice of an artificial del- egate) changes the outcome of a collective risk dilemma (CRD). We find that for high risk situations, errors decrease the success rate in comparison with the case of perfect execution. However, it is better to delegate and commit to a flawed strategy executed perfectly by an autonomous agent, than to make execution errors ourselves.