The current development and the wide use of Artificial Intelligence (AI) applications and systems have increased the demand for a faster development processes that could compromise the quality assurance of the whole development life-cycle. Quality plays a vital role in software reliability and easier maintenance after sale while the quality requirements are not well-defined in most software production processes due to rapid delivery milestones. This paper discusses the essential and essence of the Total Quality Management (TQM) for AI system development and summarizes the similarities between products and systems development life-cycles. In this paper, we propose the implementation of TQM throughout the different stages in AI system development and AI system training process. Finally, this paper recommends a set of measures to ensure a minimum level of quality assurance before system deployment.

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