Quality Assurance in AI System Development

abstracted brain in hard disk optics

Although more than 60% of CEOs worldwide consider Artificial Intelligence to be more important than the internet and fear significant competitive disadvantages, if it is not used, less than 11% of companies have managed to use AI systems productively in recent years. 

Did you know that 90% of AI projects do not materialize into a productive deployment despite a positive proof of concept? It is therefore important to introduce quality assurance measures during the development, as well as the operation of AI systems.

The whitepaper shines a light on the methods that can be used to ensure quality during development and before commissioning as well as the necessary measures to ensure the quality of productive AI systems.

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