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Proactive quality management in the era of artificial intelligence: from detection of “frustration signals” to hyper-personalization
(2026) Telnov, A.S.; Kulatkskyi, V.V.; Тельнов, А.С.; Кулацький, В.В.
The conducted research led to the conclusion that in the conditions of digital transformations, the traditional model of ensuring the quality of a digital product, which is based on reactive principles, is ineffective. The approach to meeting the needs of consumers based on personalization requires digital product developers to ensure quality at the stage of its development. Using artificial intelligence to transition to a proactive quality management model will allow diagnosing and predicting possible future problems related to product quality. This approach emphasizes preventing problems, rather than identifying them after the product is used. One of the important features of the use of artificial intelligence in ensuring the quality of a digital product is its ability to automatically detect and classify “frustration signals”. The effectiveness of using machine learning systems in recognizing user behavior patterns has been proven. As a result, companies gain the opportunity to diagnose and solve problems in the user experience, as well as retain existing customers and attract new ones.