Executive Summary


Whether it's self-driving cars, facial recognition or chatbot, we are becoming accustomed to coexisting with artificial intelligence-powered machines (IA). For five years, this ancient fantasy has been "the" central theme. Real-world applications in industry, finance, and even geopolitics demonstrate AI's potential.

When patients with Sars-Cov2 are diagnosed, algorithms can help physicians anticipate potential complications.

Over The Bridge, a Canadian organization that assists musicians struggling with mental health issues, are using an artificial intelligence to generate songs that might have been in their repertoire.

Retailers are investigating AI to better differentiate themselves by anticipating customer expectations, supporting them in their purchase and bringing emotion. 

As a result of digital transformation, enterprises are being compelled to innovate at breakneck speed today. We must commit resources to developing new sources of customer value while maintaining operational agility. Otherwise, like Nokia, we risk waking up one day to discover that despite doing nothing "wrong," we somehow lost. The speed of digital transformation is already staggering, and it will only accelerate further.

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We require assistance.

Anyone responsible for the development and delivery of software understands that the traditional methods of developing and delivering software are insufficient to meet this new demand. Not long ago, the majority of companies released software on an annual, biannual, or quarterly basis. Iterations now typically last two weeks or less. While delivery cycle times are decreasing, the level of technical complexity required to deliver a positive user experience and maintain a competitive edge is increasing—as is the rate at which compelling innovations must be introduced. These competing forces create a chasm in terms of quality engineering. Many of us have turned to Continuous Testing to help close this gap. Nonetheless, looking ahead, it is clear that even Continuous Testing will be insufficient.

We require AI-driven quality engineering to accelerate development and continue providing a positive experience to our final users.

This report aims to assist you, the reader, in improving the quality, velocity, and efficiency of your quality engineering activities work through the use of artificial intelligence.

In partnership with the top quality assurance technology companies, we are going to delve into advanced use cases, to assist you, the reader, prioritize effort and investment.

Section 1 is for executive leadership responsible for setting the direction and culture of the company. We introduce Artificial Intelligence and Quality Engineering separately and then discuss their convergence.

Section 2 examines how to dynamically optimize test design activities, using the concept of digital twin.

We wish you an excellent read.


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Contact the author:

Antoine Aymer
Antoine Aymer
Global Strategic Portfolio Director for Testing
Phone: +33767793048