Customer experience is now the top priority for quality assurance

According to the summary scores, that measured the importance placed on a QA/testing task by 1,700 CIOs and other senior technology professionals, across ten industries, from 32 countries, "ensuring end-user satisfaction" is the most important priority (with a mean summary score of 5.85 out of a total 7). This is closely followed by "detecting software defects before go-live" at 5.81 out of 7, and "increasing the quality of software or product" (also scoring 5.81 out of 7).

Artificial intelligence, predictive analytics, and smart test automation are enablers in ensuring end-user satisfaction through QA

With customer experience objectives at the forefront, the report shows that organizations are already using Artificial Intelligence to optimize Quality Assurance. Up to 45% of respondents state that they are using intelligent automation, while 57% are experimenting with new approaches in testing intelligent applications, including Artificial Intelligence and Machine Learning elements. In addition, 59% of businesses cite that they are likely to focus on predictive analytics in the coming year, while 54% are interested in robotics automation, and 36% in machine learning.

Brad Little, Executive Vice President, Global Head of Application Services at Capgemini, said: "Quality Assurance is no longer a back office function. It is a critical activity that directly affects customer experience and this year's World Quality Report demonstrates that IT professionals are more aware of this than ever before. IT teams need to be rapidly up-skilling and embracing new technologies in the near future to keep up with the AI and automation-led transformation environment."

As AI in Testing and QA matures, three new roles will distinctly emerge: AI QA strategists, data scientists, and AI test experts

The WQR 2018 shows that a challenge exists in access to the specialist skills required in the new technology landscape. Over a third (36%) of respondents think that skills are lacking among professionals who need an adequate understanding of AI implications on business processes, and 31% feel that they are not sufficiently equipped with the data science skills required. (see figure 1 below)

Brad Little added: "Skill deficiency is a big hurdle that organizations will need to overcome; working with AI requires professionals with a diverse range of competencies such as algorithmic knowledge, mathematical optimization, and business intelligence skills. Tomorrow's IT department will have far more data scientists, AI test experts and strategists than seen in the past." 

AI and Automation on the rise to handle increased volume of QA testing

The focus on end-user satisfaction highlights the significant importance of other business priorities, such as responsiveness (speed-to-market), competitiveness, and innovation, the WQR concludes. It drives attempts to process vast amounts of structured and unstructured data, within the limits of fair practices. It also reflects a change in mindset, as a defined progression from early efforts in multi-channel experience.