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The role of quality assurance (QA) and testing has changed from "mere defect finding" to being an "enabler of customer satisfaction and business outcomes", according to Capgemini and Sogeti’s 10th edition of the World Quality Report (WQR), which is published in conjunction with Micro Focus.
In order to meet this demand, the report reveals an increased focus is needed on the digitisation of the process, as well as an immediate need for expertise in digital technology for QA and testing, including security, IoT, blockchain and AI.
The report shows that 76% of applications across organisations are based in the cloud, and the number of organisations that work with IoT in some form has increased from 83% last year to 97% this year.
60% of respondents said that they are already using Blockchain in their portfolio or planning to use it in the coming year, and 57% of respondents said they had projects involving the use of AI for QA and testing already in place or planned for the next 12 months.
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, the report believes that 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.
Little added: “Skill deficiency is a big hurdle that organisations will need to overcome; working with AI requires professionals with a diverse range of competencies such as algorithmic knowledge, mathematical optimisation, and business intelligence skills. Tomorrow’s IT department will have far more data scientists, AI test experts and strategists than seen in the past.”
