The transformation of traditional testing towards Agile[1] quality management is accelerating to support value delivery to customers and end users.
Copyright: capgemini.com – “77% Of Organizations Are Investing In Artificial Intelligence Solutions To Bolster Their Quality Engineering”
Paris, November 9, 2023 – The 15th edition of the World Quality Report, published today by Capgemini, Sogeti[2], and OpenText, highlights the rising importance of Quality Engineering (QE)[3] to enhance both sustainability in business operations and value delivery to customers and end users. According to the report, 67% of organizations have incorporated QE at the core of their business operations, to ensure that technological advancements adhere to quality standards.
The report highlights that with high customer expectations, interoperability demands, regulations, evolving guidelines, and cybersecurity risks, testing now requires an approach that is more rigorous and agile than ever before. The trend of hyper-personalization is also adding to the complexity, as it demands exhaustive testing. Quality Assurance (QA) is therefore evolving from a pure testing scope to broader Quality Engineering (QE), which focuses on delivering value over volume to enable customer experiences, brand protection, and business outcomes. This shift requires a re-focus onto the end-to-end customer journey and collaboration with business teams.
The use of AI is on the rise in Quality Engineering, but an incremental approach is key
Trends in the use of AI to deliver quality outcomes are moving fast. Organizations cite for the first time, higher productivity as the primary outcome driven by AI (65%). Generative AI will make possible increased productivity and velocity, leading to more frequent deployments with a higher quality customer experience.
Respondents reported that using AI to improve the reliability of tests (33%) and reduce the number of defects (29%) was no longer their primary focus. This indicates a shift in the testing philosophy, with an increased tolerance for defects as long as they can be fixed quickly and efficiently. Continuous testing, inherent in Agile and DevOps4 practices across organizations, has accelerated this trend.
However, concerns related to security, privacy, and biased outcomes still need to be addressed, with 31% remaining skeptical about the value of AI in QA, emphasizing the importance of an incremental approach.
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Mark Buenen, Global Leader, QE and Testing at the Capgemini Group, commented: “The World Quality Report provides insights on the key trends and developments in QE. It sheds light on the evolving role of quality within sustainable IT and the opportunities for applying AI solutions, notably the huge potential of generative AI applications. It’s interesting to see the role AI can play, particularly in generating test cases and automating quality procedures, but also on the importance of quality practices to enhance value delivery to customers and end users. However, to ensure AI’s reliability within Quality Engineering long-term, organizations should take a gradual, incremental approach.”[…]
Read more: www.capgemini.com
The transformation of traditional testing towards Agile[1] quality management is accelerating to support value delivery to customers and end users.
Copyright: capgemini.com – “77% Of Organizations Are Investing In Artificial Intelligence Solutions To Bolster Their Quality Engineering”
The report highlights that with high customer expectations, interoperability demands, regulations, evolving guidelines, and cybersecurity risks, testing now requires an approach that is more rigorous and agile than ever before. The trend of hyper-personalization is also adding to the complexity, as it demands exhaustive testing. Quality Assurance (QA) is therefore evolving from a pure testing scope to broader Quality Engineering (QE), which focuses on delivering value over volume to enable customer experiences, brand protection, and business outcomes. This shift requires a re-focus onto the end-to-end customer journey and collaboration with business teams.
The use of AI is on the rise in Quality Engineering, but an incremental approach is key
Trends in the use of AI to deliver quality outcomes are moving fast. Organizations cite for the first time, higher productivity as the primary outcome driven by AI (65%). Generative AI will make possible increased productivity and velocity, leading to more frequent deployments with a higher quality customer experience.
Respondents reported that using AI to improve the reliability of tests (33%) and reduce the number of defects (29%) was no longer their primary focus. This indicates a shift in the testing philosophy, with an increased tolerance for defects as long as they can be fixed quickly and efficiently. Continuous testing, inherent in Agile and DevOps4 practices across organizations, has accelerated this trend.
However, concerns related to security, privacy, and biased outcomes still need to be addressed, with 31% remaining skeptical about the value of AI in QA, emphasizing the importance of an incremental approach.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Mark Buenen, Global Leader, QE and Testing at the Capgemini Group, commented: “The World Quality Report provides insights on the key trends and developments in QE. It sheds light on the evolving role of quality within sustainable IT and the opportunities for applying AI solutions, notably the huge potential of generative AI applications. It’s interesting to see the role AI can play, particularly in generating test cases and automating quality procedures, but also on the importance of quality practices to enhance value delivery to customers and end users. However, to ensure AI’s reliability within Quality Engineering long-term, organizations should take a gradual, incremental approach.”[…]
Read more: www.capgemini.com
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