siliconindia | | December 20188Software Quality Center is one of the world's leading IT quality assurance consulting & management consulting organization offering strategic consulting & IT services in the areas of Software Quality Assurance and Software Process Improvement.IN MY OPINIONTHE AGE OF AI & THE AGE OF AI & THE AGE OF AI & INDUSTRY-4.0INDUSTRY-4.0INDUSTRY-4.0I ndustry-4.0 and Smart Factory-4.0 are here. Gartner estimates that by 2020, more than three million work-ers will be supervised directly or indirectly by a `robo-boss'. PWC research in 2015 reconfi rmed that almost 60 percent of U.S. manufacturers are using some sort of robotics technology. Many organizations that have started using these technologies and harvesting the data are reporting that they see anywhere between 20-50 percent reduction in the total cost of Qual-ity. `Quality' is an all permeating function in businesses today. It touches every individual and every department or function. It is hence imperative we need to identify how the role of this critical function will change with In-dustry-4.0 ­ and explore key aspects that you, as the Quality Leader or Professional will af-fect you. We explore the essential nuances you will face (or already be facing) with increase in AI across industries. We also look at how can one navigate what seems to be chaos and be successful. 1. Inability to defi ne clearly where and when `Quality' should get involved in product development and delivery. Most traditional paradigms, that are used today, have the Qual-ity assurance function or role get involved right at the beginning of the PDLC. Typical-ly, at the requirements gathering phase or just after the product team has analyzed require-ments. Quality function then plans on the in-volvement for the remainder of the phases and what activities would they be performing in supporting the product teams.2. Confusion on the `what' and the `how' of the involvement ­ QA can often get con-fused or blurred in terms of what level of visibility the AI-powered processes should get from a QA perspective, as most AI-pow-ered processes tend to be algorithm-driven and rather limited to no human involvement. QA leaders may feel these processes cannot have any value-add by adding a QA role. The `let's wait for the product to be out' usually prevails. The `How' is also an issue of much
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