siliconindia | | December 20189Being cognizant about these nuances and possible ways to mitigate them will make you better prepared to lead a team of Quality professionals for the 21st century or be a practitioner in these changing timesconcern as one does not fully know what to review and audit from a QA perspective in such work processes.3. Product owners and team (somehow) believe that `AI'-pow-ered work processes tend to be of `higher' quality ­ with little or no hu-man intervention, engineers tend to think the product or sub-product cre-ated with primarily AI-based learning or algorithms are of `higher' quality. There is confusion on what is `Qual-ity' at these junctures. While theory tells us, that AI leads to a certain lev-el of mastery of behaviors, and hence a better solution may be created in much lesser time, the same is always not true. Think of an example, when Alexa plays a different song from a different band, assuming it is closely aligned with your choices in the past, and you may tend to like it. These de-cisions are made as a virtue of learn-ing, and hence the dilemma is what if anything, can QA value-add? 4. Slow erosion or dilution of the value-add of QA as seen by engineers ­ all these nuances could lead to the eventual gradual erosion of perceived value-add of QA activities by the staff engineers.We examine some simple as-pects could make these nuances ap-pear less intense or to address them, albeit partly: 1. Realign `Mission Quality' & Vision ­ from redefining and setting expectations on whether Quality is limited to only post production activ-ities or active involvement through-out the life cycle. Many organizations today use various Quality and pro-cess improvement models/standards such as the ISO standards, CMMI, FDA/FAA and so on. Some of these models inherently expect the QA function to be an active involvement throughout the lifecycle, while focus-ing on Quality Control or Testing to break, find and fix defects. With AI & increased automation, involvement of Quality function at the early stages needs to be re-aligned in terms of the Who, Why, and How of the activities. Obtain buy-in from engineers and management before agreeing on what QA's involvement will be. 2. Use the power of increased Data Availability and Analysis - in the current `Smart Factory' scenar-io, one availability of more real time data, powered by the availability and close-knit operations of Cyber Phys-ical Systems. Choosing and using the right type of Data analysis will lead to prevention ­ using Predictive anal-ysis is more critical than ever before. Predictive analysis includes the use of statistical and quantitative meth-ods for analyzing trends, formulating baseline performance, and establish-ing models to predict one more pa-rameter of critical business concern. With Industry-4.0, powered by AI & Machine Learning (ML), analyses methods are likely to be lot more reli-able, due to availability of data that is powered from CPS and non-manual data. So, be prepared to learn and un-derstand (new) data analytics meth-ods and models.3. Practice day-to-day Risk Management ­ risk management gains importance as the learning process with AI & Machine Learn-ing being much faster, capable of making decisions quicker. The Qual-ity professional has to be ready for more proactive and rapid approach to risk management. 4. (Quality) Process Automation ­ while we have quite a few process and workflow automation tools avail-able, Quality function has to evolve and think of incorporating at least some level of automation themselves. Whether it is for Metrics analysis, or for Quality audit automation, this will aid in ensuring Quality function can perform and institutionalize activities much more efficiently, especially in an AI-powered environment. Being cognizant about these nu-ances and possible ways to mitigate them will make you better prepared to lead a team of Quality profession-als for the 21st century or be a practi-tioner in these changing times!
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