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Gen AI is transforming the way we do things in everyday life in ways that were once the stuff of science fiction. It’s reshaping how we make decisions, streamline processes, and discover new insights. And as this technology weaves its way into our Software Engineering process, it is certain to transform how organizations approach Quality Engineering. But there are practical challenges to be addressed first…
Most organizations are embracing Gen AI as it offers a chance to innovate and grow but are facing challenges on operationalizing it and maximizing its potential value. In our latest World Quality Report, the 16th edition of the industry’s largest research study looking at the current state of Quality Engineering practices around the world, we find that a massive 96% of organizations have either started or plan to adopt Gen AI solutions for their quality engineering activities. This represents a sharp jump from 69% in the previous year.
But perhaps more remarkable number to look at, is the speed of adoption of Gen AI by organizations the world over – just 4% are not exploring Gen AI solutions at this time in this year’s survey, down from 31% in the previous year. So, the message is clear: Gen AI is not just the future – it’s already here and its transforming Quality Engineering before our eyes.
As organizations embark on this journey, we take a quick look at some of the hurdles that need to be tackled to be successful. There are various things to think through – from use case selection to skill management.
Use case experimentation
While organizations are increasingly adopting Gen AI and driving value, its introduction into the Quality Engineering space is still in its relatively early stages. One area where the benefits are crystal clear though is document-heavy workloads, which can be processed significantly faster compared to traditional means. Our survey respondents also expressed interest in using Gen AI for test reporting and defect analysis. However, simpler use cases, such as manual test case creation and automation script development, offer a quicker return on investment and a lower-risk approach.
Most organizations seem to still be experimenting with various use cases to identify which ones deliver the most significant benefits – and best returns on investment. There’s a lot to consider and there’s not always a clear framework of KPIs to measure cost against benefits. Without a clear framework for comparison, this evaluation represents one of the biggest challenges currently being faced by organizations, according to the data. Simply comparing the time taken without GenAI and with GenAI is not enough for a benchmarking exercise.
Starting with simpler use cases which align well with the SDLC can deliver more immediate and tangible results.
Boosting productivity, accelerating value
Also, our survey shows that reducing defects and cutting costs do not top the list of expected benefits from Gen AI and this reflects a deeper understanding of its true potential. Gen AI isn’t about replacing the human touch or magically improving Quality on its own. Instead, it’s a game-changer for boosting the productivity of Quality Engineers. It accelerates speed to value – in both manual and automated activities. – thereby making the entire Quality Engineering process more efficient.
Interestingly, 56% of respondents revealed that using Gen AI for gaining a competitive edge through innovation wasn’t a priority. And this is a clear indicator of another major challenge organizations are facing today with Gen AI – understanding how to structure Gen AI within the organization. The organizational structure driving Gen AI initiatives is critical to have the right focus on innovation and value. It appears that this is a conundrum for many organizations that is still to be figured out and seems to be quite fragmented from an organizational viewpoint.
Skillset conundrum: train versus recruit
From a broader perspective, the skillset of Quality Engineers has been evolving rapidly over the last few years. So it’s no surprise that we found insufficient skills (53%) to be a major challenge for Gen AI in Quality Engineering. These issues once again highlight the emerging nature of Gen AI and its relatively immature integration into the software testing lifecycle. As the requisite skillsets continue to evolve at pace, finding the rights skills will be a common challenge – do you train your existing people or recruit new talent?
We view this very much as a transitionary phase, as the technology will eventually evolve to become self-sufficient, with AI agents being used to automate tasks and drive outcomes with “humans in the loop” as the last line of defence for Quality.
In summary…
What is abundantly clear is that the pace of change is evolving at lightning speed, transforming how organizations approach Quality Engineering. Embracing Gen AI offers a chance to innovate and grow.
Gen AI will revolutionize Quality Engineering. The jury is still out on which generative AI solutions organizations will ultimately choose. Regardless of your chosen solution or use case though, advancing beyond the proof-of-concept stage to full implementation brings a host of known and unknown hurdles.
Having the right organizational structure with some talent infusion, clarity towards the business value you are driving and ensuring your team – including quality engineers, leadership, and other IT/business staff – are trained for the safe use of Gen AI and educated on distinguishing fact from fiction will be key to success.
To find out more, download a copy of the World Quality Report.
