During the last year, the influence of Artificial Intelligence and related technologies skyrocketed in many areas of the IT industry, and Quality Assurance is no exception. According to the data from ZipDo, in the middle of 2023, 44% of organizations admitted that they had already implemented AI in their QA process, and the other 19% had been planning to do it within two years. In 2024, the trend becomes even more prominent. The influence of machine learning technologies in the field is twofold. On the one hand, it introduces major changes to the established processes of quality assurance, introducing new tools and challenging companies to adapt to emerging technologies and approaches and implement them into their practice. On the other hand, the increasing usage of AI implies new types of apps and services, which means new approaches to QA are required to ensure the expected quality of the user experience. Meiram Ismagambetov, a software quality assurance expert from Miami, Florida, shared his vision of the current state of the market and its changing trends.
To this date, Meiram Ismagambetov has over seven years of experience conceptualizing and leading QA programs for major companies working in various fields, including retail, finance, trade, and law industries. His current position as a QA manager at GNC Holdings LLC allows him to stay on top of current trends and challenges in the industry while elaborating and implementing novel solutions. His experience includes implementing AI solutions, making them an effective part of the quality assurance processes. Being a professional, he can see the field as a whole, understand the general picture, and recognize the most important trends, with one of the latter being the rising role of AI tools.
New Horizons and New Challenges
The field of Quality Assurance per se has a multidimensional impact. The work of the quality assurance expert bears influence to the stare of multiple parties, from the end users of software products to the community and economy as a whole. Meiram Ismagambetov describes one of the key skills necessary to tackle several challenges at once. “The complexity of software systems is increasing each year,” he says, describing his experience managing quality assurance at GNC Holdings LLC, “while the demand for the rapid reaction on emerging issues also increases. The complexity is further exacerbated by the necessity to ensure seamless functioning of software applications on multiple platforms and operating systems. Consequently, the need for new methods and technologies is hard to deny.”
Meriam Ismagambetov sees artificial intelligence as one of the tools whose impact on the field will increase soon. Throughout his career, he has been managing quality assurance teams for companies operating in various industries, such as GNC Holdings LLC, Computer Enterprise Inc., and Logical Paradigm LLC. His work included redesigning and launching websites, implementing critical internal applications for businesses, and launching loyalty programs. Whether he held the position of a QA analyst and consultant at a trading company or a QA Tester supporting a global client, he worked hard to ensure high standards in all aspects of quality assurance.
Meiram’s work brought significant benefits to the organizations he worked at by leveraging technology to optimize internal processes and elevating the quality standards both for internal processes and client services. His experience allows him to overcome challenges such as those that arose during the COVID-19 pandemic or the current disruptive effects of AI technologies.
Discovering Potential Benefits
While it is easy to jump to conclusions, arguing that AI will either “replace” human professionals or, on the contrary, will remain just a buzzword, the truth is much more complex. Meiram Ismagambetov supports the opinion that the most beneficial way of implementing AI in the quality assurance processes implies not the replacement but the integration of AI into existing processes. As an example, it is worth mentioning that both artificial intelligence and machine learning help reduce the risks of human error and automate routine tasks, thus freeing valuable resources for more nonstandard tasks.
Moreover, AI and related technologies are expected to play a crucial role in cyber security, thanks to the fact that machine learning technologies open new methods for identifying novel risks and vulnerabilities. AI-based solutions can detect malware, identify anomalies in the network traffic, scan software for potential vulnerabilities, and analyze user behavior. Consequently, it becomes possible to act proactively, preventing threats instead of reacting to them after they arise.
Meiram Ismagambetov compares the current situation on the market with his work in the Digital Transformation Project at GNC during the pandemic. “While revamping and improving our digital infrastructure to provide users with the best experience,” he explains, “we also needed to swiftly adapt to new challenges arising each day while ensuring efficiency, safety, and convenience for our clients. This experience clearly demonstrates the importance of agility and adaptability in the QA industry.”
This approach to the emerging challenges is not new to Meiram. During his tenure at Computer Enterprise Inc. in 2018-2020, his main focus was supporting one of the company’s clients, a global law firm. His role included working on critical applications used for billing and accounts receivable in reporting. “While the quality assurance is of utmost importance in any field,” he comments, “working with a tool that deals with sensitive matters, such as accounting processes, adds a whole new level of challenge, as the cost of an error is high not in a figurative, but in a very literal way.” Again, his work played a significant role in optimizing internal processes in the company, implementing automation, and reducing manual interventions, thus saving valuable resources.
Meiram adds that the industry demands the same level of adaptability today: the rapid development of AI and related technologies may and will lead to unpredictable changes in the market, so one needs to react fast to continue to grow.
The Threats That Come Along with the Territory
However, the impact of artificial intelligence is not limited to positive opportunities and new tools. New threats emerge as well. Some of them are related to the fact that building an AI tool for a specific purpose requires a dataset in the model to be trained. The quality, accuracy, and applicability of the results highly depend on the initial data quality. Consequently, the role of the quality assurance expert does not diminish but increases, as for the meaningful result to be provided, the data needs to be carefully curated.
Another issue with implementing AI tools into quality assurance processes is that the AI tools, whether used in quality assurance or other IT fields, often remain a black box, making it hard to see the process that led to a particular assessment or decision. This, in turn, highlights the role of the professionals able to validate the results provided by such tools.
Moreover, the proliferation of AI models creates a new category of threats, which needs to be covered by testing as well. Meiram Ismagambetov mentioned several security threats and challenges he had to account for during his tenure at Logical Paradigm LLC, where he performed the role of a QA analyst from 2017-2018. In the company, he had been participating in creating a web application for precious metals trading, “TradeDesk,” and a depository management app, “Metal Apps.” “In such fields as trading stocks and precious metals, several factors must be accounted for, including regulatory demands and user data protection. In a situation where an error in an app may lead to major losses, the significance of QA testing is immense.” He adds that new attack vectors involving AI should be accounted for if a similar project were to be implemented today.
Whether we talk about the positive or the negative sides of AI’s impact on quality assurance methods and technologies, it will increase the demand for the skills and the ability to learn for each expert working in the field. “With the technology rapidly developing,’ Meiram Ismagambetov concludes, ‘the ability to master new tools and ensure they do not bring in additional risks with them will be one of the key skills for any QA professional.”
