Квалификационный кризис QA-инженеров: 90% компаний внедряют генеративный ИИ, а специалисты переходят к архитектуре продуктов

VMPL New Delhi [India], September 1: By 2025, nearly 90% of organisations were actively deploying or piloting generative AI inside their quality engineering workflows, according to the World Quality Report 2025 by OpenText, Capgemini and Sogeti. The role of the QA engineer is not disappearing. It is being rebuilt from the inside out. Quality Assurance engineers have long ensured software behaves as expected. Artificial intelligence is expanding that responsibility. As enterprises embed AI into products, they increasingly need professionals who can define requirements, evaluate model behaviour, collaborate with engineering teams, and shape intelligent systems from concept to deployment. For many QA engineers, the next career move is no longer into test automation. It is into AI Product Architecture. The World Quality Report 2025, published jointly by OpenText, Capgemini, and Sogeti, surveyed over 2,000 senior executives across 22 countries and found that nearly 90% of organisations are now pursuing generative AI in their quality engineering practices. Only 15% have achieved enterprise-scale deployment, leaving the majority still in early stages of building this capability. Skill gaps are a primary barrier: 50% of organisations report lacking the AI and machine learning expertise needed to scale. For QA professionals who move now, the window to enter this space remains wide open. The PractiTest 2026 State of Testing report adds sharper financial context. Senior QA professionals with 10 or more years of experience who prioritise leadership and strategy skills earn a 10.6% income premium over peers. Those who remain focused purely on technical execution face a 13.8% income penalty. The report calls this the "Specialist Penalty": at the senior level, the market stops paying for what you execute and starts paying for what you drive. Communication skills alone correlate with a 36.1% salary premium among senior testers, the highest multiplier in the dataset. The direction is clear. Professionals who move toward product strategy, system design, and business judgment will capture the premium. Those who stay in execution will see diminishing returns. According to Katalon's 2025 State of Software Quality Report, which surveyed over 1,400 QA professionals across North America, Europe, and Asia-Pacific, 82% of testers still use manual testing in their daily work, meaning the professionals who move early into AI-native, product-facing roles are stepping into a field most of their peers haven't entered yet. As more professionals accelerate that transition, a few institutions are beginning to redesign learning around these emerging careers. Here are three programmes preparing engineers for that shift. 1. Masters' Union Masters' Union has built a programme around the transition many experienced QA professionals are navigating: moving from validating software to architecting AI products. Its 15-month, full-time, on-campus Postgraduate Programme in Applied AI & Agentic Systems moves beyond conventional AI instruction by combining AI engineering, product management, and business in a single learning model. Students develop expertise across machine learning, AI agents, Retrieval-Augmented Generation (RAG), multimodal AI, evaluation, deployment, and modern AI systems before specialising in AI Product Management, Advanced AI Systems, or AI Entrepreneurship & Business Management. Across six terms, every student builds six production-grade, ready-to-ship AI products--not just academic projects--progressing from core applications to advanced builds spanning Small Language Models, multi-agent enterprise systems, and Physical AI, with some evolving their products into ventures.Rather than following a static syllabus, the curriculum is refreshed every quarter in collaboration with experts from Google, Amazon, Microsoft, OpenAI, Palantir, Atlassian, IBM, and PayPal, ensuring students work with technologies that reflect the pace of enterprise AI. With build studios, hackrooms, mentorship from more than 200 founders, CTOs, and AI leaders, and a campus culture centred on building, the programme mirrors how modern AI products are created. 2. IIT Kharagpur IIT Kharagpur remains recognised for postgraduate programmes offering rigorous foundations in artificial intelligence, machine learning, intelligent systems, and advanced computing. Backed by a research ecosystem, it suits professionals looking to strengthen technical expertise before pursuing specialised AI roles. Its curriculum is complemented by laboratory work, specialised electives, and an extensive research project, supported by AI research centres and collaborations across academia and industry. For QA engineers planning a transition into AI, its research-led approach offers a solid pathway into advanced engineering and applied AI. 3. IIT Roorkee IIT Roorkee has expanded its AI and data science ecosystem through postgraduate programmes combining academic rigour with interdisciplinary applications. Students gain exposure to machine learning, deep learning, analytics, and intelligent systems while benefiting from industry collaborations and applied research opportunities. The programme also encourages learners to apply AI across diverse engineering domains, helping strengthen problem-solving capabilities while building a solid foundation in modern AI techniques and deployment practices, reflecting how technical education is adapting to changing enterprise AI requirements. From Testing to Building The move from QA engineering to AI Product Architecture changes what "quality" means. It is no longer confined to catching defects before release; it extends to judging whether a model's behaviour, its failure modes, and its trade-offs are acceptable for the product being built. That is different from writing test cases and demands a different kind of training. QA professionals now have more than one credible route into that discipline. Whether the priority is deep research grounding, interdisciplinary technical breadth, or an execution-first, product-facing curriculum, the institutions above show that structured pathways already exist for making this shift. As quality engineering moves upstream toward product design and system architecture, professionals who invest in this transition now are well placed to shape AI product roles. (ADVERTORIAL DISCLAIMER: The above press release has been provided by VMPL. ANI will not be responsible in any way for the content of the same.)