By 2025, around 40% of corporate software testing budgets will be allocated to solutions incorporating AI, reflecting a global trend: companies are increasingly using AI to improve the quality, reliability, and predictability of digital services (IDC, 2024). Traditional QA methods often struggle with growing system complexity, manual test maintenance, and incomplete coverage, leading to delays and missed risks. AI addresses these challenges by prioritizing high-risk scenarios, predicting potential failures, and optimizing test execution. This trend is especially important for platforms where system stability and correctness directly affect end users.
Modern business is unimaginable without digital platforms, and their reliability directly depends on the quality of APIs (Application Programming Interface)—interfaces connecting different services. API stability underpins telemedicine platforms, banking transactions, and the scalability of SaaS applications (Software as a Service). In high-stakes digital environments where families rely on accurate care information, even minor API errors can cascade into serious failures, making precise and proactive QA essential. Every month, systems become more complex, and QA engineers face increasingly sophisticated challenges.
Mykhailo Sheptun, QA Automation Engineer at the American senior living platform, which helps families find senior living communities, was among the first practitioners to introduce elements of artificial intelligence into real-world testing practices. The platform connects families with more than 17,000 facilities across the United States and processes millions of requests daily, including the selection of a home based on care criteria, cost, and rating. Reliability and speed of the system here are not just metrics but a vital social function. In addition, the system integrates with internal CRMs and senior living provider APIs, requiring strict control over data consistency through automated tests. With millions of requests flowing through the platform daily and high stakes for families relying on the platform, traditional testing could no longer keep up—AI-driven automation became essential.
Intelligence in Testing: Prediction Instead of Guesswork
"When I first started introducing AI into testing, it became clear: the QA philosophy is changing fundamentally," Sheptun shares. Previously, a QA engineer's task was to write as many tests as possible. Today, the goal is to identify the riskiest scenarios and minimize the cost of verifying them. Machine learning allows engineers to think probabilistically, predict potential errors, and automatically determine which tests to run first.
At Sheptun's platform, AI is integrated into critical processes: test run prioritization, test data generation via LLM, automatic failure summaries, and classification of unstable ("flaky") tests based on run history. This allows for maintaining high development speed while preserving quality. The system itself selects the scenarios most important to users, based on bug history and current changes.
Self-healing tests and dynamic data generation allow automation to adapt to interface and business logic changes. For example, ERP systems used on the platform manage complex booking and application processing workflows for senior living communities. Self-healing tests and dynamic data generation verify application and recommendation scenarios to ensure that any interface or database updates do not lead to failures for families searching for senior homes. "Automation covers scenarios that engineers might miss manually and checks them on the fly. This allows scaling services without risking quality degradation," the engineer says.
AI-driven test prioritization ensures that the most critical user scenarios, such as finding an available spot for a specific type of care, are tested first. Monitoring and analysis through AWS CloudWatch and Sentry allow predicting potential failures in request flows that directly affect elderly users and their relatives.
Performance with Intelligence: AI Predicts Failures
Combining AI with load testing opened new horizons. Integration of k6, a performance testing tool, into CI/CD pipelines reduced API response time by 42%. Machine learning analyzes key metrics (p95 and p99), predicts degradation before release, and generates load profiles closely approximating real scenarios.
In the platform's context, this means that millions of user requests searching for senior homes are processed reliably and without failures, even during peak times, such as holidays, when families actively look for care options. "Today we don't just record errors; we can predict their occurrence," notes Mykhailo. This provides businesses with two critical advantages: a more stable user experience and lower incident resolution costs. In socially significant services like the platform, this is especially important, as it concerns the safety and trust of millions of elderly people and their families. AI tools help minimize human errors and technical failures, increasing user trust in the platform and facilitating operators' work.
Team and Quality Culture: AI as a Partner
AI changes not only practice but also the QA culture itself. Instead of long log discussions and endless disputes over flake causes, engineers receive concise summaries and cause classifiers. "This frees time for engineering work: hypothesis generation, experiments, and strategic analysis," emphasizes Sheptun.
He also notes that AI does not replace humans but acts as an advisor. The final decision always remains with the engineer, especially in high-load systems. "It is important to build transparent and reproducible processes where AI is measurable and controllable. Without this, the technology can become a source of chaos," he warns.
Differences in AI integration between startups and corporations are also evident: startups experiment faster, corporations account for compliance, data privacy, and cost considerations. In both cases, the principle is the same: AI must be measurable, transparent, and reproducible.
Mentorship and Training: Preparing the Next Generation of QA
The complexity of the platform's systems and the limitations of traditional QA quickly showed the need for smarter approaches. This is why Mykhailo's AI-driven methods immediately drew attention from aspiring QA engineers. Mykhailo actively shares his experience with young specialists: he has taught over 800 students through PASV School, a leading U.S. QA automation education platform that trains engineers for real-world corporate roles and corporate training, many of whom now work at leading U.S. companies. The curricula include AI tools for generating test ideas, analyzing logs, and self-healing UI tests, helping engineers think with data, not just code.
"For beginner specialists, it's important not to fear AI but to start with simple tasks—generating test data or analyzing logs," Sheptun explains. "At the same time, you need to be able to control the results: version prompts, store datasets, and check response stability. Without 'safety rails,' AI easily turns from an assistant into a source of chaos."
Training and mentorship also include developing educational frameworks adapted to real business processes, including user interaction scenarios with senior living platforms and integration with CRM and external APIs. This allows new engineers to quickly apply acquired knowledge in practice and reduce errors in critical services. "Graduates of my courses immediately implement modern practices: they accelerate releases, improve stability, and minimize risks. This multiplies the effect across the industry," the engineer explains.
AI as a Strategic Tool
Mykhailo sees the next five years of QA as a field where AI will become an integral part of all processes. Data-driven approaches, observability-first testing, release quality prediction, and test environments as code are no longer futuristic concepts but real engineering practices.
"The future of QA is a synthesis of engineering, data, and intelligence. We stop merely catching bugs—we prevent them before they appear," the engineer summarizes. This opens new opportunities for business: stability, scalability, and competitive advantage in critical sectors such as healthcare, fintech, and SaaS.
Sheptun plans to create a QA Automation Academy in the U.S., where AI becomes a partner to engineers: "We will teach not just test writing but designing quality systems where intelligence and data work together with humans. This is the path to sustainable and socially significant digital business."
Today, test automation goes beyond code verification: it becomes an intelligent tool capable of predicting failures and optimizing service operation even before release. This is especially important for platforms where real human needs are at stake. In the coming years, the combination of AI, dynamic data, and self-healing tests promises not only to accelerate releases but also to create a more resilient and reliable digital environment. Intelligent QA systems change the very approach to digital services, making them safer, more reliable, and more predictable for millions of people.
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