For quality assurance (QA) leaders, managers and organisations, adopting AI is no longer optional, it is essential.
Based on a recent survey by McKinsey researchers, 67% of respondents expect their organisations to invest more in AI between now and 2027.
According to Asad Khan, the founder & CEO of LambdaTest, an AI-powered unified enterprise test execution cloud platform, “QA has evolved from traditional automation testing to AI-powered testing orchestration, from tedious bug reporting to AI-driven root cause analysis (RCA) and from manual trends forecasts to predictive analytics. And now we’re looking at AI-native testing agents.”
He explained that AI-native agents can autonomously plan, create, execute and even optimize test cases, which reduces manual effort while enhancing accuracy and coverage.
“With AI redefining how software is developed, tested and deployed, QA professionals must adopt new skill sets to remain relevant,” Khan elaborated.
He is convinced businesses must rethink their hiring and upskilling strategies to secure top talent and ensure QA practices are accelerated with the successful implementation of AI across the software testing life cycle.
The 2025 QA playbook
AI is reshaping the way software is tested, and QA professionals must adapt beyond traditional automation, Khan argued in a recent Forbes analysis.
“The best will stand out by blending technical expertise with strategic thinking, data fluency and collaboration,” he said.
Understanding how AI detects patterns, predicts failures and self-heals test cases will be a game changer, Khan continued.
“The best testers won’t just run scripts but also train and refine AI models to make testing more efficient.”
Moreover, QA pros will need to spot biases, validate data quality and ensure AI models perform accurately in real-world conditions.
“AI is only as good as the data it learns from,” Khan stated.
Also, ‘shift-left’ testing catches bugs early while ‘shift-right’ testing monitors software in production, he said. “The best QA professionals will blend both approaches to ship faster, with fewer surprises.”
In addition, AI-powered systems handle sensitive data, making security testing and compliance expertise more critical than ever.
“QA teams will work closely with security teams to stay ahead of threats,” Khan shared.
“QA isn’t just about testing but also about collaborating across development, product and business teams to ensure quality is baked into every stage,” he added. “Communicating AI-driven insights in simple terms will be a sought-after skill.”
Khan said he feels that QA in 2025 isn’t about running more tests, it’s about testing smarter, faster and with AI as a co-pilot. “The ones who master these skills will lead the way.”
“In 2025, the winners in software quality won’t just test better, they’ll test smarter.”
– Asad Khan
The challenges of AI implementation, ranging from tool adoption to process transformation, are reshaping hiring and retention strategies.
To attract and retain skilled QA professionals, businesses must invest in continuous learning, Khan said.
“AI in testing is evolving rapidly, making continuous education essential. Several courses and training programs can help QA professionals, or those entering the field, grasp the fundamentals” he noted.
Additionally, organisations should offer AI literacy programs tailored to different experience levels. Beginner-friendly workshops on AI fundamentals, hands-on training with AI-assisted testing tools and mentorship programs that pair experienced testers with AI specialists can drive engagement.
Finally, the QA career path may need to be redefined.
“With technological advancements, the role of QA professionals has shifted from test execution to AI-driven strategies,” Khan said.
“With the AI-infused testing landscape, companies are creating positions like AI-assisted test strategists, ML model auditors and data-driven quality analysts. These specialized roles not only future-proof careers but also help ensure that QA teams remain integral to AI adoption,” he shared.
Talent development
AI-powered learning platforms, such as LinkedIn Learning’s AI-powered recommendations and IBM Watson’s adaptive learning modules, are changing professional development.
“I strongly believe that personalized AI-driven learning paths help accelerate skill acquisition, ensuring QA professionals are equipped to handle AI-integrated testing environments,” Khan said.
He stressed that building an AI-first culture goes beyond training.
“It requires a fundamental shift in how teams approach quality assurance. Organisations must encourage QA professionals to experiment with AI-powered tools, automate repetitive tasks and integrate AI-driven insights into decision-making.”
He also argued for the establishment of internal AI communities, where testers collaborate with developers and data scientists, can accelerate adoption and innovation.
“AI governance should also be a priority. QA teams should be involved in defining ethical AI testing guidelines and ensuring models remain unbiased, transparent and aligned with regulatory standards,” Khan noted.
“The future belongs to those who test smarter. AI isn’t replacing QA, it’s redefining it.”
He is convinced that “the future belongs to organizations that treat AI as a force multiplier, enabling QA teams to predict risks, accelerate delivery and elevate software quality. In 2025, the winners in software quality won’t just test better, they’ll test smarter.”
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