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Testing AI with AI: Navigating the Challenges of QA

Robert Salesas
Leapwork

AI sure grew fast in popularity, but are AI apps any good?

Well, there are some snags. We ran some research recently that showed 85% of companies have integrated AI apps into their tech stack in the last year. Pretty impressive number, but we also learned that many of those companies are running head-first into some issues: 68% have already experienced some significant problems related to the performance, accuracy, and reliability of those AI apps.

If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment.

The Wild Wild West of AI Applications

That AI apps are buggy isn't necessarily a damnation of AI as a concept. It simply draws attention to the reality that AI apps are being managed within complex, interconnected systems. Many of these AI apps are integrated into sprawling tech stack ecosystems, and most AI tools in their current form don't exactly work perfectly out of the box. AI applications require continuous evaluation, validation, and fine-tuning to deliver on expectations.

Without that validation process, you risk stifling the effectiveness of AI apps with bugs and security vulnerabilities (security risks were one of the most commonly flagged issues for AI applications). Ultimately, that means the company doing the integration just becomes exposed to system failures, decreased customer satisfaction, and reputational damage. And considering how reliant the world will likely soon be on AI, that's something every business should aim to avoid.

Fixing AI … with AI?

Ironically, the answer many companies seem to have settled on for fixing their testing inefficiencies is AI-augmented testing. We found that 79% of companies have already adopted AI-augmented testing tools, and 64% of C-Suites trust their results (technical teams trust even more at 72%).

Is that not a bit paradoxical? Why fix AI with more AI?

In the right context, AI-augmented testing tools can be that second set of eyes (long live the four-eyes principle) to vet the shortcomings of AI systems with rigorous, unbiased reviews of performance. The reason you would use AI-augmented testing is to gauge how well generative AI deals with specific tasks or responds to user-defined prompts. They can compare AI-generated answers versus predefined, human-crafted expectations. That matters when AI models so often hallucinate nonsensical information.

You can imagine the many linguistic permutations for asking an AI chatbot, "Do you offer international shipping?" A response needs to be factually right regardless of how the question was asked, and that's where AI-augmented testing tools shine in automating the validation process for variables.

Do We Need Human QA Testers?

There's just one outstanding question: What happens to the human QA testers if everyone starts using AI-augmented testing?

The short answer to this question? They'll still be around, don't you worry, because over two-thirds (68%) of C-Suite executives we've spoken to have said they believe human validation will remain essential for ensuring quality across complex systems.  Actually, 53% of C-Suite executives told us they saw an increase in new positions requiring AI expertise. Fancy that ...

There's a good reason why humans won't disappear from QA teams. AI isn't perfect, and that extends to testing. Some testing tools can do things like self-healing scripts where the AI adjusts a test in line with minor app changes, but they can't handle the complexity of most real-world applications without any human supervision. We have AI agents, but they don't have agency. Autonomous testing agents can't just suddenly decide independently to test your delivery app to check whether your pizza orders are going through.

All of which is to say that some degree of human validation will be needed for the foreseeable future to ensure accuracy and relevance. Humans need to be there to decide what to automate, what not to automate, and how to create good testing procedures. The future of QA isn't about replacing humans but evolving their roles. Human testers will increasingly focus on overseeing and fine-tuning AI tools, interpreting complex data, and bringing critical thinking to the testing process.

AI offers huge amounts of promise, but this promise created by adoption must be paired with a vigilant approach to quality assurance. By combining the efficiency of AI tools with human creativity and critical thinking, businesses can ensure higher-quality outcomes and maintain trust in their increasingly complex systems.

Robert Salesas is CTO of Leapwork

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Testing AI with AI: Navigating the Challenges of QA

Robert Salesas
Leapwork

AI sure grew fast in popularity, but are AI apps any good?

Well, there are some snags. We ran some research recently that showed 85% of companies have integrated AI apps into their tech stack in the last year. Pretty impressive number, but we also learned that many of those companies are running head-first into some issues: 68% have already experienced some significant problems related to the performance, accuracy, and reliability of those AI apps.

If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment.

The Wild Wild West of AI Applications

That AI apps are buggy isn't necessarily a damnation of AI as a concept. It simply draws attention to the reality that AI apps are being managed within complex, interconnected systems. Many of these AI apps are integrated into sprawling tech stack ecosystems, and most AI tools in their current form don't exactly work perfectly out of the box. AI applications require continuous evaluation, validation, and fine-tuning to deliver on expectations.

Without that validation process, you risk stifling the effectiveness of AI apps with bugs and security vulnerabilities (security risks were one of the most commonly flagged issues for AI applications). Ultimately, that means the company doing the integration just becomes exposed to system failures, decreased customer satisfaction, and reputational damage. And considering how reliant the world will likely soon be on AI, that's something every business should aim to avoid.

Fixing AI … with AI?

Ironically, the answer many companies seem to have settled on for fixing their testing inefficiencies is AI-augmented testing. We found that 79% of companies have already adopted AI-augmented testing tools, and 64% of C-Suites trust their results (technical teams trust even more at 72%).

Is that not a bit paradoxical? Why fix AI with more AI?

In the right context, AI-augmented testing tools can be that second set of eyes (long live the four-eyes principle) to vet the shortcomings of AI systems with rigorous, unbiased reviews of performance. The reason you would use AI-augmented testing is to gauge how well generative AI deals with specific tasks or responds to user-defined prompts. They can compare AI-generated answers versus predefined, human-crafted expectations. That matters when AI models so often hallucinate nonsensical information.

You can imagine the many linguistic permutations for asking an AI chatbot, "Do you offer international shipping?" A response needs to be factually right regardless of how the question was asked, and that's where AI-augmented testing tools shine in automating the validation process for variables.

Do We Need Human QA Testers?

There's just one outstanding question: What happens to the human QA testers if everyone starts using AI-augmented testing?

The short answer to this question? They'll still be around, don't you worry, because over two-thirds (68%) of C-Suite executives we've spoken to have said they believe human validation will remain essential for ensuring quality across complex systems.  Actually, 53% of C-Suite executives told us they saw an increase in new positions requiring AI expertise. Fancy that ...

There's a good reason why humans won't disappear from QA teams. AI isn't perfect, and that extends to testing. Some testing tools can do things like self-healing scripts where the AI adjusts a test in line with minor app changes, but they can't handle the complexity of most real-world applications without any human supervision. We have AI agents, but they don't have agency. Autonomous testing agents can't just suddenly decide independently to test your delivery app to check whether your pizza orders are going through.

All of which is to say that some degree of human validation will be needed for the foreseeable future to ensure accuracy and relevance. Humans need to be there to decide what to automate, what not to automate, and how to create good testing procedures. The future of QA isn't about replacing humans but evolving their roles. Human testers will increasingly focus on overseeing and fine-tuning AI tools, interpreting complex data, and bringing critical thinking to the testing process.

AI offers huge amounts of promise, but this promise created by adoption must be paired with a vigilant approach to quality assurance. By combining the efficiency of AI tools with human creativity and critical thinking, businesses can ensure higher-quality outcomes and maintain trust in their increasingly complex systems.

Robert Salesas is CTO of Leapwork

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...