Artificial intelligence may be the next great thing for industry, but users still fear its shortcomings in business and digital testing.
While customer experience (CX) and quality assessment are radically different, the associated risks may extend beyond satisfaction alone. Outcries persist that AI-powered CX systems produce subpar results and threaten human agent jobs.
AI-powered digital testing tools raise similar concerns about accuracy and the potential replacement of human software testers. Gartner predicts that by 2027, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering processes. In addition, the software testing market is projected to grow at a compound annual growth rate (CAGR) of 5% from 2023 to 2027.
This growth has sparked fears that AI could replace software testers, with potential testing failures posing risks to the business software supply chain.
Tal Barmeir, co-founder of AI-powered software testing firm BlinqIO, believes that will not happen. Instead, she sees AI reshaping the role of human testers into “AI-assisted testers,” where AI enhances their productivity and efficiency. Barmeir argues that the fear of AI replacing testers overlooks its collaborative potential to boost, rather than replace, their capabilities.
“While AI integration in software testing is increasing, I think it is unlikely to replace human testers completely. AI-augmented tools excel at handling repetitive, data-intensive tasks, which can free up human testers to focus on more complex testing scenarios that require critical thinking and decision-making skills,” she told TechNewsWorld.
Addressing AI Fear Through Multichannel Strategies
Upskilling human workers to perform their jobs better in call centers, sales conversations, and testing centers can bear similar fruit. To that point, Barmeir offered that upskilling traditional testers to use AI-assisted tools can significantly enhance productivity and efficiency.
“By integrating AI tools, testers can automate mundane aspects of the testing process, allowing them to handle more tests in less time and with greater accuracy. This shift not only speeds up the development cycle but also allows testers to focus on areas requiring deeper insight, such as user experience and security,” she explained.
Barmeir presents a strong case for how using AI in software testing can improve business outcomes.
“Humans will remain essential in software testing because they understand context, interpret nuanced user behaviors, and make ethical decisions. AI is adept at analyzing data and identifying patterns but cannot understand context or make value-driven decisions, areas where human judgment is crucial,” she reasoned.
Additionally, advancements such as the AI Test Recorder elevate AI testing capabilities by automating the capture and reproduction of test scenarios. However, even with such tools enhancing the efficiency and scope of testing, human oversight remains necessary.
“Humans play a critical role in monitoring and interpreting results, ensuring testing aligns with ethical standards and business objectives, and providing the contextual understanding that AI currently cannot achieve on its own,” she added.
Isolated Use Cases, Shared Human Roles
Does this same rationale apply to AI replacements of human workers in other use cases? According to Barmeir, AI’s impact on employment varies across sectors. In some areas, like manufacturing or data entry, AI might replace repetitive tasks traditionally performed by humans.
“However, in fields requiring interpersonal skills, creative thinking, or complex decision-making, AI is more likely to augment human abilities rather than replace them. The key is to leverage AI as a tool that complements and enhances human skills,” she observed.
Barmeir suggested that companies must adapt their sometimes misguided assumptions to change the growing anti-AI use in customer experience situations. They can do that by focusing on transparency, personalization, and control to mitigate growing concerns about AI in customer interactions.
“This involves clearly communicating how AI is used, ensuring interactions are as personalized and empathetic as human ones, and giving users the option to choose between AI-assisted and human services,” she said.
The State of Software Testing
According to Barmeir, the software testing industry is undergoing a significant transformation driven by the integration of AI and machine learning technologies. Two things are at play.
One is a strong focus on automating routine testing procedures to improve efficiency. The other is maintaining a robust framework for security and performance testing.
“This shift toward automation is significantly shortening the time to market [TTM] for new software releases, which is a critical advantage in today’s fast-paced digital landscape,” she asserted.
By enabling faster deployment cycles, businesses can respond more quickly to market demands and iterate on feedback more effectively. This approach enhances the development process and helps companies remain competitive in the digital economy.
Pros, Cons, and the Future for AI-Assisted Testers
Automation greatly accelerates the testing process and reduces human error. It allows testers to focus on more complex aspects of the software, enhancing testing efficiency and accuracy.
AI improves upon manual testing by handling increased workloads or complexity more effectively. This capability makes it easier to expand testing efforts as software complexity and business needs grow.
The cons include the potential for overlooked issues and the need to continuously update to align with new software changes. Barmeir warned that this can require ongoing investment in time and resources.
The future of AI-assisted testers looks promising, with opportunities to democratize the field and make it accessible at an entry-level. AI tools lower the barriers to entry by enabling those with less technical expertise to participate in testing processes.
“This democratization means that more people can contribute to and engage in software testing, broadening the talent pool and fostering innovation,” she said.
Reshaping the QA Industry
Barmeir insisted that AI significantly enhances the QA industry by automating routine tasks and introducing sophisticated tools like AI Test Recorder and Playwright. These tools work together to accelerate the testing process while ensuring high standards of quality and reliability in software products.
The AI Test Recorder takes a test requirement and efficiently generates a complete test suite in Playwright. The platform’s code feature further simplifies the automation process by allowing users to create test scripts through recorded interactions with a website, eliminating the need for manual coding.
