AI can improve CX, QA without cutting jobs, say experts

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 fundamentally different, the associated risks can extend beyond satisfaction alone. The outcry continues that AI-powered CX systems produce subpar results and threaten the jobs of human agents.

AI-powered digital testing tools raise similar concerns about accuracy and potential replacement of human software testers. Gartner predicts that by 2027, 80% of enterprises will integrate AI-enhanced testing tools into their software engineering processes. Also, the software testing market is estimated to grow at a compound annual growth rate (CAGR) of 5% from 2023 to 2027.

This growth has fueled fears that AI could replace software testers, with potential test failures posing risks to business software supply chains.

Tal Barmeir, co-founder of AI-powered software testing firm BlinqIOBelieve that won’t happen. Instead, he sees AI transforming the role of human testers into “AI-assisted testers,” where AI enhances their productivity and efficiency. Bermeir argues that fear of replacing AI ignores the collaborative potential of experimenters to enhance their capabilities rather than replace them.

“While AI integration in software testing is increasing, I think it is unlikely to completely replace human testers. AI-enhanced tools are adept at handling repetitive, data-intensive tasks, which can free up human testers to focus on more complex test scenarios that require critical thinking and decision-making skills,” he told TechNewsWorld.

Addressing AI fears through multichannel strategies

A similar result could be improved human workers to perform their jobs better in call centers, sales conversations and test centers. At that point, Bermeir proposed that upskilling traditional testers to use AI-assisted tools could significantly increase productivity and efficiency.

“By integrating AI tools, testers can automate mundane aspects of the testing process, allowing them to conduct 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 that require deeper insight, such as user experience and security,” he explained.

Barmeir presents a strong case for how using AI in software testing can improve business outcomes.

“Humans will be essential in software testing because they understand context, interpret nuanced user behavior, and make ethical decisions. AI is adept at data analysis and pattern recognition but cannot understand context or make value-driven decisions, areas where human judgment is crucial,” he argued.

In addition, advances such as AI Test Recorder enhance AI testing capabilities by automating the capture and reproduction of test scenarios. However, even as such tools increase the efficiency and scope of testing, there is a need for human supervision.

“Humans play a critical role in monitoring and interpreting results, ensuring test alignment with ethical standards and business objectives, and providing relevant understanding that AI cannot currently achieve on its own,” he added.

For isolated use cases, shared human roles

Does this same logic apply to AI replacing human workers in other use cases? According to Bermeir, the impact of AI on employment varies across sectors. In some cases, such as manufacturing or data entry, AI can 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 capabilities rather than replace them. The key is to use AI as a tool that complements and enhances human skills,” he observes.

Bermeir suggests that companies need to adapt their sometimes misguided assumptions to change the increasing use of anti-AI in customer experience situations. They can do this by focusing on transparency, personalization and control to reduce growing concerns about AI in customer interactions.

“This involves clearly explaining 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,” he said.

The State of Software Testing

According to Bermeir, 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. Another is maintaining a robust framework for security and performance testing.

“This shift toward automation is significantly reducing the time to market (TTM) for new software releases, a critical advantage in today’s fast-paced digital landscape,” he emphasized.

By enabling faster deployment cycles, businesses can respond more quickly to market demand and iterate more effectively. This approach improves the development process and helps companies stay competitive in the digital economy.

Advantages, Disadvantages and Future for AI-Assisted Testers

Automation greatly speeds up the testing process and reduces human error. This allows testers to focus on more complex aspects of the software, increasing testing efficiency and accuracy

AI improves manual testing by handling increased workload or complexity more effectively. This capability makes it easy to expand testing efforts as software complexity and business demands grow.

Disadvantages include the potential for overlooked issues and the need for constant updating to align with new software changes. Bermeir cautioned that this may require an ongoing investment in time and resources.

The future of AI-assisted testers looks promising, with the opportunity to democratize the field and make it accessible at the entry-level. AI tools lower barriers to entry by enabling those with less technical skills to participate in testing processes.

“This democratization means more people can contribute and get involved in software testing, expanding the talent pool and spurring innovation,” he said.

Reshaping the QA industry

Bermeir emphasized that AI will significantly improve the QA industry by automating routine tasks and introducing sophisticated tools such as AI Test Recorder And dramatist. These tools work together to accelerate the testing process while ensuring high quality and reliability in software products.

AI Test Recorder takes a test requirement and efficiently creates a complete test suite in Dramatizer. The platform’s code feature simplifies the automation process by allowing users to create test scripts through recorded interactions with a website, eliminating the need for manual coding.