The future of manual testing in the age of AI
The future of manual testing in the age of AI is an evolving topic. While AI and automation tools are rapidly transforming the landscape of software testing, manual testing is unlikely to disappear entirely. Here’s how it’s shaping up:
AI-Powered Testing Augments Manual Testing

• AI tools can assist manual testers by identifying high-risk areas that require human intervention, based on patterns they learn from large datasets.

Manual Testing

• While AI excels at automating routine tasks, manual testers will still be needed for tasks that require creativity, intuition, and understanding of user experience.
• Human testers are crucial in understanding how users interact with an app in real-world scenarios, especially for things like usability and accessibility testing. Increased Collaboration Between Humans and Machines
• AI tools will act as assistants, streamlining repetitive tasks and freeing up human testers to focus on the most complex issues. The collaboration could look like a hybrid testing approach, where AI handles repetitive checks, while humans take over tasks that involve critical thinking and complex decision-making. Improved Test Coverage with AI
• AI can help generate better test coverage by analysing past defects, user interactions, and code quality. With its pattern recognition abilities, AI tools can suggest areas that might have been overlooked in manual testing. Shift in Skillsets for Manual Testers
• With AI and automation tools handling more of the basic testing tasks, manual testers will need to evolve their skills. Future manual testers may need to develop an understanding of AI, machine learning, and how these tools work to effectively collaborate with them.
• Manual testers may shift toward roles in test design, test strategy, and test optimisation rather than executing basic tests. AI Challenges
• Despite its promise, AI isn’t perfect for all scenarios. Manual testers may still be needed to fill gaps in areas where AI struggles, such as handling ambiguous or unpredictable behaviour or understanding nuanced design issues that AI might not fully grasp.
• Testers will still be required to verify AI-generated test results, ensuring that the tool's behaviour aligns with real-world conditions. Cost and Efficiency Benefits
• Over time, automation and AI can drive down the cost of manual testing by reducing the number of routine tasks and increasing testing speed. However, this might also mean fewer opportunities for traditional manual testing roles unless testers adapt to the new tools.