How will automation and manual testing impact QA in 2025?

Business leaders agree that automation of repetitive tasks within their organizations is inevitable and AI will only speed up the process.

By 2026, 30% of enterprises are expected to automate over half of their network activities, a significant increase from less than 10% in mid-2023, according to Gartner. The overall tendency to automate is especially true in the world of quality assurance (QA) – but should manual testing be ignored completely? Read on to find out the roles manual and automation testing will play in 2025 and beyond.

Automation testing is on the rise

The global automation testing market was valued at USD 32.70 billion in 2024, and it is expected to reach USD 61.27 billion by 2033, exhibiting a CAGR of 15.68% from 2025 to 2033, as stated by Imarc Group. Automation is becoming so prevalent within software development that it is used in different types of testing: unit testing, smoke tests, integration, regression, API, security, performance, acceptance and UI tests.

Automation testing does not aim to replace people with technology. Quite the opposite, since it requires investing in skilled professionals to choose tools, configure them, write automation tests, monitor the running of tests and then analyze the results. Of course, detecting bugs, diagnosing issues and fixing problems are also essential tasks of an automation testing engineer. Investing in personnel that can effectively carry out these duties is a significant cost, but while initially this cost surpasses the cost associated with manual testers, in the long run, it will deliver a better return on investment (ROI). Furthermore, the institutional knowledge of an organization should increase as more experienced automation testers mentor, train and support more junior testers in automation testing practices.

This ROI, aside from dollars and cents, can be measured in the effectiveness of QA testing and the satisfaction of test engineers. Test automation ensures faster delivery and eliminates the long delays between developing a code, detecting a defect and fixing it. Fixing defects is one of the biggest costs in software development, as it includes not just the time and effort of engineers, but also the cost of missed opportunity by not launching software to the market fast enough. In increasingly competitive industries, reducing the time it takes to release software and launch products on the market is crucial.

Along with speed, automation testing makes it easier to find more bugs during regressions. Since automated testing relies on tools and scripts, it is not susceptible to testing fatigue and unable to commit human errors. Automation testing delivers other benefits, from guaranteeing that checks are always performed the same way, to running tests more frequently at lower costs and easily verifying if a drop in quality has occurred throughout the process. But just using the term ‘automation’ does not mean there are no human elements involved. Tests still need to be monitored and technology has limitations in this regard. Additionally, tests can sometimes be unstable or there could be synchronization issues, so augmenting automation testing with expert specialists is a cost companies can’t ignore. Nonetheless, the clear advantages of automation outweigh the drawbacks.

There’s still a place for manual testing in QA

However, there is still a need for manual testing, since not everything can be automated (yet). Moreover, while it is true that automation testing is faster, this applies to situations where cases are repeated, meaning that the same code and scripts are applied every time. But there could be a situation when this is not the case. Bear in mind that automation testing requires establishing test cases, programming them with selected automation tools and running tests – which can be time-consuming. This investment pays dividends when it can be re-used, but some projects might not require this repetition. Manual testing empowers testers with more flexibility, making it easier to quickly test through manual operations, get results and adjust the process. Automation testing does not allow for random testing, while exploratory testing is a big part of manual testing.

Additionally, automation testing, rightly considered an integral aspect of an organization’s digital acceleration, is a sizable investment that requires well thought out planning, making it a better fit for long-term projects. But for short-term projects, automation might not make sense as the upfront costs of automation would be too high to produce a return on value. Unlike automation testing, manual testing does not include the cost of setting up tools, maintenance and management, making at cheaper in the short-term.

Lastly, manual testing is essential when considering the usability of an application. As cutting-edge as technology can be, it does not (currently) have the ability to replicate human feelings or thoughts. A properly written automation test will catch all defects and identify all bugs in a code, but it will not be able to recognize how users interact with features and functionalities. Despite the wonders of modern technology, certain projects still need a manual tester’s eye, especially in terms of UI. Is the experience intuitive? Is the user journey rewarding? These are questions best answered through exploratory trial and error that manual testing facilitates.

How AI is changing QA automation and testing

Artificial intelligence is transforming software testing and quality assurance, providing powerful tools that enhance efficiency and accuracy across all types of testing. Software Mind’s experts covered those subjects extensively in the article Boosting Software Testing With AI. Here are the highlights.

AI Tools for manual testers

Manual testers can leverage AI to streamline their workflows and gain deeper insights without needing to write code.

• AI-powered test automation platforms: Tools like Mabl, Testim, and SmartBear enable codeless test creation and execution. Their core AI technology detects changes in an application and automatically updates test scripts, which significantly reduces maintenance time. This allows testers to focus on more complex testing challenges instead of fixing outdated tests. Using these platforms also helps manual testers learn fundamental automation principles, such as creating reusable test loops and designing parallel tests.
• AI for log and data analysis: AI excels at rapidly processing huge datasets. Tools such as Loggly, Sentry, and Datadog automatically analyze system logs to find anomalies and generate reports. This helps testers identify and respond to potential problems far more quickly than through manual investigation.

AI tools for all testers

Some AI tools are universally beneficial, enhancing various stages of the testing process for both manual and automation specialists.

• Large Language Models (LLMs): Platforms like OpenAI’s GPT, Google’s Gemini, and Claude act as versatile assistants. They can be used to generate test scenarios, create test data, draft documentation, and analyze error reports. For testers working with code, they can answer programming questions, review code, and even optimize algorithms.
• AI-powered visual testing tools: For applications where the user interface (UI) is critical, tools like Applitools and Percy are invaluable. They use AI to automatically compare UI elements and screenshots to find visual discrepancies. The AI can intelligently classify these changes as bugs or acceptable updates, ensuring a consistent user experience.

AI tools for automation testers

For those focused on test automation, AI offers specialized tools that accelerate the entire process from script creation to analysis.

• AI Code generators: Tools like GitHub Copilot and Codeium can generate test script code from natural language descriptions or comments. By analyzing the existing codebase, they can also suggest the next logical steps in a test, which saves considerable time and effort in manual coding across multiple programming languages.
• Automated code quality and security analysis: AI-driven tools like SonarQube, Checkmarx, and Veracode enhance static code analysis by identifying potential vulnerabilities and bugs early on. They automatically scan code and suggest fixes, streamlining the review process. Integrating these tools into a CI/CD pipeline helps catch issues before they reach production.QA specialists and test engineers look to automation for opportunities

Obviously certain tools are suited for certain projects, that’s why experienced software engineers understand that automated testing works best for regression and performance testing, along with any projects that require highly repetitive functional tests. For short-term projects, or any exploratory, usability or ad Hoc testing, manual testing is more effective.

But as digital transformation efforts continue unabated, along with the adoption of Agile methodology and DevOps, the demands on QA engineers to increase the speed of testing and optimize testing strategies have never been greater.

This pressure is counter-balanced with the exciting possibilities that automation testing offers. Given its increasingly important role, the investment, research and development of new tools is immense.

T Most teams (65%) use package-specific tools for automation, 63% use a pre-built test case repository for certifying a sprint, and 61% have testing integrated as automatic quality gates in the CI/CD pipeline, according to the World Quality Report by Capgemini. QA testers are drawn to automation testing because that’s what companies want, whether it’s because it provides increased productivity, accurate testing, automated support or increased testing coverage.

As a result, the field of automation testing is constantly evolving with new technology and tools. Take for example the number of automation frameworks. The most common are linear (otherwise referred to as record and playback), modular-based (very efficient but requires detailed planning) and library architecture (like modular-based, but with more flexibility). Having these different options diversifies the ways of working within automation testing. The range of tools is even wider, which poses risks and rewards, as tools need to be selected carefully.

Automation testing – frameworks and solutions

The market has moved beyond simply automating tests to creating intelligent, self-healing, and highly efficient testing ecosystems. Teams are leveraging a sophisticated mix of established leaders and innovative new players to ensure quality at an unprecedented pace.

Here are some popular frameworks and current solutions that are shaping the industry:

Crucial testing frameworks

The primary battle for end-to-end testing dominance continues between Playwright, Cypress, and Selenium, each having carved out a distinct and evolved niche. 

• Playwright (v1.50+): Microsoft’s framework has become a market favorite for its exceptional speed, reliability, and cross-browser capabilities (including mobile emulation). Its Trace Viewer remains a great debugging tool. The 2025 versions feature even more powerful AI-driven test generation and advanced network interception capabilities, making it the top choice for complex, dynamic web applications.
• Cypress (v16.x): Cypress continues to lead in developer experience. Its all-in-one architecture, interactive test runner, and exceptional documentation make it incredibly popular, especially for front-end developers. Recent versions have perfected its component testing offering, seamlessly blending it with E2E tests. Its cloud service, Cypress Cloud, now offers advanced analytics and predictive flaky test detection.
• Selenium (v5.x): The long-standing industry standard has fully modernized. With Selenium 5, the cumbersome setup of the past is gone. WebDriver BiDi (Bi-Directional) protocol support is now mature and universally adopted, allowing for powerful features like intercepting network requests, listening to console logs, and handling authentications natively, closing the gap with its modern competitors.

Solutions in automation testing 2025

• AI-native testing platforms: A new category of tools has gained significant traction by building AI at their core. Platforms like Reflect, Testim, and Functionize are no longer niche players. They use machine learning for test creation, intelligent element locators that self-heal when the UI changes, and automated root cause analysis. These platforms excel at reducing the immense maintenance burden associated with brittle test suites, making them highly attractive for large-scale enterprise applications.
• Integrated performance and API testing: The “shift-left” movement has led to the rise of developer-centric performance testing tools that integrate directly into the CI/CD pipeline. While Postman and Insomnia remain popular for manual API exploration, frameworks like k6 (by Grafana) and Gatling are now standard for writing performance tests as code. Playwright and Cypress have also expanded their native API testing capabilities, allowing teams to combine UI and API tests within a single framework effortlessly.

QA automation and manual testing – summary

Regardless of your goals, any automation strategies, or broader digital accelerations, require a tailor-made approach which reflects your organization’s visions and understands the market you operate in. Finding experienced, trusted support is essential – that’s why companies around the world are turning to Software Mind for the nearshore talents and dedicated engineering teams that ramp up software delivery, deliver disruptive solutions and drive revenue streams.

Additional questions

What are automation testing and manual testing?

Manual testing is a hands-on process where a human tester interacts with a software application, simulating end-user behavior to find defects. This method relies on the tester’s observation, skill, and intuition to explore the application, check its functionality, and assess its usability. It is particularly effective for exploratory testing, usability checks, and scenarios requiring human judgment. In contrast, automation testing uses specialized software tools and scripts to execute predefined test cases automatically. This approach compares the application’s actual outcomes against expected results without direct human intervention. Automation is ideal for repetitive, time-consuming tasks like regression testing, load testing, and performance checks. While manual testing offers flexibility and a nuanced user perspective, automation provides speed, efficiency, and the ability to run many tests repeatedly, ensuring consistency and broader test coverage. A comprehensive quality assurance strategy typically combines both methods for the best results.

Can a manual tester transfer to doing automation testing?

A manual tester can transition to automation testing as a path in the Quality Assurance career path. Manual testers possess a critical foundation that new developers might lack: a deep, user-centric understanding of the application, an intuition for identifying edge cases, and expertise in designing practical test scenarios.

The primary step in this evolution is acquiring technical skills. This involves learning a programming language, with Python and JavaScript being the most common choices, and mastering a specific automation framework like Playwright, Cypress, or Selenium. Building upon their domain knowledge and dedicating time to learning coding fundamentals, a manual tester is positioned to become a highly effective automation engineer. They can write robust and meaningful automated tests that go beyond simple checks to validate application quality from a user’s perspective truly.

What is the difference between manual testing and QA testing?

The key difference between manual testing and quality assurance (QA) is scope and purpose. They are not opposing methods but rather a part-to-whole relationship. QA is a broad, proactive process encompassing the entire software development lifecycle. Its primary goal is to prevent defects by defining standards, improving development processes, and ensuring quality is built into the product from the beginning. It’s a strategic approach to quality management. Manual testing is a specific and proactive activity within the broader QA framework. Its primary focus is identifying existing defects by having a human tester interact with the software and simulate user actions. This hands-on approach is just one of many practices that fall under the extensive umbrella of QA, including automation testing, code reviews, and process audits. QA represents the overall strategy, while manual testing serves as a specific tactic within that strategy.