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Why does manual testing still matter when teams have automated tests? Because automated tests check the expectations a team has encoded, while human-led exploratory testing can investigate unfamiliar behavior, adapt as evidence emerges, and judge whether a feature makes sense for people using it. The strongest strategy uses both: automate stable, repeatable checks and reserve focused manual testing for discovery, context, and judgment.
What manual testing adds that automation does not automatically provide
Manual testing is most valuable when it is an investigation, not simply a slower way to replay a fixed script. In exploratory testing, the tester designs tests, executes them, and evaluates the results together while learning about the software. What the tester sees in one step can shape the next probe.
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That adaptability helps when behavior is new, changing, poorly understood, or sensitive to user intent. A script can verify an encoded expectation; a tester can notice that the expectation itself may be incomplete, ask a new question, and examine whether the result feels coherent in context. Neither approach guarantees that defects will be found, and neither alone proves that users will be satisfied.
ISTQB Foundation Level material on experience-based test techniques describes exploratory testing as designing, executing, and evaluating tests together as the tester learns about the test object.
What should you test manually?
- Unfamiliar or changing behavior: Explore new features and areas where requirements or user behavior are not yet well understood. Follow up on surprising results rather than limiting the session to a predetermined sequence.
- Whether a feature meets user intent: Use requirements and user context to judge whether the workflow solves the real problem, not only whether individual controls respond.
- Critical user journeys: Run automated checks for repeatable assertions in important journeys, then manually examine whether the experience still works naturally from start to finish.
- High-risk or user-sensitive changes: Schedule focused exploratory sessions where a failure would matter and scripted checks do not cover the relevant uncertainty.
- AI-based behavior: Combine technical tests with human-led evaluation. ISTQB identifies probabilistic behavior, non-determinism, and reliance on data as testing considerations for AI-based systems.
For each exploratory session, record what was learned, what was checked, notable results, and risks that remain. This practical record makes discoveries useful to the rest of the team and helps decide which checks should later become automated.
Can automation replace exploratory testing?
Automation can rerun checks consistently and quickly, but it evaluates what the team has chosen to encode. A passing run is evidence that those checks passed under the conditions in which they ran; it is not proof that all relevant behavior is correct, that covered code is bug-free, or that users will find a feature satisfactory. Google’s testing guidance cautions against treating code coverage alone as evidence that covered code has no bugs.
Automation and exploration therefore answer related but different questions. A scripted test asks whether a known expectation still holds. Exploratory testing can investigate what the software does in an area where the team is still learning what to expect. Teams can turn repeatable discoveries into regression checks without assuming that automation eliminates the need to investigate new risks.
Google’s 2008 account of testing Google Talk offers a project-specific example: it describes multiple client implementations, new features, and a test plan that identified where automation applied and what role manual testing should play before release. That is an illustration of planning the mix, not a universal ratio or rule for every product. Google’s account of exploratory testing on Chat provides the project context.
How should manual and automated testing work together?
Make the division of work explicit in the test strategy. Google recommends a layered approach: a solid unit-test base, integration testing, end-to-end tests for critical user journeys, and an understanding of both code and functional coverage. Add human exploration where discovery and judgment matter.
| Testing need | Useful emphasis | Why |
|---|---|---|
| Repeating stable checks after changes | Automation | Known checks can be rerun consistently; unit and integration tests form useful layers. |
| Critical end-to-end journeys | Automated assertions plus human review | Automate repeatable journey checks and manually assess whether the full experience remains natural. |
| Unfamiliar or changing behavior | Manual exploratory testing | The tester can adapt the next probe based on what has been learned. |
| Whether a feature meets user intent | Manual judgment informed by requirements and context | Coverage of scripted checks does not by itself establish that user needs are met. |
| AI-based behavior | A planned combination of human evaluation and technical tests | Probabilistic outcomes, non-determinism, and dependence on data call for attention across the AI system lifecycle. |
- Build the repeatable foundation. Maintain unit tests for small behaviors and integration checks for interactions between components.
- Automate critical journeys. Choose end-to-end checks for important user paths, keeping the assertions focused on stable expectations.
- Plan exploratory sessions. Set aside time for new, high-risk, or user-sensitive behavior. Give the tester a goal or risk area, while leaving room to follow evidence.
- Review coverage in context. Understand what code and functional behavior the checks cover, and identify important risks coverage figures do not answer.
- Feed discoveries back into the suite. When exploration finds a stable, repeatable expectation, consider adding an automated check; preserve manual review where interpretation or new learning remains necessary.
There is no evidence-based universal percentage for the ideal share of manual versus automated testing. The appropriate balance depends on the product, its risks, the stability of its behavior, and what the team needs to learn.
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What changes when the software uses AI?
AI-based systems can behave differently from conventional deterministic software: outputs may be probabilistic, non-deterministic, and dependent on data. This makes it important to consider more than a fixed set of expected outputs. Testing may need to examine input data, models, and the machine-learning development lifecycle as well as the experience users receive.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchISTQB’s CT-AI Version 2.0 covers AI-based systems, including machine learning and generative AI systems such as large language models. Its syllabus includes exploratory testing and red teaming for generative AI and LLMs. These techniques do not make human review a substitute for technical testing; they provide ways to investigate behavior and risks that may not be fully captured by fixed assertions.
Best Value
In its 2026 announcement of the syllabus, ISTQB President and CT-AI Taskforce Chair Klaudia Dussa-Zieger said: “AI-based systems introduce new and complex testing challenges,” and described Version 2.0 as providing a clearer and more practical foundation for testers working with machine learning and generative AI technologies. See ISTQB’s CT-AI certification information and its 2026 CT-AI Version 2.0 announcement for the syllabus scope and release details.
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ScreenshotNeo API documentation has the request details. For example, this cURL call saves a WebP screenshot:
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