Using AI to Enhance Exploratory Testing

The use of exploratory testing has always been appreciated for its flexibility and creativity. It allows testers to think freely, react to what they see, and discover problems that are easy to miss with scripted tests. However, as products grow more complex and release cycles become faster, testers are often expected to explore more areas of the system in less time.

This is where AI starts to appear in QA discussions. While AI is sometimes seen as a way to “replace” human work, this mindset does not fit exploratory testing. Exploratory testing relies heavily on human intuition, product understanding, and user empathy. Those things AI simply does not have.

The purpose of this blog is to show how AI can be used as a helper or assistant during exploratory testing, not as a replacement for the tester. AI should never make decisions on its own or define what “good quality” means for a product. When used correctly, AI can help expand test ideas and reduce blind spots, while the tester remains fully in control of the exploration process.

Where exploratory testing struggles today

Time pressure and limited exploration windows

Exploratory testing can be very time consuming. It requires time to observe the system, follow ideas, try unusual paths, and investigate anything that feels suspicious. When deadlines are short, this depth is often not possible, and important issues can easily be missed.

In many teams, exploratory testing is performed under constant pressure. Release dates, sprint deadlines, and last minute changes often leave testers with only a limited window for exploration. As a result, testers may focus on a smaller part of the product and unintentionally skip areas that deserve attention.

Limited time does not only reduce test coverage, it also affects the tester. Working under pressure can make even experienced testers more prone to mistakes, tunnel vision, or overlooking subtle problems. Instead of freely exploring, testers may rush through test scenarios just to “check” as much as possible within the available time.

Repeating familiar testing paths

Over time, testers naturally develop habits on how they approach exploratory testing. These habits are built from experience, past bugs, and areas of the product that have historically caused problems. While this knowledge is valuable, it can also lead to repeating the same testing paths again and again.

When a tester is familiar with a product, exploratory sessions often start in the same areas and follow similar flows. The tester already knows what usually breaks, which features are risky, and which inputs are likely to cause issues. As a result, exploration can become predictable, even if it is not strictly scripted.

This familiarity can make it harder to come up with new unusual testing ideas. Nice paths, uncommon user behaviors, or unexpected feature combinations may be overlooked simply because they are not part of the tester’s typical thinking process of the system. Over time, this increases the risk that certain types of issues remain undiscovered.

Missing edge cases and uncommon scenarios

Edge cases and uncommon scenarios are often where the most serious bugs hide, but they are also the easiest to miss during exploratory testing. These cases usually involve unusual inputs, rarer user behaviors, or unexpected combinations of features. Specifically, situations that do not occur often in everyday use.

During exploratory testing, testers tend to focus on realistic and commonly used flows, especially when time is limited. This approach makes sense, as it reflects how most users interact with the product. However, it also means that less obvious scenarios may not receive enough attention, even though they can lead to crashes, data corruption, or security issues.

Uncommon scenarios are particularly challenging because they require testers to think beyond normal usage patterns. It is not always easy to imagine how users might misuse a feature, interact with it in unintended ways, or combine it with other parts of the system. Without explicit prompts or extra time, these ideas can remain unexplored.

Our own assumptions limit exploration

Exploratory testing is strongly influenced by how testers think about a product and its users. For example, a tester working on a registration flow may assume that users will always complete the process in a logical order. During exploratory testing, they might focus on validating correct input, error messages, and successful submissions. However, this assumption can lead the tester to overlook behaviors such as users navigating backward, refreshing the page mid-process, or interacting with the form in an unexpected sequence.

Assumptions often become stronger in stable or long-running projects. When a feature has behaved a certain way for a long time, testers may subconsciously trust it and spend less time questioning its behavior. Even small changes can go unnoticed simply because the tester does not expect them to exist.

Dealing with constant product changes during testing

Exploratory testing works best when testers have time to understand and adapt to changes. Products rarely stay the same for long. Features evolve, requirements change, and fixes are added right up until release. For testers, this means exploratory testing often happens while the product is still moving.

When changes happen frequently, it becomes harder to know what exactly should be explored. Testers may need to re-learn parts of the product, adjust their focus, or repeat exploration in areas that were already tested earlier. With limited time, this can lead to shallow exploration or skipping certain areas altogether.

Constant changes also increase mental load. Testers need to keep track of what has changed, what was already tested, and what might be affected indirectly. This makes it easier to miss important behaviors, especially when changes are not fully documented and communicated.

AI as a helper during exploratory testing sessions

Expanding test ideas beyond tester’s initial scope

AI can be useful during exploratory testing by helping generate additional test ideas once initial exploration has already been done. After a tester shares what has been tested so far, AI can suggest other areas, flows, or interactions that might be worth exploring next.

One of AI’s strengths is its ability to quickly produce multiple alternative ideas. It can suggest different ways a feature could be used, combined with other features, or interacted with under different conditions. Some of these ideas may not be relevant, but others can highlight parts the tester did not initially consider.

This approach works best when AI suggestions are treated as input, not instructions. The tester reviews the ideas, filters out what does not make sense, and decides which suggestions are worth exploring further. In this role, AI acts as a brainstorming partner that helps widen the exploration space. Used this way, AI helps reduce the blind spots by offering fresh perspectives, while the tester remains fully responsible for choosing what to test and how deeply to explore it.

Exploring alternative user behaviors and flows

AI can also help exploratory testing by suggesting alternative ways users might move through a product. These suggestions can include different navigation paths, unexpected order of actions, or interactions that are not part of the main user flow. By looking at a feature from multiple angles, AI can propose behaviors that are easy to overlook during testing. For example, it may suggest switching between features mid-action, skipping steps, or interacting with the system in a non-linear way.

However, not every suggested behavior will be realistic or useful. This is why AI should only act as a source of ideas, not as a guide. The tester decides which flows make sense based on the product, its users, and the current testing goals. When used carefully, AI helps testers step outside familiar usage patterns and explore how the system behaves under different conditions, while still keeping human judgement.

Breaking out of repetitive thinking

AI can be helpful in breaking repetitive thinking during exploratory testing. When testers work on the same product for a long time, it is easy to fall into familiar patterns and test similar things in similar ways during each session.

By reviewing what has already been tested, AI can suggest different angles or approaches that challenge these patterns. This might include testing the same features with a different mindset, focusing on unusual interactions, or exploring areas that are usually considered low risk.

Even small changes in focus can help refresh exploration and lead to new observations. AI acts as a prompt that encourages testers to rethink, and look at the product from another perspective.

Supporting exploration in complex and unfamiliar domains

AI can be especially useful when testers are exploring complex systems or domains they are not very familiar with. This could include products with technical workflows, industry specific rules, or systems with many integrations and dependencies. In these situations, it is not always clear where to start or what to focus on first.

For example, a tester working on a financial or analytics related feature may not fully understand all possible user actions or data relationships. After initial exploration, the tester can use AI to ask for ideas about typical workflows, common failure points, or areas that are often problematic in similar systems. This can help guide exploration toward areas that deserve closer attention.

AI does not replace domain knowledge or learning. Instead, it provides general guidance and prompts that help testers explore more confidently when working in unfamiliar environments. The tester still needs to validate whether the suggested ideas make sense for a specific product.

Example workflow using AI in exploratory testing

Imagine a QA engineer exploring a new feature in an e-commerce app.

Tester first

The tester begins by performing their own exploratory tests. They try adding items to the cart, applying discount codes, removing items, and completing the checkout with different payment methods. During this phase, the tester notes what they tested and observes any unexpected behavior.

AI second

After the initial exploration, the tester provides the AI with a summary of the steps they have taken. They then ask the AI for additional test ideas, such as alternative flows, edge cases, or scenarios they might have overlooked. The AI could suggest things like:

1. Attempting checkout with expired or invalid discount codes
2. Applying multiple discount codes in sequence
3. Changing delivery options midway through the checkout
4. Ordering large quantities of items that normally have purchase limits

Tester reviews and chooses

The tester reviews AI suggestions, filtering out irrelevant or impossible scenarios and selecting the ones that make sense for the product.

What AI cannot do in exploratory testing

Understand the full scope of product intent and business goals

AI can generate many test ideas, but it does not understand the purpose of the product or the business goals behind it. It can suggest scenarios based on patterns or input data, but it cannot judge whether a feature is fulfilling its intended purpose, or whether a behavior aligns with business objectives.

For example, AI might suggest testing a feature in ways that are technically possible but make no sense for real users, like attempting to use an admin only option in a customer facing workflow. While these suggestions can sometimes uncover unexpected issues, they often miss the bigger picture: whether the product behaves in a way that meets user needs or business expectations.

AI can expand exploration and generate ideas, but it cannot replace the tester’s understanding of why a feature exists, what success looks like, or which areas are most critical to the business. Human judgement is still essential to ensure testing aligns with the product’s purpose.

Judge the user experience as a human would

AI cannot evaluate the quality of the user experience in the way a human QA engineer can. It cannot tell if a feature feels intuitive, confusing, or frustrating to actual users. AI could suggest testing a multi-step form with different input combinations, but it cannot determine if the form is too long, visually cluttered, or hard to navigate. Subtle UX design issues, like awkward button placement, unclear instructions, or layouts that feel awkward, are easily missed by AI. These aspects rely on human perception, empathy, and experience with real users.

Replicate user empathy or contextual awareness

AI can generate test scenarios, but it does not understand how real users think, feel, or behave in context. It cannot anticipate motivations, frustrations, or the ways people adapt to unusual situations. Those things often reveal important issues during exploratory testing.

AI might suggest testing a feature with technically valid inputs, but it cannot predict how a first time user might misinterpret instructions. Similarly, it cannot consider the context of the user's environment, like testing a mobile app in low connectivity conditions, or understanding how users with disabilities might use the feature differently.

Risks and considerations when using AI

Sharing sensitive or confidential information with AI tools

One of the biggest risks when using AI tools, especially public AI services, is accidentally exposing sensitive or confidential data. AI models often process and retain the text users submit, and depending on the service and settings, that data may be stored or used to improve the underlying model. This means anything you share could potentially live outside your control, even if you delete it from your own chat history later.

In May 2023, Samsung banned its employees from using ChatGPT and other generative AI tools after the company discovered that some engineers had accidentally uploaded sensitive internal source code to ChatGPT. The leak occurred when employees used the AI service to help with coding tasks, without realizing they were sharing internal code and related information with an external AI platform. Because ChatGPT can store and use user input to improve its models, Samsung was concerned that this information could be stored on servers outside its control and possibly become accessible to others. As a result, the company issued a memo restricting the use of generative AI tools on company-owned devices and internal networks. 

In the context of exploratory testing, this risk often arises when testers paste internal documentation, customer data, API keys, or error logs into an AI prompt without realizing the sensitivity of the information. Even examples meant to illustrate a problem—like a snippet of configuration or a user session ID—can become problematic if they contain personal data.

To reduce this risk, organizations and software testers should:

• Avoid submitting any real confidential data to public AI tools
• Use internal, controller AI services with proper privacy guarantees when handling company data
• Follow clear policies and training on what is safe to share with AI tools

Over-reliance on AI suggestions

Another risk when using AI in exploratory testing is starting to trust it too much. AI can generate a long list of test ideas very quickly. That speed can feel impressive and sometimes convincing. But just because a suggestion sounds reasonable does not mean it is useful, correct, or relevant to your product.

Blindly following AI suggestions goes directly against the purpose of exploratory testing. Exploratory testing relies on human judgement, curiosity, and the ability to adapt based on what is observed during testing. AI does not understand which areas of the product are most risky, which features are business-critical, or which behaviors matter most in real users. QA engineers should actively review, adjust, and sometimes completely ignore AI suggestions based on their own product knowledge and testing goals.

False confidence from AI-generated ideas

AI can make exploratory testing look more complete than it actually is. When AI is used during testing, it often produces structured and well worded ideas. This can create the impression that testing has been thorough, even if important areas were barely explored. The risk here is not blindly following AI, it’s assuming coverage that does not really exist.

For example, AI might suggest many variations around one feature, while completely ignoring another area that is more critical or recently changed. Because the output looks detailed and organized, it is easy to miss the fact that some risks were never touched at all.

The false sense of coverage can be especially dangerous near release time. Teams may feel confident moving forward because “AI helped with exploration”, even though the exploration itself was shallow or unbalanced. In reality, AI has no awareness of testing depth, risk exposure, or real product history.

To avoid this, QA should not measure exploration success by how many ideas were generated, but by what was actually learned during testing:

• Did testing reveal new risks?
• Were unclear behaviors clarified?
• Were the assumptions challenged?

AI can help generate ideas, but confidence in product quality should come from insight and understanding, not from the presence of AI generated output.

Final thoughts

AI can be a powerful addition to exploratory testing when used in the right way. It can help QA expand test ideas, challenge assumptions, and uncover scenarios that might otherwise be overlooked, especially when time is limited and products are complex.

At the same time, AI has clear limitations. It does not understand product intent, user expectations. It can suggest irrelevant ideas, miss critical risks, or create a false sense of coverage if used without care. Because of this, AI should support exploratory testing, not control it.

The most effective approach is a balanced one: testers lead the exploration, make decisions, and interpret results, while AI acts as a helper that broadens thinking and reduces blind spots. When QA remains in control and is aware of the risks, AI becomes a valuable tool, not a replacement for human insight.