Why AI Can’t Replace Exploratory Testing—But Can Make It Better

I wanted to follow up on a previous post pretty quickly. A post about embedding AI in testing where it really will add value. That post can be found here Exploratory testing thrives on human curiosity. Unlike scripted testing, it allows testers to interact with software the way real users do—navigating unpredictable paths, asking “what happens if…?”, and uncovering issues that structured test cases are likely going to miss.

AI has accelerated in many areas of software testing. Some fear it might replace testers altogether. However, exploratory testing highlights a truth that experienced testers already know: human intuition, creativity, and critical thinking are irreplaceable.

Rather than replacing exploratory testers, AI can serve as a powerful assistant, helping testers focus their efforts where they matter most.

Human Insight Still Drives Exploratory Testing

A scripted test might confirm that a feature technically works. But exploratory testers often notice problems that scripts miss.

For example:

• A checkout flow technically works, but users must enter information multiple times.

• A settings page loads successfully, but the layout is confusing and difficult to navigate.

• An onboarding process works on paper, but feels frustrating to complete.

These are issues discovered through human observation and experience, not automated verification.

AI cannot replicate this type of human judgement. However, it can help testers decide where to focus their exploration.

Using AI to Highlight Known Pain Points

Before starting an exploratory session, testers often ask:

“Where should I start exploring?”

AI can analyse historical product data to identify areas where users have struggled.

For example, AI could review:

• Bug reports

• Customer support tickets

• Online reviews

• Incident reports

From this data, it might highlight insights such as:

Example AI Insight:

“42% of support tickets in the last three months relate to password reset failures.”

An exploratory tester might then:

• Attempt password reset across different browsers

• Test expired reset links

• Trigger multiple reset requests

• Attempt resets while logged in on multiple devices

AI identifies the hotspot, but the tester decides how to investigate it.

AI Suggesting Real User Behaviour Paths

Exploratory testers often try to mimic how real users interact with the system. However, assumptions about user behaviour are sometimes incorrect.

AI can analyse product analytics to reveal how users actually navigate the system.

Example insight:

“Most users navigate: Dashboard → Reports → Export → Filter by Date → Download CSV.”

An exploratory tester might then try variations such as:

• Exporting large datasets

• Changing filters repeatedly

• Switching between report types mid-download

• Exporting while another export is already running

Another AI insight might highlight a potential usability issue:

“65% of users abandon onboarding at Step 3.”

This could prompt the tester to explore:

• What happens if Step 3 is skipped

• What happens with incomplete data

• What happens when navigating backwards

AI provides behavioural clues, but the tester explores the edge cases.

Identifying High-Risk Areas Based on Development Activity

AI can also analyse development data to identify risky areas of the application.

Sources could include:

• Recent code commits

• Pull request changes

• Bug history

• Code complexity metrics

Example AI insight:

“The payment processing module was modified in the last release and historically contains the highest number of defects.”

An exploratory tester might respond by testing scenarios such as:

• Partial payments

• Payment retries after failure

• Multiple tabs are completing checkout simultaneously

• Payment interruption during network loss

Instead of randomly exploring, testers gain context about system risk.

AI Supporting Exploratory Sessions in Real Time

Exploratory sessions can sometimes be difficult to document, especially when testers are rapidly navigating through the system.

AI could assist by automatically capturing session activity.

Example support:

While a tester explores an application, AI might:

• Record navigation paths

• Capture screenshots when errors occur

• Log unusual system responses

• Suggest related workflows

Example prompt during testing:

“You have tested checkout with a credit card. Would you like to try PayPal or Apple Pay next?”

Another example:

If an API response suddenly returns a different structure, AI could flag:

“Response format changed compared to the previous session.”

This allows testers to remain focused on exploration rather than documentation.

AI as a Source of Exploratory Prompts

Even experienced testers sometimes reach a point where they feel they've exhausted obvious paths.

AI can serve as a brainstorming assistant by suggesting new angles for exploration.

Example prompts:

• “What happens if the user changes their timezone during checkout?”

• “What happens if the browser tab is refreshed during form submission?”

• “Try completing onboarding with only keyboard navigation.”

• “What happens if the session expires mid-transaction?”

These suggestions are not scripts—they are exploration triggers.

The tester remains free to pursue or ignore them.

The Right Balance: AI Guides, Humans Discover

Exploratory testing succeeds because it embraces unpredictability.

The risk with AI is turning testing back into structured automation disguised as intelligence.

The real opportunity lies in using AI to:

• surface insights

• highlight risk

• reveal real user behaviour

• reduce administrative effort

But the actual discovery process remains human.

AI might tell you where problems are likely to exist.

Only a human tester can notice:

• confusing UX

• subtle friction

• inconsistent behaviour

• features that simply feel wrong

Tools Already Moving in This Direction

Several modern testing tools already show how AI can support exploratory testing rather than replace it.

• Mabl – analyses user journeys and highlights important paths to explore

• Applitools – detects visual issues that may deserve deeper investigation

• Testim – surfaces unstable workflows and risky areas of the application

Used correctly, these tools don’t dictate what testers should do—they simply provide signals that help testers decide where to explore next.

Summary

Exploratory testing has always been about curiosity, creativity, and human judgement.

AI will never replace that mindset.

What it can do is make exploratory testers more informed and more effective.

Instead of replacing testers, AI becomes a co-pilot:

• pointing out potential problem areas

• highlighting real user behaviour

• suggesting new paths to explore

The future of testing is not AI vs humans. It’s AI supporting humans in discovering better software.