Enhops Experts Reveal How AI-Driven Testing Still Depends on Human Judgment

AI-based tools have significantly reshaped the testing landscape. With GenAI now embedded into modern testing platforms, teams can auto-generate tests, self-heal scripts, analyze failures intelligently, and accelerate feedback across CI/CD pipelines

Having worked hands-on with AI-assisted testing in real enterprise environments, we’ve seen how AI-based tools dramatically improve speed and efficiency.

However, AI fundamentally operates on patterns and probabilities. It does not understand business intent, user expectations, or risk impact. That gap becomes visible the moment teams move from “can it be tested” to “should it be trusted.”

So, one reality remains unchanged: Even the most advanced AI-driven testing still depends heavily on human judgment to ensure true software quality.

Here’s why.

AI-Based Tools Can Generate Tests while Humans Decide What Matters

One of the biggest advantages of AI-based tools is AI-assisted test creation:

• Automatic journey capture
• Suggested assertions
• Rapid expansion of test coverage

This capability is impressive, but it raises an important concern: Does more coverage always mean better testing?

Our team’s experience says –

When using AI-generated test journeys for a complex business application:

• The tool creates extensive UI coverage very quickly
• Many tests validate flows that are technically correct but low in business value

It requires team’s judgement to:

• Identify revenue-critical and risk-heavy paths
• Eliminate redundant or low-impact scenarios
• Align automation with real business priorities

AI-based tools generate tests for us; however, real human experts are needed to determine which tests are worth maintaining.

Self-Healing Automation Still Needs Human Oversight

Self-healing automation is a standout feature in many AI-based testing tools. They automatically adapt to

DOM changes, Locator updates & Minor UI restructuring. This significantly reduces test maintenance, but it also introduces risk if used blindly.

While dealing with a few AI-based automation tools what we realized was this –

In one scenario a locator changed, the AI-based tool healed the test automatically and then the test passed without any failure. However, the functional behavior behind that element had changed. The action now triggered a different backend workflow

The AI healed the test. A human validated business behavior.

AI Can Detect Changes while Humans Assess Business Risk

AI-based tools excel at detecting visual differences, flagging anomalies, grouping failures across executions

But not every detected change is a real problem.

While the team was creating regression tests, AI highlighted layout shifts and minor visual differences which appeared alarming from a pure signal perspective meanwhile, human exploratory testing uncovered a broken edge-case flow impacting a specific customer segment.

AI identified what changed. Humans decided what truly mattered. This distinction is critical in fast-paced delivery cycles.

AI Learns from Patterns and Humans Anticipate Real User Behavior

GenAI models rely heavily on historical data and observed behavior. Real users, however, rarely follow ideal paths.

AI-generated tests assume test data to be clean, journeys are linear, and network conditions are stable. But if you look at it through an experienced tester’s prism one may ask –

• What happens if the user abandons mid-flow?
• What if data is partially saved?
• What if the system responds slowly but does not fail?

These scenarios come from experience, domain understanding, and user empathy and from not training datasets.

AI Assists Failure Analysis and We Find Root Cause

AI-based tools now provide Intelligent failure clustering, Screenshot comparisons and logs and insights to reduce triage time. This is extremely valuable, but root cause analysis still requires human reasoning.

With one of our clients, CI runs showed repeated failures and AI insights suggested general instability and one of our SDETs identified the actual issue which had to do with test data collisions caused by parallel execution

The solution was not test-related, it required changes in data strategy, execution design and of course framework architecture

AI surfaced patterns; however, our team solved the real problem.

AI Does Not Own Quality, Humans Do

When defects escape production, AI does not explain why a risk was accepted. Tools do not justify release decisions and dashboards do not answer “why this was missed”

People do.

Every release decision ultimately involves – risk evaluation, business trade-offs and confidence in user impact

These are human responsibilities, not algorithmic outputs.

The key is to strike a right balance where AI serves as an accelerator and humans as inevitable decision makers

Used correctly, AI-based tools will reduce repetitive effort, improve execution speed, lower maintenance overhead and scale automation efficiently

But the most successful teams ensure a perfect blend where –

• Humans define the testing strategy
• AI executes and optimizes
• Humans interpret outcomes
• Humans own quality decisions

AI makes testing faster. Human judgment makes testing meaningful. Because in the end AI can test software but only humans can understand quality.

How Enhops Can Help

Enhops helps enterprises adopt AI-driven testing in a way that strengthens human judgment rather than replacing it. Our AI-driven Quality Engineering approach combines intelligent automation with experienced QA leaders who understand business context, risk, and real user behavior.

With Enhops, teams can:

• Standardize their testing processes even before thinking about AI
• Apply AI for test generation, self-healing, and failure analysis without losing control over quality decisions
• Design automation strategies that prioritize business-critical and high-risk workflows
• Validate AI-driven outcomes through human-led exploratory testing and domain expertise
• Test GenAI and AI-powered applications for accuracy, reliability, and behavior consistency, not just functional correctness

Our approach ensures AI accelerates execution while human judgment guides strategy, interpretation, and release confidence—so teams deliver software that works for real users in real-world conditions.