The big problem with manual testing (and how AI can fix it)

In April 2018, TSB Bank was trying to migrate about 1 billion customer records to a new platform. 

But “something” went wrong, and 5.2 million users were kicked out of their accounts, exposing personal data and leading to fraudulent transactions. Lack of rigorous testing and oversight resulted in a £48.65 million fine.

Had TSB used AI-powered testing tools, many of these issues could have been identified and resolved BEFORE the migration. These tools can simulate complex user interactions, detect anomalies, and ensure system stability under various scenarios.

Let’s explore the problems with manual testing, the benefits of AI in software testing, and illustrate how AI can enhance software quality assurance without replacing human expertise.

The big problem with manual testing

It’s too slow for agile development

Agile = fast feedback, tight loops, and frequent releases. Now, look at classic manual testing. Provided several deployments a week (or daily), waiting two to three days for regression testing is a blocker:

• Feedback is toooo sloooow.
• Teams skip or rush tests.
• Bugs sneak through and trigger chaos.

A typical regression cycle includes 400-800 manual test cases. Let’s say a tester spends 10 minutes per case; that’s 65-130 hours per cycle. In a two-week sprint, that eats up nearly all of QA’s time. When pressure mounts up, teams give up test depth or coverage, and eventually, everything breaks.

Prone to human error

We are all humans; we tend to miss things from time to time due to cognitive overload. When testers run repetitive test cases for hours, mental fatigue is inevitable. Unconsciously, they are missing edge cases. Minor bugs slip past. Assumptions go unchecked.

Working memory has a limit, and testers who try to manage UI logic, backend dependencies, and business rules simultaneously will make mistakes.

Impact: Missed bugs in core workflows (payment processing, login flows, etc.) create downtime and cause customer churn.

Limited scalability

Manual testing doesn’t scale with the product. Sooner or later, the application will grow; you add new features, you multiply combinations, user paths, and environments. Covering all of that manually means adding more testers, more hours, and more budget.

Performance testing also needs more testers to simulate real-world usage.

Compatibility testing across browsers and devices? You got the message.

Eventually, teams either skip tests or postpone releases. Neither is a great option when your competitors are pushing updates weekly.

High costs

Comparing AI vs. manual testing, the latter requires more resources:

• Min 3 QA engineers
• 40 hours/week each
• USD 45/hour average loaded rate

That’s USD 5,400 per week or USD 21,600 per month for one product. Add more environments, parallel dev streams, or internationalization, and costs will increase.

Over a year, this cost can cross USD 250,000 for a team doing largely repetitive work. At scale, this model just doesn’t hold.

Inconsistent results

The traditional approach depends on individual testers’ styles, tools, and documentation habits. Even within the same team, two testers might interpret the same case differently. One logs it as a bug, while another flags it as “expected behavior”.

Eventually, you get:

• Fragmented test coverage.
• Unreliable results.
• Missed edge cases due to inconsistency.

Worse, teams lose time moderating reports instead of fixing real issues.

How AI can fix manual testing challenges

Speeding up the testing process

Among other AI-powered software testing benefits, speed stands out. The next-gen testing tools take over repetitive workloads (regression, exploratory, smoke, performance testing) and execute them with every code change. Without waiting and wasting time, without a queue. 

Benefit

Devs receive almost instant feedback. They push code in the morning and have actionable test results before lunch.

Example

Given: A QA team running 1,000 regression cases manually. Usually, they need 5 testers and 3 full days.

Better solution: OwlityAI runs the same volume in under 3 hours via parallel cloud threads. That’s the difference between reacting late and fixing fast.

Machine learning algorithms improve accuracy

AI runs tests and learns with every cycle. AI recognizes patterns and scans historical data to identify what’s likely to fail, where the app behaves atypically, and which parts of the UI change most frequently. Autonomous QA tools use computer vision to scan UIs, track DOM mutations (all elements on the page in a tree-like form), and compare baseline behaviors over time.

Benefit

You no longer depend on unstable human precision. The system is more likely to catch defects that manual testers often overlook, especially edge cases.

Example

If a feature breaks only when a certain user flow hits a third-party API at scale, AI can pick up that anomaly through network request monitoring and historical test variance. It flags what looks like a stable UI but is actually a flaky integration.

Scalability on its own

Manual teams scale linearly. More tests = more people. And AI-driven testing doesn’t. Whether you’ve got 500 or 5,000 test cases, autonomous QA solutions handle them in parallel across environments, builds, or configurations.

Benefit

This means teams can expand their test coverage as the product grows without hiring a dozen more testers or building a costly device lab.

A tip:

Launching/updating the web app, chances are, you need browser compatibility. OwlityAI executes all branches simultaneously: no scheduling, delays, or extra staffing.

Automation lowers total cost

Determine high-effort, low-value tasks and let artificial intelligence handle them. From test script maintenance to flaky test triage, AI-powered testing tools reduce manual input across the QA cycle.

Benefit

No more need for begging for an additional budget for QA initiatives. AI stabilizes and even reduces spend over time and frees your senior testers to focus on exploratory testing.

Calculation example

A mid-sized app having four builds per month relies on a manual tester with a USD 85,000 annual salary. 

Add regression execution, bug triaging, and test case upkeep.

Consistency at its best

Intuition is a human advantage. But in some cases (for example, when the tester’s level is not that high), intuition turns into a roadblock. 

On the other hand, every time AI executes tests, it does so in the exact same way. No skipped steps. No undocumented shortcuts. Its algorithms ensure comprehensive coverage across test suites with minimal variation.

Benefit

Inconsistency of tester-by-tester execution is no longer a problem. Even when testing complex, repetitive scenarios, AI guarantees that every test adheres to a strict way.

Additionally, AI tools like OwlityAI auto-generate audit-ready summary reports, which help teams maintain compliance (HIPAA, SOC2) without added overhead. 

Transitioning from manual to AI-driven QA

1. Determine bottlenecks

The goal of the audit is to identify tasks that drain time and introduce errors. In SMB companies, they typically pop up in the following areas:

• Manual regression testing: Often takes up 50% of the QA team’s sprint. Many companies struggle to catch up with hiring strong PROs when the product grows and requires more rigorous testing. 
• Maintenance: Even minor frontend changes break dozens of scripts, requiring hours of manual updates, especially in UI-heavy apps.
• Bug triaging and false positives: Another time-consuming task is double-checking unstable test results. And this is the problem in outdated scripts, not in factual bugs or flow.
• Cross-browser or device compatibility: Maintaining coverage across environments manually becomes unsustainable beyond two or three variants.

Use Jira’s ability to calculate a time-on-task metric or create a simple chart to measure the time each type of task takes. Then, prioritise automation where it saves the most effort.

2. Start with repetitive, high-ROI testing areas

You can’t complete everything at once, including automation. Start with frequently repetitive areas/tasks. Consider these areas:

• Regression suites: Run every sprint and typically mount up testing volume.
• Smoke tests: Validate whether core flows work before deeper testing begins.
• Happy path scenarios: Ideal for initial automation because they’re predictable and stable.

OwlityAI scans your application, identifies relevant components for the tests, and generates scenarios.

Tip: Isolate one flow (user registration, checkout, etc.) and use that as your pilot.

3. Check your CI/CD stack and choose a relevant tool

Tough processes and incompatibility are among the biggest reasons AI adoption stalls. Just check before you pay. Book a demo/call with the vendor team to find out as many details about the tool as possible. Are there any custom patches, or does the tool work with all modern CI/CD pipelines?

Key integration points to check:

• CI/CD pipelines: OwlityAI integrates with Jenkins, GitLab CI, GitHub Actions, and Azure DevOps via APIs.
• Bug tracking systems: Ensure your test results auto-export to Jira, Azure Boards, or whichever issue tracker.
• Version control: Look for tools that trigger test cycles based on code changes in your Git branches.

4. Keep your QA team prepared

Replacing humans is not a problem with manual testing. But the cycle's acceleration is. And AI helps humans get faster by changing their roles: Case Executors → Test Architects + Quality Strategists. 

Yet, this poses another challenge — your team should know how AI actually works.

Top 3 resources for team readiness:

1. Vendor-led onboarding: Vendors typically offer interactive sessions where your team gets real-time feedback on how to structure flows and interpret results.
2. Sandbox environments: Let your team run real test cycles in a non-production environment to experiment with all elements of the typical testing process.
3. Skill bridging courses: Coursera, Microsoft Learning, and other digital edu platforms offer specialized tracks in AI. Turn to this type after trying the previous ones.

5. Track testing KPIs and tune your strategy

Don’t gauge to just gauge. Measure to improve. Implemented an autonomous QA solution? Great. Then, consistently track performance metrics to validate ROI and fine-tune your strategy.

ABCs to focus on:

• Defect detection rate: How many bugs were caught pre-release?
• Test execution time: How much has runtime decreased?
• Test coverage: Is the AI expanding your coverage into complex flows or edge cases?
• False positives: Are test results stable, or are you still chasing unreliable failures?

Why AI still needs human testers

Exploratory insight and critical thinking

Artificial intelligence needs patterns to bring the most value. Yet, it lacks flow understanding, edge-case creativity, and business-critical decisions — this is where humans are still irreplaceable. They spot gaps that automation won’t flag without prior learning.

Example

Given: SaaS invoicing platform with lots of features and intricate flow. AI test suite covered all form fields, PDF exports, and backend validation flows. But a senior QA noticed a loophole during exploratory testing: clients could change currency mid-invoice without triggering recalculations in totals.

Action: This wasn’t a functional bug, and it led to revenue discrepancies. The fix required a code patch and, most importantly, a business decision. AI would never flag this without strategic awareness.

Bringing context and domain expertise to the table

Again, AI-powered testing tools see code and interaction patterns. Testers see product goals, user expectations, and business constraints. That contextual awareness influences shaping what to test, when to test it, and how much confidence a release deserves.

Example

A healthcare app was releasing a new feature allowing doctors to send prescriptions digitally. The tool handled basic UI testing, API validation, and ensured no regressions. 

But the QA Lead must better understand HIPAA compliance rules and pharmacy workflows. And they raised a red flag: if a prescription contained a controlled substance, it had to trigger multi-factor authentication and log an audit trail for legal purposes.

The AI didn’t flag it because nothing was “broken”. However, there was a regulatory risk. The QA team designed additional test cases to validate legal compliance, preventing a potential lawsuit.

Finance, insurance, and health require human validation even more than other industries.

AI frees testers to level up their game

When autonomous QA solutions handle regression, flaky test maintenance, and bulk script updates, humans can shift from routine to strategic roles. 

They become quality analysts: they interpret the results and identify trends to bring more business value.

Collaborative shift in practice:

• AI flags test failures and performance degradation.
• Human testers validate root cause, assess business impact, and escalate only critical issues.
• QA Leads use the tools’ KPI dashboards to brief product managers in sprint reviews.

Bottom line

To describe in detail all AI-powered software testing benefits, we might run out of digital paper.

But the main points:

• AI accelerates testing cycles
• AI brings scalability to your testing table and saves you money
• Next-gen tools turn routine into software quality and save your reputation

In a nutshell, are you ready to test the waters? 

OwlityAI is the AI-powered testing tool that makes testing faster, easier to perform, and more efficient.