How to balance human and AI efforts in QA effectively

Are you also scared that AI will take your job? Let’s face it: AI has already taken thousands of jobs. But let’s look up trusted sources: The World Economic Forum indicates that 40% of all working hours could be impacted due to LLMs’ invention. 

Not will, but could. Not replace, but impact. Human expertise remains indispensable. To balancing AI and manual testing: Human testers bring domain knowledge and expertise that enable them to validate the machine’s findings. 

Deloitte’s 2024 GenAI research states that over 53% of US tech companies implemented or plan to implement AI-driven solutions within the next 12 months. OpenAI challenges the tech world with its fresh-backed Operator. But it is still far from a high-performing employee, innit?

QA strategies with AI are not meant to replace humans, but to create a collaborative ecosystem where AI handles computational heavy lifting and humans provide nuanced, contextual decision-making. 

AI is supposed to accelerate economic growth. That’s why the US government announced an unmatched USD 500B investment in AI and technology. Also, that’s why Deloitte assumed a USD 12B annual gain for the American economy due to the implementation of AI coding tools.

Despite this, human testers continue to play a crucial role in ensuring software quality. And here is how and why.

The distinct strengths of humans and AI in QA

What AI brings to the table

1. Task automation

Snowed under repetitive tasks, and they keep you from strategizing your testing? AI testing flops the scenario: It handles all repetitive, high-volume testing tasks. Namely:

• Regression testing: Here, autonomous testing can play a significant role: An AI testing tool automatically executes thousands of test cases simultaneously and detects even the smallest deviations caused by code changes.
• Load and performance testing: Imagine that your app became utterly popular overnight and all users hit the app concurrently. Chances are your app will go down without appropriate preparation. This is what AI does: it simulates thousands of users and analyzes system behavior under stress to create appropriate “defense” tests.
• Test script maintenance: Every modern tool self-updates test scripts when UI elements or workflows change. This is what the desired 25th hour in a day looks like.

2. Smart scanning, wider test coverage

How was it before? The tester analyzed the app features, its goals, and architecture elements and, based on their experience and intuition, wrote tests to cover potential pitfalls. Too much reliance on the tester, innit? Speaking from experience, gaps in coverage are inevitable. What does AI do:

• Analyzes historical test data and production logs and generates optimized test cases; changes them if needed.
• Identifies risk-prone areas in code based on past defect patterns.
• Detects UI anomalies across different screen resolutions and devices.

3. Real-time insights and predictive defect detection

This next-gen technology is not limited to detection — prediction is the next big thing. The system counts on deep analysis and assumes where defects are most likely to pop up so that developers can handle that preventively. 

• Anomaly detection: ML models flag abnormal system behavior by analyzing logs, performance metrics, and previous defects.
• Prioritization of test cases: AI determines which tests to execute based on risk assessment.
• Self-healing: AI-powered testing tools adjust test scripts automatically when UI elements or API responses change.

The unique strengths of human testers

Creative thinking and curiosity

Most models are built to follow predefined rules, they lack human intuition and creativity — both crucial for exploratory testing. Where AI falls short:

• Expect unexpected cases: May sound weird, but makes sense — humans can predict that particular situations may occur even without much evidence or proof for that. Because here is imagination and fear which helps humans to play it safe. On the other hand, AI is based on logic and therefore, can’t understand why people may want to hit all the buttons in your app at once without any purpose. 
• Real-world thinking patterns: Not all designs are the same. Some of them are creative and too sophisticated intentionally. AI may flag such styles and elements because it is taught to flag all misaligned things.

More dimensions:

• Contextual problem-solving
• Intuitive edge case identification
• Emotional intelligence in user experience evaluation

Case in point: Many scientific works assure that AI can outperform only kids or less educated personalities in some sorts of tasks. Still, the best humans excel in creative thinking and reasonable problem-solving. Harvard Business Review seconds that, adding that Artificial Intelligence is an outstanding aider for human creativity and innovation, not the substitution.  

Strategic oversight in QA processes

Modern testing tools can prioritize tests, but it is more about tactics, as their prioritization heavily depends on the initial setup. On the other hand, humans potentially know hidden influencers and ensure business-critical endeavors come off.

Humans use their domain expertise to decide which test cases need manual execution, especially in areas involving security, compliance, or financial transactions.

Empathy for end users and UX validation

AI can examine functional correctness, but when it comes to overall user experience (the one, we sometimes call “feeling”), it screws.

Key differentiators:

• Contextual interpretation
• Emotional intelligence
• Complex scenario navigation
• Ethical reasoning

Humans evaluate emotion-driven usability (e.g., "May the user feel frustration from a confusing UI or dissatisfaction with slow response times?").

AI lacks emotional intelligence — it can’t assess how a particular user segment might perceive trust, clarity, or ease of use.

Case in point: Google’s Gemini has demonstrated not only significant limitations in creative problem-solving but also a critical (and scary, to be honest) twist in behavior. In one case, Gemini generated aggressive and inappropriate responses, forcing the user to… end their existence.

Where AI and humans work best together

Cycled regression testing

• AI’s role: The tool executes thousands of test cases simultaneously and, this way, reduces execution time from hours to minutes. This local automation speeds up feedback on software quality. Also, with Machine Learning algorithms, the tool identifies high-risk areas for the following targeted regression testing.
• Human role: Reviews flagged anomalies, examines false positives, and ensures business logic is preserved.

Data-backed exploratory testing

• AI’s role: Looks over historical defects, user behavior, and current performance to identify high-risk areas.
• Human role: Someone like a free artist, conducting free-form exploratory testing, identifying usability issues and unexpected behaviors that AI might miss.

Scalable performance testing

• AI’s role: Does everything to completely check your software under 100x traffic load. Real user simulation, “crushing” the app’s infrastructure and identifying performance bottlenecks across different environments. These different conditions allow the model to spot potential tie-ups before they impact users.
• Human role: The main task here is to interpret tool reports. Human testers should also try to contemplate the cause of slow response time: whether it is due to system limitations, architectural flaws, or suboptimal configurations. Then, develop comprehensive security strategies that incorporate both automated findings and human expertise. 

Defect triage and prioritization

• AI’s role: Analyzes and interprets previous bugs and historical code changes, modules they occurred in to assign severity scores.
• Human role: Double-checks AI’s prioritization, assesses business impact, and pushes through the critical-first approach.

User Acceptance Testing (UAT)

• AI’s role: Process user feedback and performance metrics and identifies areas that require improvement.
• Human role: Gathers qualitative input from users and highlights insights into the overall user experience.

User experience validation with AI-driven analytics

• AI’s role: Tracks heatmaps, analyzes user interactions, and identifies behavioral patterns that indicate usability issues.
• Human role: Interprets the data and oversees the process to clarify UX flaws and make user-centric design improvements.

Training and onboarding

• AI’s role: Uses all the findings (common user paths, bug logs, and behavioral data) to generally assist in creating training materials and onboarding programs.
• Human role: Mentors and guides new team members to understand the nuances of both the testing process and the software.

Benefits of balancing human and AI efforts

More efficient, more independent

AI’s impact: Automates regression, performance, and load testing, reducing execution times from days to hours. This allows QA teams to focus on test strategy, risk assessment, and exploratory testing rather than routine execution.

OutSystems and KPMG found that AI reduced software development time by up to 50%. The lion’s share of this reduction lies in shortening testing time — without wasting time on repetitive tasks, teams accelerate release cycles.

Example: An average e-commerce project can implement AI testing for their regression suite to speed up the check process. This way, they potentially can lower a testing cycle from two weeks to just three days, release features faster, and improve responsiveness to market demands.

More accurate with complete automation

AI’s impact: Any AI model is based on Machine Learning. The system detects patterns, anomalies, and defects in large datasets, picks the most influential ones, and learns from the overall analysis. And which is important — spends much less time on that. Specifically on testing, these precise and efficient moves minimize false positives and ensure consistency.

Human oversight: Hybrid QA processes bridge the gap between humans and modern technologies. Even though we are living in the age of unprecedented tech breakthrough time, every new technology needs its facilitator. Edge cases, UI/UX inconsistencies, and accessibility concerns — humans reign supreme in areas where AI still lacks contextual awareness.

Example: AI might flag a UI change as a defect, but a human tester understands that the update aligns with the design team’s strategic vision. Still, all form validation rules should remain intact across different input scenarios. This is where cooperation is paramount.

Allocates resources wisely

AI’s impact: When we were talking about automating repetitive “tech chores”, we kept in mind wise resource allocation. Data-backed decision-making is crucial here. Since machines can process more data sets in a short time, they are becoming irreplaceable aids to humans.

Example: The mentioned OutSystems and KPMG survey showed that AI spotted 42% to 56% of potential security and compliance concerns.

Scales AI in QA with reliability

AI’s impact: Thanks to parallel test execution across all platforms, the next-gen testing tools ensure effortless scaling. Rapid growth? No longer a problem. 

Human role: Humans validate AI-generated reports, and align test scripts with real-world usage scenarios since machines could miss important details sometimes.

Example: In a large-scale e-commerce platform, AI continuously tests thousands of transactions across different devices, browsers, and geographies. Human testers focus on complex payment flows and fraud detection. Every gear knows its function, the entire machine runs smoothly.

Tips for balancing human and AI in QA

1. Be flexible, implement incrementally

Tip 1: There is no need for an all-in-once approach. Automate repetitive tasks first: smoke tests, regression testing, API validation, etc. Maintain human oversight for more creative and important types.

Tip 2: Got succeed? Then expand. Don’t position this approach as human vs AI testing, it is rather human ALONGSIDE AI testing. Incorporate next-gen testing tool’s capabilities to reduce script maintenance overhead. 

2. Have AI for smart prioritization

Tip: Dive deeper into analytics, especially since modern tools provide a clear-cut picture of defect patterns, test case selection, and high-risk areas. Additionally, the predictive side significantly improves defect detection efficiency.

McKinsey’s material highlights that GenAI and narrowed AI tools can reduce not only testing time but entire IT modernization by as much as 50%. This is how QA strategies with AI can change the business world as 70% of Fortune 500 companies’ software was developed 20+ years ago.

3. Ensure AI literacy and hands-on training

Tip: Every tech advancement has its learning curve. Ensure your testers can interpret AI-generated reports and realize risk-based test case recommendations. Without it, even if AI produces pure gold, you won’t be able to machine it.

Upskilling strategies (tech focus):

• Develop machine learning interpretation competencies
• Create cross-functional AI-QA training programs
• Implement data science fundamentals for testing professionals

Training approaches:

• Design scenario-based AI result interpretation workshops
• Develop AI testing simulation environments
• Establish mentorship programs bridging traditional QA and AI technologies

Tip 2: Encourage teams to experiment with AI-assisted testing tools in sandbox environments before full-scale deployment.

4. Delineate AI and human responsibilities

Tip: Use AI tools for repetitive, high-volume test cases and human testers for exploratory testing, security assessments, and UX validation.

Advice for managers:

• Base decisions on test complexity: Automate structured, deterministic tests (unit and regression), but keep humans in the loop for ambiguous or user-experience-driven scenarios.
• Alignment is key: If you chase faster releases to outperform competitors, you should focus on reducing execution time. Then, your main steps should be toward AI-driven test automation; if improving exploratory testing is key, prioritize AI-assisted test insights.
• Assign responsibly: AI is cool, but we still can’t hand vital tasks over to it. For this reason, high-frequency, low-risk tests are the best choice. On the other hand, humans should focus on low-frequency, high-impact scenarios (e.g., security vulnerabilities).

5. Fine-tune AI-human collaboration

Tip: Piece together all stages of AI performance and assess its alignment with your QA team on regular retrospectives. Adjust AI parameters and test data strategies if needed.

What to evaluate:

• False positive/negative rates
• Test maintenance effort
• Defect detection trends
• CI/CD pipeline efficiency

Cases in point

Bert by Amazon

The retail giant has used robots in its warehouses for almost a decade. The latest advancement is the robot, called Bert which complements human efforts in picking, finding appropriate goods, and packaging. This type can safely navigate anywhere on the factory floor.

Bringing closer to testing, Bert has its own software which is utterly important to test. And now just calculate: Amazon has over 180 fulfillment centers across the globe. If every new update went through manual testing it’d stop the entire workflow since it’s time-consuming a bit.

On the other hand, autonomous testing ensures fast check-ins while maintaining high quality.

Healthcare software

Moxie is a healthcare robot that enhances the efficiency of hospital staff, particularly nurses, during their daily rounds. 

It can autonomously make deliveries and perform a variety of non-clinical tasks (restocking supplies, collecting samples, etc.). Since it integrates with electronic healthcare records, it’s crucial to ensure a thorough testing process. 

Although it doesn’t require constant and clear-cut instructions, his functioning is double-checked by humans to avoid misfunctions and fatal mistakes.

How to implement a balanced QA strategy

1. Analyze your current QA process

Mapping out your way is the most influential step. Testing bottlenecks you identify determine your next moves. Among these setbacks might be slow execution times, inconsistent test coverage, or frequent script maintenance issues. Also, analyze defect detection efficiency — make sure you didn’t miss critical bugs.

Determine which tasks are rule-based and repetitive (ideal for AI) versus strategic and creative (best handled by humans). 

2. Choose an AI testing tool

Different companies have different goals and different software accordingly. Even though modern autonomous testing tools have common functions, they also usually have appropriate focus and limitations. But ensure your tool has must-have features:

• Predictive analytics: It can proactively identify high-risk areas
• Self-healing: It adjusts to UI and code changes without manual script updates
• Optimization: It can continuously scan the app and adjust tests accordingly and even generate new ones if needed.
• Integration with CI/CD: The tool should seamlessly fit into your current workflows

Why OwlityAI?

Because the tool has all the listed must-have features and a bunch of additional testing perks: 

• It automatically generates comprehensive test cases that cover your entire application (95+% coverage).
• Under-the-hood AI assesses and assigns priority levels to test cases (High, Medium, or Low) depending on the severity of the area.
• Your app will grow, so will OwlityAI. The system monitors the user base, user behavior, and all new features and automatically updates test cases and scripts.
• OwlityAI also detects unstable tests, resolves any issues, and re-runs them for more reliable results.
• Has sufficient storage for all tests, scripts, and other important stuff.

3. Map your AI integration in timeframe

The step-by-step approach creates a calm atmosphere where you don’t need to put out the fire every time the app gets an update. Here is the three-phase checklist:

Phase 1: Pilot

• Select a low-risk test category (e.g., regression, smoke tests).
• Run AI tests alongside manual tests and then compare.
• Have human testers check AI-generated reports to validate accuracy.

Phase 2: Expanding

• Hand other areas over to AI: API and cross-browser testing.
• Optimize test data management. It’s crucial to make sure your model learns from relevant data.
• Adjust automated reporting dashboards to your preferences.

Phase 3: Full AI-human cooperation

• Start AI-driven exploratory testing to uncover unknown risks.
• Shift human testers toward UX validation, strategic planning, and complex test scenarios.
• Continuously refine AI models using historical defect data.

4. Keep collaboration between AI and human testers smooth

To avoid resistance to change and further potential pitfalls, establish leadership and technology advocacy. If you are a small startup, chances are you won’t encounter such a problem. But there could be another one — skepticism. 

So, use the fast wins approach: demonstrate quick results from AI testing. Heavily use real-time shared dashboards to consolidate AI-generated reports and human insights and show that humans are still important.

Tune regular feedback loops where testers validate AI findings and refine test strategies and vice versa (AI double-checks humans and shares its suggestions).

Last but not least, gather cross-functional teams (developers, testers, AI specialists, non-tech pros) to get the most out of AI impact and admire human expertise.

Bottom line

QA strategies with AI are the new normal. However, we are still at the point where we can’t rely on modern technology more than on humans. We’d say that Artificial Intelligence and other tech advancements are here rather to aid humans than to replace them.  

Yet, many companies of all sizes have difficulties in balancing AI and manual testing. While automation settled down a long time ago, enterprises and startups still hire humans to improve their products by using humans' creativity and curiosity. 

The presented article covers all the crucial steps on how to strike the desired balance and ensure your software updates quickly and keeps a high quality level.