Artificial intelligence is changing how software is built, and that transformation is also reshaping software testing.

Quality assurance professionals are no longer responsible only for manually executing test cases, reporting defects, and maintaining regression suites. Modern testing teams increasingly work with automated testing frameworks, AI-assisted development tools, continuous integration pipelines, APIs, cloud environments, and products that may themselves contain machine learning or generative AI features.

For employers, this creates a new hiring challenge.

Companies still need testers who understand the fundamentals of software quality, but they also need professionals capable of working effectively alongside AI tools and evaluating software whose behavior may not always be perfectly predictable.

The goal is therefore not simply to hire an “AI tester.” Employers need software testing professionals who combine strong testing fundamentals, automation knowledge, analytical thinking, domain understanding, and the ability to use AI intelligently.

The shift is already visible across quality engineering. Capgemini’s World Quality Report 2025-26 found that 63% of respondents considered generative AI a key skill for quality engineers, while 60% highlighted core quality engineering skills. Soft skills also remained important, demonstrating that AI knowledge is adding to — rather than replacing — traditional testing expertise.

Here is how HR teams, recruiters, and engineering leaders can adapt their hiring strategies.

How AI Is Changing Software Testing Roles

AI can assist testers with repetitive or time-consuming activities such as generating initial test cases, analyzing requirements, creating test data, identifying patterns in defect reports, writing automation scripts, and summarizing test results.

That can make testing faster, but it also shifts where human expertise is most valuable.

Instead of spending the majority of their time executing predefined test cases, skilled testers can focus more attention on areas such as:

• Test strategy
• Exploratory testing
• Risk analysis
• Complex edge cases
• User behavior
• Security and privacy considerations
• Automation architecture
• Test data quality
• AI output validation
• Root-cause analysis
• Business-critical workflows

The technology being tested is changing as well.

Traditional software generally produces predictable outputs when given specific inputs. AI-driven applications can behave differently. Large language models, recommendation engines, AI agents, and machine learning systems may produce probabilistic or non-deterministic results.

ISTQB’s current AI Testing framework reflects this evolution, covering areas such as testing generative AI and LLM systems, input data testing, machine learning model testing, and AI-specific quality characteristics.

As a result, employers may need to hire for two related capabilities: using AI to improve testing and testing products that contain AI.

They are not necessarily the same skill.

1. Define the Testing Role Before Starting Recruitment

One of the biggest hiring mistakes is publishing a generic “QA Engineer” job description without determining what the individual will actually test.

Before sourcing candidates, clarify the technical environment.

For example, determine whether the position primarily involves:

• Manual functional testing
• Web application testing
• Mobile application testing
• API testing
• Test automation
• Performance testing
• Security testing
• Data testing
• AI or machine learning product testing
• SaaS platform testing
• Embedded software
• Enterprise applications
• DevOps and continuous testing

The requirements for a manual QA analyst testing an internal business application will differ significantly from those for an automation engineer testing an AI-powered SaaS platform.

Recruiters should work with engineering leaders to separate essential skills from desirable skills rather than creating an unrealistic list covering every testing technology available.

2. Prioritize Strong Testing Fundamentals

AI familiarity should not compensate for weak testing fundamentals.

A candidate might know how to ask an AI assistant to generate dozens of test cases, but that does not necessarily mean they understand whether those tests adequately cover the application’s risks.

Strong software testers should understand concepts including:

• Test planning and strategy
• Functional and non-functional testing
• Regression testing
• Integration testing
• System testing
• User acceptance testing
• Exploratory testing
• Boundary-value analysis
• Equivalence partitioning
• Risk-based testing
• Defect lifecycle management
• Root-cause analysis
• Test coverage
• Requirements analysis

This foundation becomes even more valuable when AI is introduced.

AI can propose tests quickly. The tester still needs to decide whether the suggestions are relevant, identify what the AI missed, and determine which failures matter most to users and the business.

3. Look for AI Literacy, Not Just AI Tool Experience

Employers should avoid reducing AI competency to whether a candidate has used a specific product.

AI tools change rapidly. A tester who understands their strengths and limitations will usually adapt more easily than someone whose experience is limited to one platform.

Useful AI-related capabilities can include:

• Using generative AI to brainstorm test scenarios
• Generating or refining test cases
• Creating test data
• Assisting with automation code
• Analyzing logs and defects
• Summarizing testing results
• Understanding hallucination risks
• Reviewing AI-generated code rather than trusting it automatically
• Protecting confidential data when using AI platforms
• Evaluating prompts and LLM responses
• Understanding model variability and non-deterministic behavior

Candidates should also understand that AI-generated output requires verification.

For example, if a tester asks an AI system to produce an automated test script, the candidate should still be capable of reviewing the code, identifying incorrect assumptions, running the test, interpreting failures, and modifying the script when necessary.

ISTQB’s specialist framework for testing with generative AI similarly emphasizes both practical use of LLMs and issues such as hallucinations, bias, risk, privacy, and prompt engineering.

4. Evaluate Automation Skills According to the Role

Not every software tester needs to be a senior automation engineer, but automation capabilities are increasingly valuable.

Depending on your technology stack, relevant skills could include experience with tools and frameworks such as:

• Selenium
• Playwright
• Cypress
• Appium
• Postman
• REST Assured
• JUnit
• TestNG
• PyTest
• Cucumber

Programming or scripting languages may include:

• Java
• JavaScript
• TypeScript
• Python
• C#

Candidates working in mature engineering environments may also need familiarity with Git, CI/CD platforms, containers, cloud environments, databases, logging systems, and observability platforms.

Instead of requiring every tool on your organization’s technology stack, evaluate whether the candidate understands the principles behind test automation.

Someone with strong Playwright experience, for example, may be perfectly capable of learning another browser automation framework.

5. Assess Whether Candidates Can Test AI-Powered Products

Organizations building AI functionality may need additional capabilities beyond standard QA.

Testing an AI chatbot, AI agent, recommendation system, or machine learning model is different from validating a conventional form submission.

A traditional test might ask:

When the user selects “Save,” does the record appear in the database?

An AI application can introduce less deterministic questions:

• Is the generated response factually correct?
• Does the system consistently follow instructions?
• Does it refuse unsafe requests appropriately?
• Does performance change when prompts are phrased differently?
• Does the application expose sensitive information?
• Does the model behave consistently across user groups?
• Can users manipulate the model through prompt injection?
• What happens when supporting data is incomplete?
• Does an autonomous agent execute the correct action?
• How should an acceptable answer be scored?

Candidates do not necessarily need years of AI testing experience, particularly because many of these technologies are relatively new.

However, they should demonstrate an ability to reason about uncertainty, risk, evaluation criteria, data quality, and unexpected behavior.

6. Hire for Exploratory Thinking

One of the most valuable traits in a tester is curiosity.

The strongest testers do not simply ask whether the software behaves as documented. They ask what could go wrong.

Give a candidate a simple feature such as a checkout page and ask:

“How would you test this?”

A strong candidate may investigate:

• Valid payments
• Invalid payment information
• Multiple currencies
• Discounts
• Tax calculations
• Network interruptions
• Duplicate submissions
• Browser compatibility
• Mobile usability
• Accessibility
• Session expiration
• Payment security
• Unusual quantities
• Inventory changes during checkout

The objective is not to count how many test cases they can name.

You are evaluating how they think about systems, users, dependencies, failure conditions, and risk.

AI may generate a large checklist in seconds. Human testers create value by recognizing which problems deserve the most attention.

7. Use Practical Assessments Instead of Trivia Tests

A long list of theoretical questions may identify candidates who memorize testing terminology without revealing whether they can solve real QA problems.

Practical exercises are often more effective.

For example, provide the candidate with:

• A small demo application
• A feature specification
• A sample API
• A short user story
• A deliberately flawed webpage

Ask them to identify risks, develop a testing approach, document important defects, and explain what they would automate.

For automation roles, candidates could create one or two representative automated tests rather than completing a lengthy algorithm challenge unrelated to the position.

If your organization encourages AI-assisted work, consider explicitly allowing candidates to use AI during the assessment.

This creates a useful additional evaluation: Can the candidate use AI responsibly while still demonstrating independent judgment?

Ask them to explain what AI produced, what they changed, what they rejected, and how they verified the final result.

That approach may reveal more about real-world performance than banning AI completely.

8. Ask Better Interview Questions

Software testing interviews should encourage candidates to explain their reasoning.

Useful questions include:

How would you decide which tests should be automated?

Look for consideration of repetition, stability, business importance, execution frequency, maintenance cost, and return on investment.

Tell us about a defect that was difficult to reproduce. How did you investigate it?

This tests troubleshooting ability and persistence.

What would you do if requirements were unclear?

Strong testers should know how to clarify assumptions rather than silently inventing expected behavior.

How have you used AI in your testing workflow?

Look beyond whether they name an AI tool. Ask what task it improved and how they verified its output.

When should AI-generated test cases not be trusted?

Good candidates may mention incomplete requirements, hallucinations, incorrect assumptions, privacy concerns, lack of business context, and insufficient edge-case coverage.

What makes a test suite valuable rather than simply large?

Candidates should recognize that hundreds of low-value automated tests can be less useful than a smaller, stable suite covering critical risks.

How would you test an AI chatbot?

Possible discussion points include accuracy, relevance, safety, hallucinations, consistency, latency, privacy, prompt injection, edge cases, and evaluation methodology.

9. Evaluate Communication and Collaboration Skills

Testing is increasingly collaborative.

QA professionals may regularly work with:

• Software developers
• Product managers
• UX designers
• DevOps engineers
• Data scientists
• Security teams
• Customer support teams
• Business stakeholders

A tester may discover a serious problem, but identifying it is only half the job. They must communicate the issue clearly enough for others to understand its severity, reproduce it, and make an informed decision.

Evaluate whether candidates can write concise bug reports, explain technical problems to non-technical colleagues, challenge assumptions constructively, and participate effectively in product discussions.

This remains important even as testing becomes more AI-driven. Capgemini’s World Quality Report found that written and verbal soft skills continued to rank among important capabilities for quality engineers alongside technical and generative AI skills.

10. Do Not Assume Manual Testers Are Becoming Obsolete

Employers should be cautious about assuming that AI and automation eliminate the need for manual testing.

Automation is particularly effective for repeatable checks such as regression testing, API validation, and predictable workflows.

Human exploration remains valuable when assessing:

• New functionality
• User experience
• Unexpected workflows
• Ambiguous behavior
• Accessibility
• Complex business logic
• Rare edge cases
• Visual issues
• AI-generated results

The more appropriate hiring strategy is often to find testers who understand when automation is valuable and when human investigation is necessary.

A strong QA organization may include different specialists rather than attempting to replace every testing function with one highly automated role.

11. Watch for Common Hiring Red Flags

No candidate will know every testing tool, but certain behaviors deserve closer attention.

Potential warning signs include:

• Depending entirely on AI-generated test cases
• Being unable to explain why a test is necessary
• Focusing only on “happy path” scenarios
• Automating everything regardless of maintenance cost
• Treating the number of test cases as the main measure of quality
• Ignoring business impact when prioritizing defects
• Being unable to explain previous testing decisions
• Trusting AI-generated code without validation
• Having little understanding of data privacy when using AI tools
• Treating developers and QA as opposing teams

The strongest candidates typically think of software quality as a shared engineering responsibility.

12. Consider Transferable Skills When the Talent Pool Is Limited

The ideal candidate may not already have experience with every technology your company uses.

Evaluate transferable capabilities.

For example, a candidate who demonstrates excellent testing fundamentals, automation knowledge, debugging ability, and strong technical curiosity may be able to learn your specific framework quickly.

Similarly, experienced automation engineers without extensive generative AI experience can often develop AI testing capabilities through training.

Look for evidence of continuous learning:

• Personal projects
• Technical certifications
• Open-source contributions
• Testing communities
• Courses
• Conference participation
• Experimentation with new tools

Relevant certifications such as ISTQB can demonstrate structured knowledge, while newer specialist certifications now cover both testing AI systems and applying generative AI to software testing.

Certifications should generally complement practical evaluation rather than replace it.

13. Build AI Upskilling Into Your Talent Strategy

Hiring cannot be the only answer to changing technology requirements.

AI tools will continue evolving after today’s new QA hire joins your organization.

Companies should therefore create opportunities for existing testers to develop capabilities in areas such as:

• AI-assisted test design
• Prompt engineering
• LLM evaluation
• Test automation
• API testing
• Python or JavaScript
• CI/CD
• Cloud testing
• Security testing
• Data quality
• AI governance

Organizations are still relatively early in implementing generative AI across quality engineering. The World Quality Report 2025-26 found that 43% of organizations were experimenting with GenAI in QA while only 15% had scaled it enterprise-wide.

That suggests employers should avoid designing hiring requirements around the assumption that every experienced tester already has years of advanced AI testing experience.

Learning ability may be more valuable than experience with whichever AI platform happens to be popular today.

What Should a Modern Software Testing Candidate Look Like?

There is no universal profile, but a well-rounded modern QA candidate may combine several qualities:

• Testing expertise: Understands testing methodologies, defect management, test design, risk, and software quality.
• Technical ability: Can work with APIs, databases, automation frameworks, development tools, and modern software environments appropriate to the role.
• AI literacy: Knows how AI can assist testing while understanding its limitations and risks.
• Critical thinking: Questions assumptions and looks beyond obvious test scenarios.
• Business understanding: Prioritizes failures according to customer and organizational impact.
• Communication: Can clearly explain defects, risks, and testing decisions.
• Adaptability: Learns new frameworks, platforms, and AI capabilities as the technology landscape changes.

Hiring managers should determine how much weight each capability deserves according to the product, engineering organization, and seniority of the position.

AI Should Change How You Hire Testers — Not Why You Hire Them

AI is making software testing faster and more sophisticated, but the fundamental objective of QA has not changed.

Organizations need confidence that their products work reliably, safely, and as users expect.

AI can generate tests, create scripts, analyze data, and accelerate routine activities. It cannot eliminate the need for people who understand risk, recognize unusual behavior, question assumptions, and make informed decisions about software quality.

The most valuable software testing talent in the AI era will therefore not be professionals who compete with AI on repetitive tasks.

They will be the professionals who know how to use AI while retaining the independent judgment required to determine whether software can actually be trusted.

For HR teams and recruiters, the hiring strategy should reflect that balance. Look beyond individual tools and prioritize strong QA fundamentals, technical adaptability, AI literacy, analytical thinking, and communication.

Those qualities are far more likely to remain relevant as both software and artificial intelligence continue to evolve.

Frequently Asked Questions

1. What skills should employers look for when hiring software testers in the age of AI?

Employers should prioritize core software testing knowledge, analytical thinking, automation capabilities appropriate to the role, API and technical skills, AI literacy, communication, and an ability to understand business risk. Candidates testing AI-powered products may also need knowledge of LLM evaluation, data quality, model behavior, security, and AI-specific testing approaches.

2. Will AI replace software testers?

AI is more likely to change testing responsibilities than eliminate software testing entirely. AI can automate repetitive activities and assist with test generation, but organizations still need professionals to evaluate risk, investigate unexpected behavior, conduct exploratory testing, validate AI output, and determine whether software meets user and business requirements.

3. Should QA engineers know how to code?

Coding requirements depend on the role. Manual QA positions may require limited programming knowledge, while automation engineers and software development engineers in test typically need stronger coding skills. Basic scripting, APIs, databases, and technical troubleshooting are increasingly useful across many testing positions.

4. How can recruiters assess software testers effectively?

Combine structured interviews with practical exercises. Give candidates realistic requirements or a sample application and ask how they would test it. For technical positions, include a small automation or API-testing task. Evaluate the candidate’s reasoning, prioritization, communication, and ability to identify risks rather than focusing only on the number of test cases produced.

5. Should candidates be allowed to use AI during testing assessments?

Allowing AI can make sense when employees will be permitted to use AI in their actual role. Instead of evaluating whether candidates can complete tasks without AI, employers can assess whether they use AI effectively, identify incorrect AI output, protect sensitive information, and independently validate the final work.

6. What is the difference between AI-assisted testing and AI testing?

AI-assisted testing involves using AI tools to support activities such as creating test cases, generating automation code, analyzing defects, or producing test data. AI testing focuses on evaluating applications containing artificial intelligence or machine learning capabilities. Some QA professionals may need expertise in one area, while others may require both.