BETA
Updated: Mar 24
BETA
Updated: Mar 24
Evolving
Last Update: 3/13/2026
Your role’s AI Resilience Score is
38.5%
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are shifting as AI becomes part of everyday workflows. Expect new responsibilities and new opportunities.
AI Resilience Report for
Software Quality Assurance Analysts and Testers
They ensure software works correctly by checking for problems, testing features, and making sure everything runs smoothly before it’s released to users.
This role is evolving
The career of Software Quality Assurance Analysts and Testers is labeled as "Evolving" because AI tools are starting to handle some repetitive tasks like running test scripts, but they can't fully replace the human skills needed for complex problem-solving and communication. Testers still play a crucial role in designing tests, analyzing tricky bugs, and working with developers, which AI can't yet do effectively.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is evolving
The career of Software Quality Assurance Analysts and Testers is labeled as "Evolving" because AI tools are starting to handle some repetitive tasks like running test scripts, but they can't fully replace the human skills needed for complex problem-solving and communication. Testers still play a crucial role in designing tests, analyzing tricky bugs, and working with developers, which AI can't yet do effectively.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Evolving68.8%
68.8%
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Changing fast8.7%
8.7%
Anthropic's Observed Exposure
AI Resilience
Based on observed patterns of how Claude is being used across occupational tasks in real conversations
Changing fast2.6%
2.6%
Will Robots Take My Job
Automation Resilience
Estimates the probability of automation for each occupation based on research from Oxford University and other academic sources
Evolving36.7%
36.7%
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
Stable72.6%
72.6%
High Demand
Labor Market Outlook
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
10.0%
Growth Percentile:
92.7%
Annual Openings:
14,000
Annual Openings Pct:
60.7%
Analysis of Current AI Resilience
Software QA Analyst/Tester
Updated Quarterly • Last Update: 2/17/2026
What's changing and what's not
QA analysts use many tools today, but AI usually helps rather than fully replaces them. For example, official job guides say testers “design and execute tests” and “document software defects” [1] [1]. Some new AI tools can auto-generate simple test scripts or run routine checks, but studies show limits.
One academic study found that AI models could produce valid tests only for very easy code, and struggled badly with harder cases [2]. In practice, this means testers still write and update most test scripts by hand, using AI suggestions only as a starting point. Likewise, bug-reporting still needs human judgment: even if software flags an error, a tester has to describe it clearly.
Industry surveys reflect this mix: Deloitte reports that over half of companies using AI are actually adding more manual testing steps to double-check results [3].
On the other hand, a few routine tasks are already quite automated. Modern QA teams commonly run regression suites or automated test cases with tools (sometimes using AI to stabilize them). But tasks that need creativity or context show little sign of full automation.
For example, QA analysts may “participate in software design reviews” or suggest how a program should meet standards [1] [1]. We found no examples of AI fully handling those roles – likely because design work and complex bug investigations require human insight and clear communication. In short, AI today augments tasks like running tests and spotting obvious bugs, but human testers are still needed for nuanced analysis, planning tests, and communicating with developers [2] [3].
Sources
AI in the real world
Whether AI is adopted quickly in QA depends on costs, benefits, and trust. Many companies already use AI in development: for instance, Deloitte found over 30% of surveyed firms have integrated generative AI into products and tools, and expects nearly universal use by 2027 [3]. Large tech firms have resources to buy or build AI testing tools, and testing is often time-consuming, so AI can seem attractive.
Also, QA testers are relatively well-paid (about \$102,000 median per year [4]), so in theory AI that cuts their work could save money in the long run.
However, adopting AI also brings costs and risks. Tools may need special licenses and training. If an AI misses a serious bug, the cost could be huge, so companies must trust new tools.
In fact, QA jobs are actually growing fast (15% projected increase by 2034 [4]), which means firms still expect to hire humans for testing. In a tight labor market with high demand for software, companies might prefer skilled testers they trust over unproven AI. Socially and legally, using AI in testing is mostly seen as a practical tool with little controversy, but it must be proven reliable first.
Overall, the benefits of AI (speed and efficiency) are weighed against implementation costs and the need for accuracy. This balance suggests AI will continue to augment QA work – taking over repetitive steps – while human skills like critical thinking, creativity, and clear communication stay very valuable [3] [4].
Sources
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More Career Info
Career: Software Quality Assurance Analysts and Testers
Parent Careers
Similar Careers
Employment & Wage Data
Median Wage
$102,610
Jobs (2024)
201,700
Growth (2024-34)
+10.0%
Annual Openings
14,000
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Evaluate or recommend software for testing or bug tracking.
2
Identify program deviance from standards, and suggest modifications to ensure compliance.
3
Participate in product design reviews to provide input on functional requirements, product designs, schedules, or potential problems.
4
Provide technical support during software installation or configuration.
5
Monitor program performance to ensure efficient and problem-free operations.
6
Review software documentation to ensure technical accuracy, compliance, or completeness, or to mitigate risks.
7
Install, maintain, or use software testing programs.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
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