There is no question, AI is speeding up development.
However, one thing we have realized, QA is more important than ever. From a volume standpoint, we have a lot more to validate, but with vibe-coding, we are seeing loose standards being accepted, a lack of release controls and security controls, and code bases that are becoming difficult to maintain.
Our solution: AI-powered QA systems that can handle inflated output to ensure what we are releasing is safe, secure, and functioning correctly before it reaches users.
Collectively, we call this Excelerate AI-Assisted QA, part of our EXCELERATE AI portfolio.
Why Is There a Confidence Gap in Traditional Software Testing Services?
Organizations can build and deploy software faster than ever, but many struggle to validate quality at the same speed. As AI accelerates development, traditional testing approaches often become a bottleneck, creating uncertainty around release readiness, performance and business risk.
Common challenges include:
- Development teams generating more code at unprecedented speeds
- Manual testing practices remaining largely unchanged
- Traditional automation becoming expensive and difficult to maintain
- Regression testing delaying releases
- Increased pressure to release quickly without increasing risk
The symptoms often become visible across the organization:
- Growing backlogs of untested functionality
- Incomplete regression coverage
- Reduced confidence in release readiness
- More production incidents despite increased automation investments
Research shows this challenge is widespread. According to SmartBear's AI Software Quality Gap Report,
70% of software leaders report that application quality has declined as AI-generated development accelerates, while 68% worry that testing cannot keep pace with AI-driven development.
The lesson isn't that AI is the problem.
The lesson is that quality engineering must evolve alongside development.
What Does AI-Assisted QA Actually Mean?
AI-assisted QA uses artificial intelligence to support quality engineering activities such as test creation, test execution, defect prediction, coverage analysis and risk prioritization.
One of the biggest misconceptions about AI in QA is that it exists to replace testers.
I don't believe that's the future.
The most successful organizations treat AI as a force multiplier for quality engineers rather than a replacement for human expertise.
AI-assisted QA can support teams in several ways:
- AI-generated test cases based on user stories, requirements and existing application behavior
- Self-healing automation that adapts to minor UI and application changes
- Predictive defect analysis that identifies areas of elevated risk
- Risk-based testing recommendations that prioritize high-value scenarios
- Synthetic test data generation for broader and safer testing coverage
- Coverage analysis that helps identify testing gaps
These capabilities allow teams to expand coverage and accelerate testing without dramatically increasing effort.
Most importantly, they enable quality engineers to spend less time on repetitive tasks and more time on critical thinking, exploration and risk assessment.
The graphic below illustrates how AI can augment quality engineering across the entire testing life cycle — from planning and test design to automation and reporting — helping teams deliver higher quality software faster and more efficiently.
Case Study: AI-Powered API Testing at Scale
A global healthcare and pharmaceutical distribution organization needed a faster, more scalable way to validate hundreds of APIs supporting its growing portfolio of cloud-native applications and microservices.
Experis implemented an AI-driven API testing framework that automated test generation, synthetic test data creation, documentation and validation while integrating directly into the client's CI/CD pipeline.
Results included:
- Standardized more than 350 APIs under a common testing model
- Reduced API test creation time by over 90%
- Cut API onboarding and automation efforts from two days to less than two hours
- Achieved 90%+ coverage for regression and negative test scenarios
- Saved an estimated 20,000+ engineering hours annually through intelligent automation
This approach enabled faster releases, broader test coverage and earlier defect detection while maintaining confidence in software quality.
What Works (and What Doesn't) When Implementing AI-Assisted QA?
Organizations achieve the best results when AI enhances human expertise instead of replacing it. Successful implementations combine automation, governance and engineering judgment, while unsuccessful programs rely on AI without sufficient oversight.
What Works
Successful organizations typically focus on:
- Risk-based testing
- Continuous quality engineering
- AI-assisted test generation
- Human validation and review
- Incremental adoption and experimentation
These teams start with targeted use cases, measuring outcomes and expanding gradually based on proven success.
What Doesn't
Organizations often struggle when they rely on:
- Fully autonomous testing
- Blind trust in AI-generated outputs
- Automation without a quality strategy
- Limited governance or accountability
AI can identify patterns, suggest tests and surface risks. However, it cannot fully understand business context, customer expectations or organizational priorities.
That's why human expertise remains essential.
The most effective quality organizations use AI to accelerate decision-making, not eliminate it.
How Can Organizations Build Trust in AI-Assisted QA?
Trust is built through governance, transparency and accountability. Organizations need clear processes for validating AI-generated outputs, protecting sensitive data and ensuring humans remain responsible for quality decisions.
For many organizations, concerns about AI are less about functionality and more about accountability.
Questions often include:
- How do we know AI-generated test cases are accurate?
- Can we trust AI recommendations?
- How do we ensure compliance and security?
- Who remains accountable for release decisions?
These concerns are valid — and essential to address.
Organizations should establish:
Governance Frameworks
Define clear standards for how AI tools can be used, monitored and validated within quality engineering processes.
Auditability and Explainability
Teams should understand why AI is making recommendations and be able to trace decisions when necessary.
Security and Compliance Controls
Sensitive data must remain protected, and AI usage should align with organizational compliance requirements.
Human Approval Checkpoints
Critical quality decisions should remain under human control. Trust ultimately rests with people, not algorithms.
Measurement and Reporting
Organizations should continuously monitor quality outcomes, defect trends and business impact to ensure AI is delivering measurable value.
Where Should Organizations Start With AI-Assisted QA?
The best starting point is a focused assessment of current quality engineering processes, automation maturity and testing bottlenecks. Most organizations can identify several high-impact opportunities that deliver measurable results quickly.
I recommend a structured approach:
1. Assess Current QA Maturity
Understand how testing is performed today and identify the biggest bottlenecks in your software delivery process.
2. Identify High-Impact Use Cases
Look for areas where AI can immediately improve efficiency, speed or coverage.
Common examples include:
- Test case generation
- Regression optimization
- Defect prediction
- Test data creation
- Release readiness assessments
3. Establish Governance and Metrics
Define success criteria before implementation begins.
Measure outcomes such as:
- Defect escape rates
- Test coverage
- Release velocity
- Mean time to resolution
- Production stability
4. Run Controlled Pilots
Start small and validate results before expanding adoption.
5. Scale Based on Proven Outcomes
Expand successful initiatives while continuing to refine governance and oversight processes.
Continuous Confidence Is the Future of Software Quality
I believe the future of software quality is not simply faster automation. It's continuous confidence — the ability to release quickly while maintaining trust that applications are secure, stable and ready for real-world use.
For years, many organizations accepted a reactive approach to software quality:
"Let's release now and fix issues in the next version."
Unfortunately, that mindset often turns customers into testers.
The future looks different.
Organizations are shifting toward proactive quality engineering models that prevent avoidable issues before they ever reach users.
Whether it's a banking platform processing millions of transactions or a point-of-sale system supporting critical business revenue, the goal remains the same:
- Catch issues earlier
- Reduce downtime
- Improve reliability
- Enhance user experiences
Our objective is simple: to give our customers the speed they desire, keeping up with market demand, but to catch issues early, reduce downtime and ensure a seamless user experience.
Explore Excelerate AI-Assisted QA
Author: Josh Eastman, Experis QE Practice Lead
Based out of Columbus, Ohio, Josh is an accomplished software testing and development professional with over 15 years of quality assurance and testing experience primarily within automation testing and Acceptance Test Driven Development (ATDD). He has held senior management and staff consulting positions at a great number of industry-leading companies including Nationwide Insurance, Capital One, Dell, Cisco, iHeart Media, Starbucks, Mercedes Benz, and First Citizens Bank. His background includes designing, implementing, and teaching automation solutions for a wide range of clients in systems and software, as well as consulting on process and methodology for improvement of quality and efficiency in the Software Development Lifecycle (SDLC).
