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3 ways AI will reshape quality engineering teams

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AI Test Automation: Speed, Accuracy &#038; Risk Reduction

AI is rapidly changing the software development and quality engineering landscape. From generating code to automating test cases, AI is driving efficiency across the software development lifecycle and inspiring many organizations to rethink how they work to deliver software.
For quality engineering (QE) teams, this evolution marks a pivotal moment. Testing has become the leading use case for AI in the SDLC, according to the <a href="https://www.tricentis.com/resources/ai-augmented-devops-trends-shaping-the-future">2024 Techstrong Research and Tricentis 2024 Survey</a>. At the same time, the complexity of AI systems and outputs has introduced new demands for oversight and strategy.
Read on to learn more.

Software testing is a natural choice to lead the first wave of AI use cases in software delivery. That’s because teams will need to rapidly adjust their testing strategies for AI-generated code, which has the potential to exponentially increase the volume and velocity of code that needs testing. If they don&#8217;t figure out how to adapt, testing teams could quickly find themselves overwhelmed.
Testing teams will also have to adjust their strategies for shifts in code quality. The increase in AI-generated could introduce more, and more varied, bugs and vulnerabilities than is expected in human-generated code. As ZDNet beehiv’s head of growth Edward White told <a href="https://www.zdnet.com/article/gen-ai-gives-software-developers-surge-in-productivity-but-its-not-for-everyone/" target="_blank" rel="noopener">ZDNet</a>, &#8220;While AI tools can produce efficient code for specific tasks, they may not always consider the unique dependencies, frameworks, or structures of older systems.&#8221;
&#8220;This mismatch can lead to problems such as unexpected behavior or even cause disruptions if the AI-generated code is implemented without thorough testing,&#8221; White cautioned.
Quality engineers should see the influx of AI-generated code as both a challenge and an opportunity to elevate their role in software development. But it also means the traditional structures of QE teams must adapt. Below, we explore three ways AI is transforming the way quality engineering teams work, as well as the role they play across the SDLC.

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AI enables QE teams to shift focus from repetitive tasks to activities that add strategic value. As automation handles &#8220;toil&#8221;—repetitive, manual work— quality engineers will concentrate on roles that demand critical thinking, collaboration, and ethical oversight.
What exactly does toil represent? Broadly speaking, “busy work.” This includes the specific repetitive and rote tasks across all IT teams such as manual code deployments, schema updates/rollbacks, manual test scripting, changing test cases to fit new business requirements, to name a few.
The Sisyphean burden of sustained toil that has traditionally plagued QE teams can lead to team burnout, errors leaking into production, or even system outages – therein lies the potential for AI to help QE teams work smarter, not harder. AI tools have already advanced to the point of being capable of generating test cases, adapting existing tests to new requirements, and automating other historical sources of toil for quality engineers.
The reduction in toil will naturally cause an increase in time and brainpower left for more critical, strategic thinking. The next generation of QA teams in the age of AI will be tasked with maintaining quality of the AI-generated outputs, ensuring data remains private as appropriate, and patching any gaps that may arise between different teams and their respective AI tool usage.
<h3>Navigating the inner and outer loops of development</h3>
According to a <a href="https://www.fastcompany.com/91045570/thanks-to-ai-the-coder-is-no-longer-king-all-hail-the-qa-engineer" target="_blank" rel="noopener">FastCompany</a> article, the inner loop of development refers to the rapid iteration of code by developers, while the outer loop encompasses broader processes like quality assurance, planning, and release management. AI&#8217;s role in the inner loop has been well-documented, with tools like GitHub Copilot generating code in seconds. However, QE&#8217;s integration into the outer loop is where AI&#8217;s potential shines.
<ul>
<li>Ensuring compliance and quality: QE teams will refine AI outputs generated in the inner loop, ensuring they meet ethical and operational standards.</li>
<li>Cross-loop collaboration: QE professionals must facilitate seamless transitions between inner and outer loops, leveraging AI to provide actionable insights across both.</li>
</ul>
For example, AI tools can act as &#8220;bridges&#8221; between the loops, automating error reporting and integrating feedback mechanisms that streamline communication between developers and quality engineers. This evolving role makes quality professionals indispensable in maintaining code quality and reliability as developers increasingly rely on AI.
By automating repetitive tasks such as regression testing or generating test scripts, AI reduces toil for QE teams. This liberation allows engineers to focus on optimizing workflows, analyzing high-risk areas, and driving innovation.
<h3>The rise of centralized expertise</h3>
To further capitalize on AI&#8217;s benefits, many organizations are establishing Test Centers of Excellence (TCoE). A TCoE serves as a hub for:
<ul>
<li>Providing AI experts who can coach teams on AI tools and methodologies.</li>
<li>Managing a centralized AI tooling library to ensure consistent, healthy usage across projects.</li>
<li>Standardizing AI governance practices to enhance compliance and ethical standards.</li>
</ul>
Such resources enable QE teams to maximize AI&#8217;s potential while maintaining uniformity and efficiency across the organization.

1. Day-to-day quality engineering work will become more strategic and analytical 

By next year, 70% of teams plan to use AI across the SDLC, according to the State of AI in DevOps 2024 report.

Future trend

As AI becomes more embedded in QE workflows, the skill set required of quality engineers will evolve. Beyond technical expertise, teams will need competencies in managing AI systems and analyzing their outputs.
<h3>Critical AI skills for quality engineers</h3>
<ul>
<li>Understanding inner and outer loops: quality engineers must know how to balance quick iterations in the inner loop with comprehensive oversight in the outer loop.
<ul>
<li>Delegate: Effective management of generative AI agents requires diligent oversight. Assigning team leads who monitor agent outputs can address their teams’ output discrepancies promptly and set performance benchmarks. By regularly reviewing outcomes, employees can keep agent tasks aligned with business goals.</li>
</ul>
</li>
<li>Evaluating AI outputs: Ensuring correctness and minimizing bias in AI-generated code will be critical.</li>
<li>Proficiency in AI-augmented tools: Tools like generative AI for test automation will be foundational to testing operations:
<ul>
<li>Build effective prompts: Focus on refining your team&#8217;s prompt-writing skills &#8211; clear, consistent instructions lead to more accurate generative AI agent responses. By coaching your team to test different prompts, weigh the outcomes, and refine their prompts, they can repeat until the agent delivers usable results.</li>
</ul>
</li>
<li>Human training agents
<ul>
<li>To make agents truly valuable, businesses must focus on training them beyond standard datasets. Humans must take on the role of trainers, imparting the subtleties of relationships, preferences, and contextual knowledge that agents can’t learn independently.</li>
<li>There’s another piece to this puzzle. Agents aren’t exceptional at reasoning—yet. They’re more like smart workflow systems than independent thinkers. To maximize their utility, tasks must be broken into discrete steps, with workflows meticulously designed for them to follow.</li>
<li>In this sense, training agents mirrors how we train human employees. It’s not just about inputting data but teaching processes, behaviors, and nuances. As agents gain more “agency” in the future, these skills will become even more critical.</li>
<li>Here’s the kicker — even individuals with no management experience will need to learn these training skills. Leading a team of AI agents will become as essential as managing human employees is today.</li>
</ul>
</li>
</ul>

2. Quality engineers will need additional skills in 2025 and beyond 

AI&#8217;s rapid advancements mean quality engineers must stay adaptable. Team members should embrace opportunities to learn from AI experts within a TCoE and seek out real-world case studies to understand best practices.
We sat down with Mav Turner, CTO at Tricentis, about the advent of new skills quality engineers will need to embrace to thrive in a post-AI software testing landscape. In the following video interview, he highlights the need for quality engineers to embrace change as AI becomes more prevalent in organizations’ IT landscapes, and how a positive attitude can not only keep things running smoothly but take productivity to the next level.

Adaptability and continuous learning

AI has the potential to revolutionize workflows, enabling teams to accelerate testing processes while maintaining a high standard of quality. While the individual quality engineer will benefit from new skills to work more comfortably alongside AI, the team itself has room for evolution, as well. Squeezing the enormous potential efficiency gains from AI tools in QE starts at the individual level, but with the right subtle shifts in mindset at the team level, we can expect to see benefits in leaps and bounds. Here are a few places where AI opens the door to improve how your team works:
<h3>Collaboration across teams</h3>
The integration of AI requires closer alignment between QE, development, and business teams. Centralized AI tools and shared dashboards can foster this collaboration by providing a unified view of testing and development progress.
<h3>Creating a standardized AI toolkit</h3>
A centralized AI tooling library within a TCoE ensures consistency and quality across projects. It minimizes redundancy, enforces governance, and enables teams to reuse successful AI models, creating substantial efficiency gains over time. This building synergy between AI and QE yields innovation and efficiency: A TCoE can provide the expertise and tools needed to manage AI across teams. Healthy AI practices, such as standardized tooling and rigorous auditing, ensure sustainable and ethical AI usage.
<h3>How AI-augmented testing can add value in practice</h3>
<ul>
<li>Automated test case generation: AI tools can analyze requirements to produce reusable test cases, significantly reducing manual effort.</li>
<li>Predictive testing: By identifying high-risk areas based on historical data, AI enables proactive defect prevention.</li>
<li>Anomaly detection in CI/CD pipelines: AI systems can monitor deployment pipelines in real time, flagging anomalies before they become critical failures.</li>
</ul>
<h3>Ensuring integrity of AI outputs</h3>
Building trust in AI-generated artifacts is essential:
<ul>
<li>Rigorous auditing: QE teams must implement processes to verify the accuracy, completeness, and reliability of AI outputs. According to a recent survey, a staggering 95% of QE teams currently verify AI-generated outputs before implementation. This trend will likely grow as the volume of AI-generated code increases, necessitating robust test automation systems to keep pace.</li>
<li>Ethical oversight: QE professionals play a key role in ensuring AI usage aligns with ethical principles and avoids harm.</li>
</ul>

3. Quality engineering teams will require a new mindset 

With high priority launches and software updates demanding close attention, it can be difficult to trust AI tools to take care of the dozens, sometimes hundreds of mundane test steps. However, by automating repetitive steps, AI can drastically eliminate bottlenecks in testing workflows. By using AI to maintain and update test scripts in the background, leaving your team the much quicker final review, you may find the overall manual burden greatly reduced on your teams, allowing them to focus on higher-value activities even in the weeks before a business-critical software release, questions or out-of-the-box thinking that you might not normally have scheduled for.
New mindset for new tools: Hear from Mav Turner, CTO at Tricentis, and his thoughts on how quality engineering teams can benefit from an evolved mindset toward AI in their testing workflows.

Rising above the toil across workflows

The future of QE lies in embracing AI technologies while maintaining governance and trust. Organizations that invest in upskilling their teams and centralizing their AI resources will unlock new levels of efficiency and innovation, transforming QE into a business enabler.
By recognizing the changing demands of AI-driven software development and preparing accordingly, QE teams can not only meet the challenges ahead but thrive in this evolving landscape.

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3 ways AI will reshape quality engineering teams