AiThority Interview with Mav Turner, Chief Product and Strategy Officer at Tricentis

Mav Turner, Chief Product and Strategy Officer at Tricentis chats about his AI development journey, qTest Copilot, impact of AI on DevOps, and more in this Q&A:

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Hi Mav, take us through your journey in AI development and more about your role at Tricentis?

AI has been a fascinating area of growth for me, combining my background in computer science, IT and product development. Over the years I’ve seen how technology can transform businesses — not just through innovation, but by solving real-world challenges. At Tricentis, my focus with AI is on how it can accelerate business outcomes for our customers — making processes faster, reducing costs and ensuring quality doesn’t fall through the cracks.

I wear two hats in my role as Chief Product and Strategy Officer at Tricentis: balancing product delivery with long-term growth strategy. Tricentis is already ahead of the curve in areas like cloud transformation, SaaS and embedding AI into testing workflows, but it’s also critical for all businesses to think beyond immediate product roadmaps. My goal is to align our innovations with the larger transformations happening in global enterprises, such as modernization efforts and shifts in how teams work. It’s an exciting challenge to bring our various solutions together cohesively and ensure that we continue driving impact for customers while setting the stage for sustainable growth.

Tell us more about qTest Copilot and how it enables QA teams? 

qTest Copilot is a generative AI tool integrated into our broader test management and analytics platform, Tricentis qTest. It simplifies and speeds up the process of creating test cases by leveraging AI to generate test coverage for applications, identify potential quality gaps and provide detailed test steps and expected results in seconds. The tool also helps standardize test case descriptions, making it easier for teams to maintain consistent documentation across their testing efforts.

As development timelines grow shorter, QA and developer teams are facing increasing pressure to improve test coverage while maintaining efficiency. qTest Copilot automates repetitive and time-consuming testing tasks, enabling teams to focus on more strategic and higher-value activities. Through the intentional combination of humans and generative AI, playing on the unique strengths of both, the release of qTest Copilot is helping teams improve overall software quality while supporting faster and more efficient delivery cycles.

How are you seeing AI impact the DevOps game today?

AI is transforming DevOps by addressing some of the most persistent challenges, like improving efficiency, reducing costs and enhancing software quality. It is also a key to accelerating continuous improvement cycles which is a core tenant of DevOps.  One of the biggest shifts we’ve seen is in testing, which practitioners consistently identify as the most valuable area for AI investment. We’re seeing AI-augmented tools help teams automate test case generation, analyze test results and even perform risk analysis on code changes. This doesn’t just save time — with teams reporting gaining back as much as 40 hours per month, according to our research — but also allows QA and development teams to focus on higher-value tasks that drive better outcomes.

Beyond automation, AI is also changing how DevOps teams approach problem-solving. Generative AI tools, for example, simplify complex workflows, helping teams identify quality gaps or predict potential defects. However, I want to be clear that this isn’t a fully autonomous future: humans remain central to ensuring quality, with most teams still reviewing AI outputs regularly. The key is using AI as a powerful collaborator, enhancing productivity and accuracy without sacrificing oversight.

Can you talk about software teams across the world and how they’ve been using AI to power their test cycles? 

Across the globe, software teams are using AI to redefine how they approach testing — the APAC region is one standout example of this transformation. With AI adoption accelerating, nearly three-quarters of organizations in APAC cite generative AI as a driving force behind IT investments, helping teams tackle the growing demands of faster and more efficient test cycles. AI-powered tools are enabling these teams to move beyond manual methods, allowing them to quickly generate test cases, broaden testing scopes and identify quality gaps in ways that weren’t possible before. This means they’re not just keeping pace with innovation: they’re pushing boundaries.

In APAC, government-led programs like AI Singapore are further fueling this momentum, creating a supportive environment for upskilling teams and fostering AI expertise. By leveraging these advancements, teams are addressing regional challenges like scaling operations and maintaining consistency across complex systems. Globally, AI is helping save significant time — our research showing 60% of developers are more productive due to AI, and 42% are seeing productivity gains in testing and QA — while also improving test accuracy and identifying risks earlier in the development process. Whether through smarter test planning or automated risk analysis, software teams worldwide are proving that AI isn’t just a tool; it’s a cornerstone for building the next generation of software delivery.

When using AI to accelerate software delivery, what should Engineers keep top of mind?

When using AI to accelerate software delivery, engineers must first evaluate the trade-off between speed and accuracy. Generative AI tools can automate repetitive tasks, streamline workflows and uncover potential quality gaps; however, they also introduce inherent uncertainty. Generative AI operates with non-deterministic outputs, meaning there will always be some degree of variation, which could lead to unforeseen errors. Engineers and testers need to determine whether the level of variability is acceptable for their specific use case and focus on value, rather than blindly plugging in new technology.

It’s also important to approach AI as a collaborator rather than an autonomous decision-maker. By keeping humans in the loop, engineers can maintain oversight, validate outputs and address potential issues before they escalate. Establishing clear guidelines for acceptable outputs, embedding human review points and continuously testing and iterating on AI systems will be the key to ensuring both speed and quality are achieved without sacrificing trust or reliability.

Some thoughts around the future of AI and software development?

The future of AI in software development lies in its ability to augment human creativity and efficiency, not replace it. As AI continues to mature, we’ll see it play an even greater role in automating complex tasks, from generating code to predicting potential defects. Tools like generative AI are already transforming workflows, enabling teams to focus on higher-value activities while reducing the time spent on repetitive or mundane tasks. However, the key will be finding the balance between leveraging AI to enhance productivity while not compromising quality or trust.

Looking ahead, AI’s role will evolve alongside more robust regulatory frameworks and an increasing emphasis on responsible use. Software teams will need to adopt new skills to work effectively with AI, transitioning from more traditional programming roles to ones that rely more heavily on reviewing, fine-tuning and guiding AI outputs through a strategic and creative lens. While AI won’t solve every problem, its integration into tools and processes will certainly reshape the development lifecycle, making software delivery faster and more collaborative.

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