Reimagining Software Quality with AI: Tricentis’ Bryan Cole Shares Insights

1. What key trends in software quality engineering and continuous testing stood out among this year’s award winners?

This year’s award winners recognize and act on the need to deliver quality software at speed and scale while reducing risk and increasing time to value. Top organizations are prioritizing continuous testing and automation to keep pace with rapid development cycles without compromising quality. These leaders are also enhancing their test strategies to support evolving architectures and integrating resilient testing frameworks that can adapt to growing complexity. As digital transformation continues to accelerate, companies that focus on scalable, resilient testing strategies to support innovation, while maintaining reliability, are reaping the greatest success.

2. How have advancements in automation and AI-driven testing changed the way enterprises approach software quality?

Automation and AI-driven testing are transforming software quality by enabling enterprises to shift away from reactive defect detection and toward proactive risk prevention – dramatically improving software quality, increasing software release velocity, and reducing costs. AI-powered automation has made the technical aspects of testing faster, smarter, and more adaptive. It’s helping teams identify risks earlier in the software development lifecycle (SDLC), reducing human error, helping to maintain test cases, and increasing test coverage as ecosystems grow increasingly complex. AI-powered analytics and machine learning algorithms are also enhancing the detection of patterns and prediction of failures, while also providing deeper insights into application stability.

Both automation and AI have enabled enterprises to improve their test efficiency, reduce redundant manual effort, and simulate complex business scenarios more accurately. These advancements ensure that quality assurance keeps pace with accelerated release cycles, making software delivery more resilient, cost-effective, and aligned with business goals. While still early in our AI-driven transformation of quality assurance, we’re already seeing the impact on QA teams.

3. Can you share an example of a company that leveraged continuous testing to drive significant business transformation?

One of our customers, PHOENIX Group, a European pharmaceutical wholesale and pharmacy retail leader, transformed its approach to quality assurance by implementing automated, continuous testing to support its expanding SAP operations. Previously reliant on manual testing, the company faced challenges consistently running large-scale regression tests, particularly for its most vital business processes within SAP Enterprise Warehouse Management (EWM). Each SAP system upgrade or warehouse migration exposed further critical testing needs, and the lack of automated regression testing presented a risk to business continuity.

By automating its regression tests, PHOENIX Group successfully expanded the scope of its test coverage and enhanced its quality assurance processes, minimizing production errors and maintaining quality across its large, complex warehouse environments. As a result, PHOENIX Group was able to reduce its testing cycles by 66%, accelerate deployments by a month, and significantly improve error detection, identifying 2-3 previously missed software issues per cycle. Automating its deep test suite also helped ensure consistent documentation and system traceability, meeting stringent European GxP compliance requirements.

Overall, deploying continuous testing minimized crucial production errors and created a scalable, high-quality testing framework – supporting PHOENIX’s ongoing business transformation, bringing greater visibility to quality efforts with the organization, and even giving the test team a “seat at the table” in strategic planning discussions. PHOENIX Group is already planning to expand its use of Tricentis solutions to further test both its ERP and SAP systems, increasingly implement test automation across teams, and establish centralized test management to facilitate defect tracking and enhance cross-departmental collaboration.

4. What strategies can organizations adopt to drive digital transformation using modern testing models and quality engineering principles?

Organizations can drive digital transformation by leveraging modern testing models and quality engineering principles. For example, teams can harness model-based testing to streamline test creation and improve coverage across complex enterprise applications. Utilizing a service virtualization approach enables teams to simulate unavailable or difficult-to-access systems, allowing continuous testing earlier and more thoroughly without dependencies, which is critical for accelerating digital initiatives. Risk-based testing can help organizations prioritize the most critical business processes, ensuring resources are focused on high-impact areas while reducing unnecessary test execution. Additionally, test data management solutions can generate secure datasets to improve testing accuracy while maintaining compliance.

For these strategies to drive business outcomes, it is important that teams discussing their approach at the onset of and throughout the SDLC to ensure each testing and quality engineering measure directly supports the overarching technical and business outcomes. Testing is not a one-time activity – it is a continuous process that drives business agility and directly contributes to positive outcomes in production.

5. What challenges do organizations commonly face when scaling continuous testing, and how have award winners overcome them?

The increasing complexity of today’s application environment causes many organizations to have several silos across their enterprise applications, hindering full ecosystem visibility and application integrity. By integrating these systems through our unified testing platform, this year’s award winners have eliminated these barriers, creating a more streamlined approach to implement and scale continuous testing.

The lack of standardized testing frameworks across today’s disparate systems often leads to inconsistencies in test execution and coverage gaps, making it difficult to ensure quality. Centralized test management ensures visibility across teams, enabling better collaboration and more efficient scaling of continuous testing. The first step to solving the challenge of implementing a continuous quality practice is to get visibility into the excellent QA work being done by your teams already!

6. How are industries outside of tech-such as healthcare, finance, and manufacturing-adopting modern testing frameworks?

In manufacturing and healthcare, the quality of products is essential as formulations and components’ unique specifications must meet strict specifications and documentation requirements to ensure safety and compliance. Organizations in these industries are adopting continuous testing to ensure these processes run smoothly, minimize the risk of defects, and maintain compliance with evolving regulatory standards, all while delivering on important society needs and patient outcomes.

In the case of financial services, organizations are also adopting AI-powered testing frameworks largely to enhance the reliability of digital payment processing, transaction authorization, and fraud detection systems. Through automated regression testing and reduced maintenance efforts, financial service organizations ensure seamless integration across banking platforms, compliance with financial regulations, and stability in high-volume transaction environments.

Every industry brings a unique set of challenges that an organization’s testing framework must address.  As such, it’s critical that each organization pinpoints the technical and business outcomes that are most important for its success and strategically determine its testing framework accordingly.

7. How can organizations recognize and celebrate innovation within their teams to promote a culture of continuous improvement?

Increased organizational communication can elevate the whole team by fostering a culture of shared learning and continuous improvement. Encouraging teams to discuss their successes and failures openly helps prevent redundant mistakes and accelerates innovation. Organizations should create structured opportunities for collaboration, such as cross-functional working groups, internal knowledge-sharing sessions, and innovation showcases where employees can present their solutions. Teams can improve their agility by breaking down knowledge silos and promoting a collective problem-solving mindset, which starts at the top. If leaders model this sense of collaboration and encourage managers to promote this mindset within their teams, it will have a natural domino effect and organizations will, in turn, see stronger agility across teams as a result of this collaborative, team-centric atmosphere within their organization.

Further, it’s imperative to make sure this celebration is implemented at the individual level. Recognizing contributions through awards, promotions, or peer acknowledgment further reinforces a culture where openness and innovation are valued and rewarded.

8. What impact does continuous testing have on innovation speed and risk reduction in software development?

Continuous testing is the backbone of effective and rapid innovation, and overall risk reduction, in software development. It enables businesses to move fast without compromising quality, as developers are not bogged down by repetitive, manual tasks and can reallocate their time toward more complex business challenges. Automated testing can also help identify bugs and coverage gaps early. As software release cycles accelerate, untested changes can lead to major disruptions. Automated regression, load, and performance testing ensure new code does not break existing functionality, even under heavy traffic or unusual user behavior. Continuous testing empowers sustainable development, reducing risks and preventing failures before they occur.

9. How can businesses collaborate with partners and stakeholders to drive innovation in testing and quality assurance?

Businesses must prioritize intentional collaboration across their ecosystem — with partners, stakeholders, and internal teams alike. As a foundational point, businesses should foster shared accountability for software quality by embedding testing earlier in the SDLC and aligning on metrics that matter to both technical and business outcomes. Additionally, it is important to regularly seek feedback from internal stakeholders and end users to ensure proper functionality and A-grade user experience (UX). Further collaboration with technology and consulting partners can also introduce fresh perspectives and frameworks that keep businesses at the cutting-edge of innovation. Quality assurance teams are responsible for quality control – everyone who participates in software delivery is responsible for quality assurance.

Ultimately, innovation thrives when testing is no longer a siloed function, but rather a cross-functional discipline informed by diverse insights and driven by a common goal: delivering high-quality software at speed.

10. Looking ahead, how do you see the future of testing and quality engineering evolving in the next five years?

With today’s business world being “dynamically disruptive” – largely driven by the rapid evolution of AI and the markets finding themselves in a state of adaptive flux – it’s difficult to say where exactly testing and quality engineering will be in five years. That being said, I think it’s safe to say that the industry will be even further automated and driven by AI – specifically agentic AI.

During the first part of 2025, AI agents have dominated headlines and professionals across every industry have expressed concerns about how this new technology might affect their work. However, it’s our stance that those who learn how to use agentic AI to their advantage stand to reap significant benefits. In the world of testing and quality engineering specifically, agentic AI is poised to assume much of the tedious and repetitive tasks that consume workflows – freeing up the time of testers and developers to focus on more strategic, complex business challenges. While the specifics of this 5-years-ahead future are unknown, the overall takeaway is crystal clear: the future evolution of testing and quality engineering will have enterprises innovating more and delivering better software, faster.

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