Q&A Summary.

You’ve had a long career in enterprise technology. What brought you to Tricentis, and what keeps you motivated in the field of software quality?

I’ve been in enterprise technology for over three decades, and what continues to excite me is how relentlessly this industry evolves. I started my career at Mercury Interactive, which was pioneering automated testing long before it became mainstream. We helped organizations shift from manual testing to scripted automation, a breakthrough that transformed how enterprises approached software quality. But after several acquisitions, that company’s focus fragmented, and innovation slowed.

When Tricentis approached me in 2021 to lead the Asia-Pacific business, I felt that same spark Mercury had in its heyday, but this time with even more potential. Tricentis was pushing a fundamentally new paradigm: a codeless, AI-driven, model-based approach to test automation. It solved the limitations of script-based systems that had become too slow and brittle for today’s highly integrated, fast-changing enterprise environments. Joining Tricentis in 2022 felt like déjà vu, but version 2.0, an opportunity to help organizations drive quality and performance in a way that truly supports digital transformation. Now with agentic test automation, we're into the world of autonomous cars.

For listeners less familiar with Tricentis, what is the company’s mission and role in the enterprise software ecosystem, particularly in the Asia-Pacific region?

Tricentis exists to help enterprises deliver quality at the speed of innovation. In practical terms, that means empowering every team to build, test, and deploy software efficiently, ensuring it is high quality, scalable, and ultimately delivers business value. The goal is not quality for its own sake, but to drive better business outcomes by ensuring digital initiatives don’t fail under pressure.

Across the APAC region, we are deeply engaged with two dominant themes: application modernization and digital innovation. Many large organizations are still running legacy systems that are monolithic, highly customized, and deeply entangled with countless other applications. These legacy environments carry massive technical debt, slowing innovation and creating risk every time a new update is introduced. When one component changes, it ripples across interconnected systems. That’s why an end-to-end approach to testing has become mission-critical.

At the same time, enterprises are in a race to out-innovate competitors through digital products and experiences. Software is now the business itself. The companies that innovate digitally, faster and safer, win. This is especially true in APAC, which is leading global digital transformation in many sectors. We’ve expanded rapidly in the region, from Singapore, Australia, and India to new offices in Japan, Korea, and the Philippines, to support that growth and ensure enterprises here can compete globally on both speed and quality.

Generative AI has made it possible to generate code at unprecedented speed. How is that changing the dynamics of software quality?

GenAI is rewriting the economics of software creation. When Anthropic’s CEO, Dario Amodei, predicts that 90% of code will be AI-generated within six months, it signals both an opportunity and a problem. If AI can generate code faster than humans can test it, the bottleneck simply shifts downstream. Testing and validation become the new constraint.

Research shows that a large share of AI-generated code contains defects or vulnerabilities. So while AI accelerates creation, it amplifies the importance of quality assurance. That’s where Tricentis plays a critical role, helping organizations ensure that rapid innovation doesn’t turn into operational chaos. The challenge is no longer how fast you can build software, but how safely and reliably you can deliver it at that speed.

You're coming from the enterprise software world, where mistakes are not allowed. It's not like you have an app where, oh, it doesn't work…

Actually, it leads me to that very famous article that I like to cite. It was published last year in Fast Company magazine. It’s entitled “Thanks to AI, the coder is no longer king: All hail the QA engineer.” I think that’s representative of the times that we are in. For decades, QA was treated as an afterthought, something to squeeze in at the end of a project if time allowed. But with AI generating massive amounts of code, quality assurance has become the new frontline of innovation. Without robust validation, organizations risk releasing faulty systems that can cost millions or even damage reputations overnight.

Today, QA engineers are no longer simply testers; they are the guardians of enterprise resilience. As code generation accelerates, it’s QA that ensures automation doesn’t turn into automation of errors. That shift in responsibility, from developers to quality engineers, represents one of the most profound cultural changes happening in enterprise technology right now.

How has the field of software testing evolved in recent years, and what does this shift toward “agentic” testing mean?

The evolution of software quality has been remarkable. It began with manual testing, where every test was performed by hand. Then came script-based automation, which allowed repetitive testing to be coded and reused, a huge leap forward in productivity. Tricentis then pioneered codeless model-based testing, which abstracted the business process from the underlying code, allowing teams to model workflows rather than write scripts. That was another paradigm shift.

Now we are entering what I call the era of agentic test automation, the equivalent of moving from driving a car to riding in a self-driving one. Agentic AI systems can autonomously plan, execute, and validate tests without constant human intervention. They understand goals, assess context, and make decisions dynamically. Imagine testing that continues 24/7, learning and improving on its own. It’s like upgrading from a fleet of taxi drivers to fully autonomous vehicles: productivity skyrockets, and human error plummets.

This shift doesn’t eliminate the human role; it elevates it. Instead of writing and executing tests, humans now define strategy, oversee results, and train the AI to think more intelligently about risk and coverage.

Many CIOs now consider QA bottlenecks a top business risk. Why has software quality become a boardroom issue?

Because software failures are no longer technical hiccups; they are existential business threats. In our Tricentis Quality Transformation Report, we found that quality gaps cost organizations, on average, over half a million dollars annually, while two-thirds reported being at risk of a software outage in the next year. Even more concerning, nearly half admitted they prioritize delivery speed over quality, and two-thirds acknowledged regularly shipping untested code… which is like playing Russian roulette. 

The consequences are visible everywhere. The global CrowdStrike outage earlier this year disrupted airlines, hospitals, and government systems worldwide. In Singapore, the former CEO of DBS Bank publicly admitted that four out of five of their major outages were caused by software bugs. When you connect those dots, you realize that quality isn’t an IT metric anymore; it’s a business survival metric.

I often say that shipping untested code is like playing Russian roulette. You might get lucky, but when the bullet fires, the fallout can destroy brands, careers, and entire customer relationships. That’s why quality is now a C-suite and board-level conversation, not just a function buried inside IT.

What are the biggest misconceptions business leaders still have about software quality and testing?

The most dangerous misconception is that quality belongs to the QA department. Many executives still treat software testing as a downstream task, something to delegate. But today, software is the business and quality cannot be an afterthought. That means quality must be owned at the board level. If your core systems fail, whether you’re a bank whose transactions stop processing or an airline whose flights can’t depart, those aren’t minor defects; they’re existential crises.

Another misconception is that traditional testing tools and processes are “good enough.” Manual and script-based methods simply can’t keep pace with the complexity of modern digital ecosystems. It’s like showing up to a Formula One race with a bicycle; you’re guaranteed to lose. Organizations need high-performance, AI-enabled testing engines to match the velocity of digital change. What got them here won’t get them there.

How does Agentic AI specifically change the speed and economics of software testing?

Agentic AI redefines testing speed by automating not just execution but decision-making. Traditional automation still depends on humans to define every test case, interpret results, and decide next steps. Agentic systems can analyze the system under test, identify coverage gaps, generate new test cases, and run them continuously, autonomously.

That transforms the economics of testing. Human-led testing scales linearly: more code means more testers. Agentic AI breaks that relationship. Once deployed, these systems can scale infinitely, running 24/7 without fatigue. They accelerate testing cycles, reduce defect leakage, and free human engineers to focus on higher-value analytical work. It’s a structural shift in how organizations think about speed, cost, and quality at scale.

Looking ahead, how do you see the future of QA evolving over the next few years?

The future of QA is autonomous, integrated, and business-aligned. As enterprise systems become more distributed and AI-driven, quality assurance will no longer be a discrete function; it will be embedded across the entire development and delivery lifecycle. Agentic testing will merge with DevOps, observability, and risk management platforms, creating a continuous, self-healing feedback loop between code generation and production performance.
We’ll also see a shift in mindset. QA leaders will evolve into “quality strategists” responsible not for testing tasks but for defining trust frameworks, compliance standards, and AI ethics within the development process. The organizations that master this transition will be the ones that can innovate without fear because they’ve built confidence into every line of code they deploy.

In short, the future belongs to those who can deliver speed without sacrificing safety. And that’s exactly the balance Tricentis was built to deliver.

What excites you most about leading Tricentis’ growth in Asia-Pacific?

APAC is at the forefront of digital transformation. The region’s enterprises are adopting cloud, AI, and automation faster than anywhere else, often leapfrogging legacy stages entirely. That makes it an incredibly exciting and challenging market for Tricentis. Our mission is to ensure these companies can sustain their innovation pace without compromising reliability.

Ultimately, what excites me most is the impact. When a bank prevents an outage, when a telecom avoids service disruption, or when a government system stays online during a crisis, that’s real value. We’re not just helping companies test software; we’re helping them earn and maintain the trust of their users. And that, to me, is the ultimate measure of success.

Beyond speed, what are the key advantages that AI brings to software testing?

Speed is certainly one of the most visible benefits, but it’s far from the only one. When applied to quality assurance, AI delivers greater compliance, better coverage, and smarter decision-making. For instance, Tricentis has been pioneering “agentic test automation,” which removes bottlenecks caused by manual processes. Instead of relying on humans to create, execute, and maintain test assets, AI can now automate much of that work. This not only accelerates delivery but also improves accuracy and consistency across releases.

A second area is what we call quality intelligence—essentially, knowing what to test and when. Traditional approaches treat testing as a blanket activity, but with AI, you can pinpoint what code has changed and which elements genuinely need validation. That means fewer unnecessary tests, reduced effort, and a faster path from code change to deployment. Finally, there’s intelligent test management. Business requirements often come in natural language, and AI can now translate those into structured test cases and scenarios. What used to take weeks of human interpretation can now be done in minutes, ensuring better coverage and reducing human error.

Have you seen organizations successfully embed agentic AI into their testing workflows?

Yes, although we’re still in the early stages. Most enterprises are currently running pilots and trials. Agentic AI is new and can sound almost magical, so naturally, teams want to ensure it’s tangible and trustworthy. That said, AI in quality assurance itself isn’t new—Tricentis has been embedding it in our platform for years.

A great example is Zespri, the world’s largest marketer of kiwifruit, based in New Zealand. They use Tricentis’ intelligent testing capabilities to maintain the quality of their ERP systems through features like self-healing test assets. When an ERP software update occurs, AI automatically reviews and updates the test cases affected by code changes. This prevents errors from slipping through, something that could otherwise cause production outages.

Contrast that with what happened to Haribo in Germany a few years ago. A glitch in an ERP update disrupted global gummy bear shipments—a reminder of how critical continuous quality assurance can be. AI mitigates exactly this kind of risk by ensuring systems adapt dynamically to change, rather than breaking when updates occur.

That’s a vivid example. But in regulated industries, trust and compliance are paramount. How does AI help reduce these risks?

AI can be a powerful tool for strengthening compliance and trust. Take the recent CrowdStrike incident, where a single untested line of code caused a global outage. The real question for every CIO is: how do you prove that your code was fully tested before release?

That’s where technologies like Tricentis’ acquisition, SeaLights, come into play. SeaLights detects code changes and verifies whether tests have been executed against those changes—whether they’re unit, API, or end-to-end tests. It gives organizations a full audit trail to show regulators exactly how they validate software quality. In regulated sectors such as finance or healthcare, this level of traceability is a game changer.

Our Transformation Report found that nearly two-thirds of organizations admit to pushing untested code into production. That’s a staggering number, and it underlines why AI-based validation isn’t optional—it’s essential for compliance and operational resilience.

What should CIOs and CTOs consider when building their AI-based QA roadmap?

The first step is education and exposure. Technology leaders should engage actively with the global QA and AI community—through conferences, expert discussions, and yes, even podcasts. Learning from others’ experiences can accelerate understanding and prevent missteps. Tricentis, for instance, will host its flagship AI Tour in Singapore on September 4, 2025, to share case studies and best practices around AI-driven testing. Events like these are valuable because they combine theoretical insights with real-world implementation lessons.

The second step is collaboration with specialists. Partnering with a company that focuses on AI for quality assurance helps organizations grasp not only the technical considerations but also the cultural and procedural changes required. These early consultations help leaders map out their transformation strategy, identify quick wins, and build internal momentum. This is not just about buying tools—it’s about reshaping how quality is delivered across the enterprise.

How can organizations balance innovation with risk, especially in highly regulated environments?

That’s one of the toughest challenges enterprises face today. Many of our clients tell us their regulators worry about data exposure when interacting with public large language models. That’s why Tricentis supports the Model Context Protocol (MCP), which enables enterprises to bring their own AI models into the workflow securely.

MCP acts as a universal connector. It lets organizations use private, internally trained LLMs while maintaining compliance and data sovereignty. For regulated industries like banking or government, this approach means they can still benefit from AI-driven automation without sending sensitive data to third-party models. In short, innovation doesn’t have to mean sacrificing control.

You mentioned earlier that people fear being replaced by AI. What skills and organizational changes are needed to adapt successfully

Adaptability is the first and most important trait. Employees who embrace AI as an augmentation tool, not a replacement, will thrive. The fear of being automated away is natural, but the reality is that AI elevates rather than eliminates human work. It takes away repetitive tasks, allowing people to focus on higher-value problem-solving.

The second essential capability is prompt engineering. Understanding how to communicate effectively with AI is now a core professional skill. For example, Tricentis recently integrated MCP with our NeoLoad performance testing platform. Users can now interact with NeoLoad in natural language—no need to master complex technical syntax. You simply tell it what you want, and it generates the corresponding tests. This democratizes testing, allowing business users and non-developers to contribute meaningfully.

Learning to structure prompts is similar to learning a new language. Precision and clarity matter. It’s not about coding; it’s about communicating intent in a structured way that AI systems can interpret accurately.

Looking ahead, where do you see enterprise quality assurance in the next five years? Will manual testing disappear?

Manual testing won’t vanish completely, but its share will decline dramatically. As AI-generated code and continuous deployment become the norm, manual approaches simply can’t keep up. Intelligent, automated testing will grow exponentially because the volume of code changes is increasing faster than human teams can manage.

However, there will always be areas, particularly in business process validation or user acceptance, where human judgment remains vital. Those final checks ensure that systems not only function correctly but align with business objectives. Yet even these processes will be supported by AI tools that reduce preparation time and guide testers toward critical paths.

In short, we’re not eliminating human testing; we’re elevating it to a strategic oversight function while AI handles the execution and adaptation.

For leaders still hesitant to adopt AI-driven QA, what advice would you give?

Every new technology cycle comes with hesitation. The key is to view AI adoption not as a leap into the unknown but as an incremental evolution. No one today can claim to be an absolute expert in this field; we’re all learning and adapting together. But doing nothing is a greater risk. Organizations that delay AI adoption in quality assurance risk being left behind—both technologically and competitively.

The good news is that AI carries a strong business case. Projects with an “AI” component tend to receive faster executive approval and dedicated budgets. Leaders can leverage that enthusiasm to fund transformation initiatives that might otherwise struggle for attention. In many ways, this is a once-in-a-generation opportunity for QA leaders to redefine their strategic role within the enterprise.

Beyond testing, what broader technology trends do you think will shape enterprise software innovation?

Agentic test automation will certainly be one of them, but so will quality intelligence. In an AI-driven world, understanding what to test and when will become as critical as the testing itself. Many enterprises still waste resources running tens of thousands of regression tests simply because they lack visibility into which areas of code were actually affected. That’s inefficient and costly.

With intelligent analytics, you can identify exactly where to focus your efforts, dramatically reducing time and cost while increasing reliability. It’s a paradigm shift from “test everything” to “test what matters.” As software ecosystems become more complex, this precision will define the next competitive advantage in quality assurance.

Transcript.

Peter: Damien, thanks for joining us.

Damien: Thanks, Peter, for having me on your show.

Peter: Starting with a short introduction, let's start with your journey. What led you to your current role? What motivates your work in software quality today?

Damien: Well, great. A quick introduction to myself. I'm Damien Wong, Senior Vice President for Tricentis across the Asia Pacific geography. I joined Tricentis in 2022. So, in terms of my career history, I've been in the enterprise technology industry for just over 30 years now. I like to say that I started my career when I was just five years old. But who's kidding, right? Otherwise, it would give my age away. Anyway, one of the things that I really like about this industry is that it never ceases to innovate and change. The last 20 years of my career has been spent in enterprise software. I just love the pace at which this industry moves. There's never a boring moment in enterprise technology.

20 years or so ago, I joined a company called Mercury Interactive. We created this category to help organizations move from manual testing to script-based test automation, which was what I would consider first generation of automated testing. It was revolutionary back then, Peter, and was very fulfilling knowing that we were helping organizations drive quality and performance of their software portfolio. But unfortunately, Mercury was acquired and subsequently changed ownership or changed hands a number of times. That was a bit of a shame, because with focus, it allowed the company to thrive. But once that focus was lost, the level of innovation that was needed to address customer's evolving needs started to diminish. That's why, in 2021, at the end of 2021, I was approached by Tricentis to lead the Asia Pacific business.

Tricentis just reminded me of all the wonderful attributes that Mercury had in its heyday. This laser focus on helping customers drive better business outcomes using software quality and performance was something that I could really identify with. So the only difference was, we were taking enterprises forward with a radically different approach, a fully codeless AI-driven model-based test automation approach. It's a bit of a mouthful, right? But what it did was, it addressed the current needs of enterprises—given the fact that many of them were starting to struggle to keep up with using script-based test automation, given that the whole game had changed with the new technology architectures that they had to deal with, and the complex application landscapes that were highly integrated. In summary, it was like deja vu, Peter. It was like a new and improved version 2 of mercury. That's really the reason why I got excited and joined the company in 2022.

Peter: Thanks, Damien. It makes sense. Could you briefly describe Tricentis' mission? You already gave a little bit away. So what's its role in enterprise software, in the enterprise software ecosystem, particularly here in the APAC region?

Damien: We would definitely be happy to share that. I think I did allude to the fact that Tricentis' mission was around driving quality for software in organizations. I mean, essentially, it's four things that we do. We want to empower every single team in organizations to deliver efficiently, high-quality software. The third thing is: do it at speed and scale, but not just for the sake of driving quality. Finally, the fourth thing is to drive better business outcome. So empower every team to efficiently deliver high-quality software at speed and scale to result in better business outcomes. I hope that kind of encapsulates everything that we aspire to do. In short—because it's a long mouthful—we drive quality at the speed of innovation. That's what I always like to say. Because organizations are driving digital innovation at speed, but then we ensure that there's quality as they drive speed to innovation. So that's really what we do.

And if you look at the role that we play in the enterprise software ecosystems, I think the thing that I keep hearing from the CIOs that I speak to, the IT leaders and business leaders that I speak to, is really around two key themes. The first theme is application modernization, and the second theme is digital innovation. First, application modernization, Peter, it's really that many organizations have all these legacy applications and systems that need to be modernized. They generally are large, monolithic applications that are very brittle, and they're not very good at supporting innovation, right? So while a number of custom developed, many of them are also packaged systems. You spoke earlier that you have a long history with SAP. Well, SAP is one of those applications that is commonly found in large enterprises as well. The technical debt that we see accrued on these apps over the years is something that organizations are trying to address. Because if you don't modernize them, then this can impede your innovation, right? It can also be very risky for organizations to not have them be able to meet evolving customer needs and demands.

The other issue is that these apps have also been integrated with many other growing portfolio of applications that they have acquired over the years. This is the basis of all those business processes that power those organizations. So making a change to an app within that portfolio oftentimes results in a ripple effect, and unintended consequences are happening if you're not careful. So this ability to take an end-to-end approach to testing is so critical today in ensuring that organizations are able to successfully update, upgrade, and refresh some of these applications that they have.

I mentioned earlier the second point was digital innovation, Peter. Businesses generally compete today on digital innovation, right? Software is the business for many organizations. So how an enterprise innovates digitally faster against the competition is a key advantage for many of them.

Peter: Absolutely.

Damien: I'm sure you've heard of this term, Gen AI, right? All of us are now hearing all the time about Gen AI, right, Peter? Gen AI has actually made this phenomenon even more acute. Recently, I was reading about the CEO of Anthropic. I'm not sure if you're familiar with Anthropic, but they are, today, fairly synonymous with this whole Gen AI domain. The CEO of Anthropic—who was also the Vice President and Head of Research for OpenAI prior to Anthropic—a gentleman called Dario Amodei, shared in recent times that, in three to six months, 90% of all code will be AI-generated. He said, in 12 months, he predicts all code will be AI generated. So that's just mind blowing. But it's also game changing, right? If you think about it, code can now be generated automatically at speeds unheard of previously. This creates this phenomenon. There's a bottleneck that now is forming and being created. Because you can generate all this code at such speeds, you have to now validate and test that code. So this is especially important because most of the research that's out there have shown that a lot of the generated software code actually comes with bugs and defects.

So I think that's one of the things that we obviously are there to try and help organizations address in terms of trends. We have a unique role to help the organizations drive quality and speed of innovation, especially in the APAC geography. So we've grown our footprint, Tricentis' footprint, across APAC tremendously. We started with offices in Australia, in Singapore, and in India. Over the last couple of years, we've actually grown our presence to cover Japan, Korea, and even an office in the Philippines. This is because, if you look at the landscape here, APAC is really leading the way in many cases in digital transformation and innovation. So it's a very exciting time to be out here in Asia, in APAC. Tricentis is also obviously very excited to play a huge role in helping organizations succeed digitally with what we do.

Peter: Excellent. Yeah, actually, you're really making a point in terms of testing. Because, a quick example from my end: we’re just now implementing one additional process, and, as I said, we are working with a SAP implementer. We are implementing one additional process. Configuring that process takes just two, three days. Testing it, making sure that everything is fine from logistics, to finance, to output documents takes one and a half months.

Damien: Wow!
Peter: So the essence is really into testing, making sure that there are zero mistakes. Especially, you're coming from the enterprise software world as well, mistakes are not allowed. It's not like you have an app where, oh, it doesn't work. Well, I don't like it. No, you might be losing money.

Damien: You got that right. I mean, for the company, they might be losing money. There's reputational risk. For the individual, they might lose their jobs, right?

Peter: Oh, yeah.

Damien: So that’s the risk of it. Actually, it leads me to that very famous article that I like to cite. It was published last year in Fast Company magazine. It’s entitled “Thanks to AI, the coder is no longer king: All hail the QA engineer.” I think that’s really representative of the times that we are in.

Peter: Yeah, actually, that makes sense. Well, as you said, you have to ensure the quality. You can't trust a code, or you can't trust an AI to write a code. It's the same thing.

Damien: Yeah, I think that's a key to it, right? It's a tool. It's a great tool in technology, but then there comes implications that we have to deal with.

Peter: Yeah, right. Okay. So how has quality assurance in software changed over the past five years? Of course, now we have the big change with AI, but how did it evolve over the past five years? Would you be able to give an intro?

Damien: Yeah, of course. I can share a little bit about how this has changed over the last few years. I did mention that article. I think that article is welcome for all people involved in the quality assurance space. Because, in the past, quality assurance was seen as an afterthought, right? If I have time, I'll do it. And if I don't, then, okay, let's see. So I think it's putting a whole spotlight on this whole domain of quality assurance. But in the time that I've been here and also looking back at Tricentis' history, we've seen the software quality assurance space evolve. From the traditional way of manual testing, it's gone to script-based test automation, which worked very well for a time when things moved a lot slower, and there were less systems that were integrated to power those business processes.

Then Tricentis brought to the table codeless model-based test automation, which was again a paradigm shift. Because we are no longer talking about coding scripts. You are now looking at modeling business processes and being able to abstract the business process from the underlying technologies, which was fantastic because it took away a lot of the pain associated with traditional script-based test automation. And then, now, we're starting to move into this whole domain of agentic test automation. So if you want to put it in layperson's terms, Peter, you can think of manual testing almost like running, and a script-based test automation like you have a bicycle now. So you're cycling. You can get there faster, but you're still using your manual effort to get there. Then moving on to driving, so now you have codeless model-based test automation. You have a car, and the car can take you to places further and faster than you could with either running or cycling. Then now with agentic test automation, we're into the world of autonomous cars, autonomous vehicles. So I would use that as a way to help people understand this evolution that we're seeing in the software quality assurance space.

Peter: Very good intro, Damien. Thanks. Then let's move on to the problem: QA bottlenecks and leadership risks. Our first question I have here is: I read you've mentioned QA bottlenecks are now a concern at the C-level suite. Why is that the case today more than ever before?

Damien: It definitely is. That's something that we're constantly trying to ensure we do. We're trying to address concerns at the senior executive level, business leader level. Maybe if you look at the research we are also conducting—we published recently our Tricentis Quality Transformation Report, which is based on surveys that we have conducted with organizations—some of the statistics are pretty surprising, Peter. If you look at the responses, quality gaps are costing organizations, on average, more than half a million dollars a year. That's a lot of money as an average figure. Two thirds of those organizations that were surveyed said that they are at risk of a software outage this year itself, and nearly half also prioritize delivery speed to software quality. So, to them, it's like, "Okay, let's roll out the software faster." Whether there's the right level of quality, that's secondary. But first, roll out at speed.

The last point I want to point out is—I think the most concerning—nearly two thirds of those organizations admit to regularly shipping untested code. This is often because of the demands for speed. So think about it. You're shipping code out there that is completely untested. That is crazy, right? So we've seen, as a result of this, many organizations having these spectacular software outages. There are companies that splashed on the front pages of newspapers. Examples that were in recent times are like CrowdStrike, the outage that happened globally. I'm not sure if you're familiar with the CrowdStrike outage, Peter, but that was spectacular. Also, we have in Singapore, for example—some local examples—the former CEO of DBS Bank actually publicly said that four out of five of their major outages—that was two years ago—were caused by software bugs. Think about the impact that these software quality issues are facing, the fact that they're not testing. So if an organization prioritizes speed over quality and ships untested code, I like to say think of it like playing Russian roulette. For those who are not familiar, Russian Roulette, you put a bullet into a gun, you point it in your head, and then you spin the barrel. You hope you don't get the barrel with the loaded bullet. But that's exactly what it's like, right? You're lucky if the code works. But what if it doesn't? This is why it's become such a C-suite concern. Because C-level executives are now focusing on this quality assurance bottleneck that is forming, and they need to ensure it gets unblocked. Those who don't do that — well, let's just say I wouldn't want to be in their shoes if or when stuff hits the fan.

Peter: Yeah, absolutely. Someone's got to take the responsibility for those mistakes.

Damien: 100%. Buck stops with them.

Peter: And then especially sectors like finance—you mentioned finance—health and government. I guess then traditional QA is failing to keep up with the demand nowadays, right? I guess that's where you come in to to help them.

Damien: Yeah, Peter. I think you've highlighted a few examples of highly-regulated industries: finance, health, government. I think it goes beyond those industries where organizations are generally facing this whole challenge because they are using or adhering to traditional quality assurance techniques and tools. I think all the reasons I've mentioned earlier, traditional QA approaches just don't stack up anymore.

Manual testing and script-based test automation in this age of Gen AI, I think it's like trying to bring a bicycle into a Formula One race, Peter. Imagine you turning up at the Formula One race, and you bring this fancy bicycle expecting to compete. The speed and frequency of change that enterprises face today is just growing exponentially. Hence, what I think got those organizations to where they are today is definitely not going to get them to where they need to get to tomorrow. So if you're being forced to compete in a high performance vehicle race, you're going to need to show up with a high-performance vehicle, or you're just not going to compete.

Peter: Yeah, that makes sense. So there are misconceptions out there, right? Would you have some examples for common misconceptions business leaders still have about QA and software testing and their impact on their businesses?

Damien: Oh, I hope you have enough hours in the podcast for me to go through them, right? But I'll just cite some of the common ones. A lot of the business leaders that I speak to still think that software quality should be owned by the software QA team that resides in the IT department, and they tend to delegate that down whenever we have a conversation. The reality is that quality needs to be owned at the board level. It is a board-level topic because it has company-wide existential impacts—given that in today's world, software is the business. I'm not sure if you recall Marc Andreessen who is the co-founder of Andreessen Horowitz—one of the largest private equity firms and VC firms in the world. He wrote the book "Software is eating the world." But today, that's come true.

Software is the business, and quality cannot be an afterthought, right? Because if you don't do the right thing and take ownership at the board level, then no one's going to place the right level of focus and emphasis. Think about it: if you are an airline and a software glitch prevents your planes from flying, or if you're a bank and your software outage prevents your customers from making financial transactions, those are no longer just nice to have. Those are existential issues. Wouldn't you agree, Peter?

Peter: Yeah, that's correct. Well, at least there is Agentic AI now. I guess your solution will be filling a big gap there, or you are already filling a very big gap, yeah.

Damien: We hope so.

Peter: At least, what I see, it's expanding. The company is expanding. I'm very glad to see it. So talking about your solution, entering Agentic AI, could you explain what Agentic AI means in the context of software testing? How does it differ to the traditional automation?

Damien: Yeah, 100%. I mean, everyone is putting up all these buzzwords, right? Sometimes it's confusing for people hearing all these new buzzwords. Agentic AI is definitely a new buzzword, but I think it's a very relevant buzzword. Let's look at the definition of what Agentic AI means. Agentic AI generally refers to an AI that's able to autonomously—meaning, on its own—make decisions and take actions to achieve specific goals, often without human oversight. And so I think that's an important thing to take note of. It can make decisions and take actions on its own, often without humans intervening. That's the whole concept of Agentic AI.

In the context of software testing, that's where we are looking at agentic test automation. Because that's really the Nirvana that all of us have been looking toward for a long time, and it's finally becoming a reality, which is fantastic, right? Then you compare that with the traditional definition or context of test automation. Traditional test automation is essentially taking manual development of test assets based on certain test requirements and test cases. And you've done software projects before, so you know about this, right? Someone defines what the software needs to do. Those are the requirements. Then based on the requirements, you define, what do you need to test? Then if you're automating it, you develop those test assets that actually execute on the test to validate those software actually does what it's supposed to do. In the traditional way, you do it manually. So think of it in the context of driving. It's knowing where you want to go, and then driving there yourself. Agentic test automation is then akin to an autonomous car driving you to your destination once it knows where your destination is. It will do it with all the constraints, et cetera that you define, and then it will just do it for you.

So I think, with agentic test automation, the speed at which testing can be executed will just accelerate exponentially, and all this bottleneck that's based on human effort is actually going to be reduced significantly. I like to use analogies. I'll give you an analogy. Think about a situation where you're a taxi company owner. I own a fleet of taxis, right? In the past, I would be dependent on the number of taxi drivers I had and the number of hours they would drive in order to generate the kind of revenue for my company. So now you go into the world of autonomous vehicles. I am no longer constrained by the number of taxi drivers I have and the number of hours they can drive. The cars can drive on their own. They don't get tired. They can drive 24/7 if they need to. I think that's going to be absolutely game changing. So if you look at that analogy and apply that to software quality assurance, that's really going to be the same kind of paradigm that we're looking at. So I hope that kind of explains this whole concept.

Peter: I guess so. Yeah, that makes perfectly sense. I think it also brings us to the next topic: what the core benefits are. One is speed. I can confirm speed. It's very good. If we can increase speed and software testing, that's what we all need, right? If we can shorten projects and still deliver the same quality, that's perfect.

Damien: Well, I've been told in the past that testing is a speed bump, right?

Peter: Kind of, yeah. You have to go back a lot and rework.

Damien: Yeah.

Peter: So what other benefits are there, besides speed?

Damien: I think we can touch on it. Yeah, besides speed, compliance, better coverage, et cetera. Maybe I'll name a few things. If you look at AI and how it's applied on the domain of quality assurance, Tricentis is obviously a specialist in this domain. We're applying it to areas such as, I mentioned earlier, agentic test automation. Removing the bottleneck for humans and then being able to speed up the creation and execution of test assets, which I think is one of the big benefits that you get. You get a lot of speed. You get a lot of accuracy as a result of that.

Then you also have that whole domain of knowing what to test, when to test. So that's quality intelligence. Because good that you want to have a car that drives you to where you want to get to. But first, you have to define: where exactly do I want to get to, and how do I really want to get there? There are many ways to get there. Faulty intelligence effectively tells you, okay, what's exactly changed in the code, for example, and then what do you really need to test? Or do you need to test anything as a result of that? That reduces the effort required for testing, the resource required for testing, and increases your speed to deliver innovation.

Another area, a third area, could be around intelligent test management. We all want to ensure that we're getting requirements from our business owners or business stakeholders. Oftentimes, the requirements come in the form of natural language. This is what I need to get done. How do you automatically translate that into, "These are the test requirements; these are the test cases and test scenarios, et cetera?" Is this comprehensive? So having something that's able to read from natural language, all of that, and be able to translate that automatically into the relevant test cases, test requirements, test scenarios, and be able to create those test assets that support those requirements, I think it's going to be fantastic. Because it ensures that you have better coverage, and you're able to do this at speed and scale—something that, in the past, a business analyst would have to spend days, weeks, months to try and translate. I'm sure you've seen that in the past. So imagine if you can have AI now be able to do that for you in a matter of hours or even minutes, right? How wonderful that would be.

Peter: Yeah, it sounds wonderful. Yeah, you have to give it a lot of trust as well, right? So you need to trust the solution.

Damien: 100%.

Peter: So are there any examples you could mention where organizations have successfully embedded Agentic AI in their workflows?

Damien: Yeah, I would say that Agentic AI as a category is very new, and many of the enterprises that we're working with today are doing this in terms of pilots and trials. They like it. But like what you've just said, it sounds new. It sounds like magic. So a lot of people, when you're encountered with this type of disruptive technology, they like to actually get their hands around it and ensure that there's no smoke and mirrors underneath, right? So the pilots and trials are definitely happening. But having said that, AI in quality assurance, Peter, is actually not new. I'm sure you would have heard that AI has been used in QA for a while now. Tricentis has embedded AI into our portfolio for a very long time. So I'll give you an example of maybe a company in the Asia Pacific region. I don't know if you've heard of a company called Zespri. Zespri is—

Peter: Yeah, of course. The kiwis.

Damien: Good. Yes, kiwi fruit. Absolutely. So, New Zealand company, the world's largest marketers of kiwi fruit, right? Yeah, so wholesome company. It's a wholesome company. So Zespri uses intelligent capabilities in the Tricentis software to help them with ensuring quality of their applications through some capabilities like self-healing of test assets. So imagine this: if they have an ERP software update that came in, it would then be able to review, upgrade, and update automatically their test assets. Because imagine this: if a code change came in and it broke all my test assets as a result, then I might unwittingly miss out on a software defect, right? I wouldn't have found it. It might have a false positive or false negative result in my test. And as a result, it might cause an outage. Knock on wood. That's not a good thing. Because for me, that is a personal disaster. My eldest daughter, she adores kiwi fruit. She has at least one every single day, so I cannot imagine her not being able to get her daily fix of kiwi fruit.

You're German. I actually want to use a German example of something that happened like this a number of years ago. There was a company in Germany called Haribo.

Peter: Okay. Yeah, Haribo.

Damien: Haribo—yeah, I'm not sure if you are familiar with them—is, I believe, the world's largest manufacturers of gummy bears. I've got a sweet tooth, so I do eat gummy bears from time to time. So because of a glitch that they didn't detect in the ERP software upgrade, it actually caused a global gummy bear outage. So I would say that that was a true disaster, definitely, for all gummy bear fans. But imagine the company not being able to ship and fulfill on all the demand that was there, right? So that's an example of AI in action and how it can be used to help in real-life situations.

Peter: Very impressive. I had no idea. Both from countries—I'm a German, but I'm also a PR of New Zealand.

Damien: Yeah.

Peter: Sort of the most prominent brands.

Damien: I've hit the home, yes?

Peter: Yeah.

Damien: It hit close to home.

Peter: Yeah, very interesting. That's already some pretty, big examples, actually. Yeah, very impressive. I had no idea.

Damien: One positive and one not so positive.

Peter: Yeah, I really had no idea. Okay. Thanks, thanks, Damien.

Damien: Sure.

Peter: Actually, going into the next question—this is probably already partially answered—how does AI help reduce compliance and trust-related risks, particularly in regulated industries?

Damien: That's a great question, and that's a question I often get. How do I help address some of the compliance risk because I'm a bank, or I'm a healthcare company, or whatever, right? I think AI can help, in many ways, address some of these risks.

I'll give you an example. I mentioned earlier CrowdStrike had a global outage, right? That global outage was caused by a single line of code not being tested before it was rolled out into production. I don't know if you were affected by this, Peter, when the CrowdStrike situation happened. Maybe you weren't using Windows machines, et cetera, but many of my colleagues actually suffered blue screens because they were using Windows laptops. That, I think, was a situation where many organizations worry. How do I know if there is a code change or new piece of code that has actually been completely tested before it is rolled out? In most organizations, there is no way to confirm that it has actually been tested. A software developer can say, "Look, I've tested it." But in the past, there's just no way you could actually confirm it and validate it independently, right?

That's why Tricentis acquired a company called SeaLights last year. SeaLights allows us to detect if a change has been made in the code and then also detect if tests have been run against those code changes. And if a test has been run, what kind of test? Is it a unit test, an API test, et cetera, et cetera, right? This kind of capability allows organizations to tell their regulators, "Hey, I can show you how I do it, and I can guarantee that I fully tested my code before it is released into production." So I think that is one of the key ways that organizations can help to meet regulator demands. And remember, when I shared earlier the Tricentis Quality Transformation Report finding, that two thirds of organizations admit to regularly pushing untested code into production. So that should be a big worry for any regulated company. Wouldn't you agree, Peter?

Peter: Yeah, absolutely. Yeah, it is very scary.

Damien: It is.

Peter: I had no idea about that actually before our talk here. So far, I've already learned a lot. That's very good.

Damien: I'm glad. I'm glad you are learning. Yeah, good.

Peter: Let's move on to the implementation, building the roadmap. What should CIOs and CTOs consider when starting their AI-based QA transformation? What are the practical first steps for them to take?

Damien: Great question. There are two things I would advise tech leaders to consider when they are starting this journey. I think, number one, attend conferences. Listen to podcasts. Speak to industry experts and peers who are also embarking on this journey. Definitely listen to the Digital Asia Podcast, right? Peter, I'm sure you will agree with them, yeah?

Peter: Thank you. Thank you. Thank you, Damien.

Damien: Yeah, 100%, right? I mean, this is something that Tricentis believes is important. That's why we are having our flagship AI tour event in Singapore on September, the fourth. So if you are in Singapore around that time, definitely sign up and attend the event. We'll cover this exciting topic in detail. So anyone listening in, if you're going to be in Singapore on September 4, 2025, do plan to attend the Tricentis AI tour here.

The second thing that I would advise is: come and speak to a company that specializes in the domain. Completely unbiased, of course, Peter. Completely unbiased. I'm talking about company that actually is able to do this. It starts with the letter T. Anyone come to mind, Peter?

Peter: Well, I think we have you in the call here, right?

Damien: Yeah.

Peter: It is okay to present this forward.

Damien: We encourage you to come and speak to Tricentis, obviously.

Peter: This is why we have you here, right? We want to feature you because we are actually independently impressed by what Tricentis is doing. That's independent marketing here. Very clear.

Damien: Great to hear. No, I was just kidding, right? I mean, you can obviously speak to different organizations. But we do believe that speaking to people who do specialize in this and have a focus around it will allow you to better understand the domain, some of the challenges, some of the implications and considerations. As you start this journey, I think it's important to know what you're getting into, and what are some of the things that others are doing to also do the same thing, right? I think that is going to be critical as the CIOs and CTO start this transformation.

Peter: Yeah, okay, makes sense. How can enterprises balance innovation with risk? We touched the topic a bit earlier, right? Being able to trust new solutions. But how is it especially handled in highly-regulated environments?

Damien: Fantastic question. When we talk about AI in regulated environments, many of our customers tell us, "Hey, look. My regulator is very concerned about exposing your data to public large language models, and so on and so forth." That's actually one of the reasons why Tricentis introduced MCP support—model context protocol support. MCP, for those who may not be familiar, is essentially — think of it as a standard that allows you to plug into just about any AI framework. This allows you to bring your own AI and use your own AI. So if you've got your own private large language model that you've been developing, as long as it's MCP supported, you can plug it in. Then it will allow you to utilize your own context, your own training data, and so on. So I think this is going to be an important consideration. Because, while you all want to leverage the innovation that's there for regulated industries, this becomes a big consideration. I hope that helps, because this is one of the key things that organizations have been asking for.

Peter: Yeah, it's a key thing for not just highly-regulated environments. Everybody is concerned about it.

Damien: That's true.

Peter: Uploading their data to somewhere you don't know where it actually ends up. Even for private people, right? I'm running my personal AI as well here on my MacBook. I keep it like that.

Damien: That's a good idea. Otherwise, you might find that whatever you are using actually lands up in the public domain, right?

Peter: Yeah, that's something we don't want. Okay, okay, thanks, Damien. So what skills and organizational changes are needed to truly benefit from Agentic AI?

Damien: It's an interesting question, especially because people in organizations are also worried. Because are they thinking, "Is this going to replace me? Do I have a job in the future, et cetera?" I think number one is adaptability. It's not a skill, but a trait. If you're not going to embrace the technology, I think it will, at the end of the day, make you obsolete. You need to accept that AI is actually here to stay. Embrace that, and then don't be afraid. So learn how it can augment you, make you more effective, make you better. Not be afraid that it's going to replace you and fight it. I think that's the first thing.

The second thing is this whole concept of prompt engineering. I think it's a skill that all professionals need to start to learn. I think, as an example, one of the concerns some of our customers have when looking at our performance testing platform—it's called NeoLoad—is whether they would be able to learn how to use it technically. But now we've launched MCP for NeoLoad. So with MCP for Neo load, you can use natural language prompts now and just get results like you would a professional. So you no longer have to learn the tech and then be able to do some of the more technical stuff in order to get what you need. Right now, you can just speak to it, communicate with it like you would a human, and say, "This is what I want. This is how I want you to do it, et cetera," and it will give it to you. So imagine how wonderful that is. I truly think that that is going to be important. But that's also based on prompt engineering. You need to be able to understand how to communicate and speak. Even with humans, Peter, you and me, we're speaking in English now, right? But English may not be our first language, right? So we have to be careful how we phrase things, how we position it, and the terms we use, et cetera, so that we don't misunderstand each other. So prompt engineering helps you do it.

Peter: Okay. Thank you, Damien. Very good. Then we're coming to our last section, future outlook and advice. So where do you see enterprise QA heading over the next three, five years? Where do you see this whole industry moving to? Will manual testing entirely go away? What do you think?

Damien: Yeah, I'm not going to scare people who are in doing manual testing as a job today, and say that it will go away. I don't honestly know. I think I don't know if it's going to go away completely. I am sure that the need for intelligent, automated testing is going to go up exponentially. So if I'm a betting man, I would start to scale up on how I can get into that field. Enterprise quality assurance, I believe, will be inundated with this deluge of code that is being generated by AI. And along with this barrage of updates and upgrades to existing applications that we have, like your ERP systems, et cetera, I think it's impossible to deal with all this manually. Again, I'm not going to make a prediction and say manual testing is going to go away completely, but I will make the prediction that intelligent, automated testing is going to go up exponentially.

Peter: Yeah, I mean, it makes sense. I think my opinion is: there will still be fields where manual testing will be required. Like what I mentioned earlier, testing a process at the end, where actually the business has to accept the process as working. They have to validate financial data, logistics information, where they then actually have to sign off and say, "Okay, we accept it as it is here"—assuming that all the technical tests have been performed beforehand.

Damien: Completely. Although I did hear in a recent interview, that bank CEO, a large bank CEO, just said that the board thinks that AI can replace the CEO in the future as well. So who knows who will be signing off on those at end of the day?

Peter: Could be. Yeah, you're right. Well, let's see. I mean, this is all in the very early stages, the AI. Let's see.

Damien: Oh, yeah.

Peter: Let's see what our kids will experience at some point in time. Yeah, we just have to make sure they get the right education to do something which cannot be.

Damien: 100%.

Peter: So what advice would you give to tech leaders who are still a bit hesitant to adopt AI-driven QA?

Damien: Well, anything new is always going to come with a little bit of hesitation, right? Whenever I learn something new, I always also am excited but I'm also concerned. I think that's the kind of reaction that most people would have. So it depends on the balance of that. I think the important thing for tech leaders to understand is that everybody is still learning and adapting at this stage. No one can say they are the foremost expert, and they know exactly what they're doing, right? But having said that, virtually, all enterprises have said that they're going to be adopting AI for quality assurance in the near future. So not planning to do anything with AI in quality assurance is probably not a good strategy, because you risk being left behind. The good news that I think tech leaders can also celebrate is that anything that now comes with the AI label is going to create C-suite attention and C-suite investment. So I would leverage that it will drive some initiatives because there's a good likelihood that it will get funded. And from what I see, this is going to be very helpful for tech leaders, for their organization and, actually, in my opinion, for themselves, too.

Peter: Excellent. Makes sense. Getting fast approvals is always something we, tech guys—

Damien: Yeah, tell me about it, right?

Peter: Okay. Well, I have one last question. Are there any trends you're personally watching that could shape the future, shape the next wave of enterprise software innovation—not limited to QA and testing? I mean, in general.

Damien: Yes, I mean, obviously, I'm going to have a QA slant here. We already spoke about agentic test automation. I think that's going to be very big. It's a trend that is very much gaining momentum. The other area I also mentioned earlier is quality intelligence, yeah, Peter. So I think it's not just about automating the testing. It's also about knowing what to test, when to test it, and ensuring that it has actually been tested, right? I mean, these are important things to know. So when you don't know what needs to be tested, you're going to default to either, number one, testing everything, or, number two, testing nothing if you don't have enough time and resources to do so. That's not a great approach, right? Either approach is not good. Because if you are going to the former approach of testing everything, many organizations have thousands or tens of thousands of regression tests, for example, that they need to run. It's a huge waste of time and money just testing everything because you don't know what needs to be tested, right? But if you can then change that situation and know exactly what you need to test, you can find that it takes a fraction of the time and effort to do so. And I think that's something that organizations are going to increasingly focus on. I think that's a trend worth looking at. Wouldn't you agree, Peter?

Peter: Yeah, absolutely. That's very well aligned with my thoughts.

Damien: I'm glad to hear that.

Peter: Yeah, amazing. Okay, okay. Very, very good. Very good to learn from you here today. Amazing. Thanks. Thanks again to Damien Wong and the team at Tricentis for this good interview.

Damien: Thank you, Peter, for having me once again. Yeah, I hope to join you again in the future for future podcasts.

Peter: Yeah, I would be glad to do so. Thank you. All right.