Name: Charting the Future of AI-Driven IT Operations in Asia Pacific
Uploaded: 2025-11-13T04:06:05.849Z
Duration: 50 min 8 s
Description: Charting the Future of AI-Driven IT Operations in Asia Pacific

Transcript for "Charting the Future of AI-Driven IT Operations in Asia Pacific": Charting the future of AI driven operations in Asia Pacific and Japan. Thank you all for joining us today. Before we start, a few housekeeping points to be noted. Please note that all participant lines will be muted. For the webcast, if you need closed captioning, just click on the button on the bottom right of the display. Finally, if you have questions, please use the q and a section within the tool. We do have a packed agenda today loaded with lots of information and sharing. So we will reach out to you separately with those answers as soon as possible. With that, let's get started. To kick things off, we have doctor Chris Marshall, vice president, data analytics, AI, and industry research at IDC. So doctor Marshall will be sharing IDC's insights and recommendations around the topic of enterprise observability powered by AI and automation. Welcome to this session from insight to action, AI fueled IT ops and APJ perspective. My name is Chris Marshall. I lead IDC's research in Asia Pacific on AI and its implications across a host of different businesses and industries. Now the last few years, I think, will come as no surprise to anyone. It's really been a a bit of a focus and a scramble, if you will, towards Gen AI and AI in general. And it's certainly no surprise that many, many companies have really thought and worried about how they get POCs into production and actually try to generate some sort of value from them. Now that was certainly the case a year or two ago. I think we've now moved to the stage where we realized that it's not enough to do twenty, thirty, 40 POCs if 90% of them fail. Rather, the challenge is that many of those POCs were POCs were failing precisely because there wasn't the infrastructure and the supporting operations to make an AI fueled business a success within the enterprise. Now those foundations for an AI fueled business really require many different components. They require, for example, strategy, data, skills, governance, infrastructure, platforms, applications. All of these pieces need to be in place. And the absence of these components, in many ways, is what has made AI quite hard to achieve real business value from in the last year or so. It's changing, but it's taking time to change. And one of the reasons it's taking time to change is because companies consistently say that they face challenges. Now the five challenges that they point to again and again really boil down to the following. Now Asia Pacific organizations are rapidly transforming themselves to deploy AI, but there are clearly some important changes that threaten their ability to scale AI, but also threaten their ability to use AI in a resilient and safe fashion. Now to address these real risks, organizations have got to modernize and optimize their entire operation portfolio. And that really means thinking about how they can use AI itself to automate, to do more proactive monitoring, to actually scale the infrastructure, to drive better efficiency and resiliency, and eventually long term business value. So in many ways, although the focus was trying to use AI, to create business value, we're now starting to use AI to support the infrastructure and the operations that in turn support AI. And I think it's with that in mind that we see five things that are coming to the fore right now that companies are saying to us that they need to address in order to overcome the barriers to using IT and leveraging IT to support AI more generally. Data overload is perhaps the obvious one, and that's especially important at the early stages. And at the same time, as companies start to scale their AI approaches to deal with the ever increasing, ever more complex workloads that AI implies. 32% of companies tell us that high implementation costs and high maintenance costs get in the way of successful AI deployment. So operational costs are really real concerns for many companies as they have to deal with a wide variety of data sources and a wide variety of different models that they worry about to deploy AI at scale. Nearly 50% say that security and compliance issues really are getting in the way of AI success. And again, there's been new threat vectors that have appeared as a result of AI. But at the same time, AI has partly tried to address some of those remedial effects that are impacts and ways of dealing with those security threats that AI imposes. At the same time, an awful lot of POCs have come on the back of public cloud initiatives. So hybrid and multi cloud complexity makes it more complicated, really, to manage AI at scale when they start to become embedded within the enterprise as a whole. And finally, the skill shortages. We see a host of companies saying that with AI, a lot of their senior IT department leads are actually moving towards more of an AI focus simply for career reasons. And that leads to AI skill shortages in other parts of the IT department. And this delays transformation. It makes it more difficult in many ways to support AI at scale. So what we see as companies make this transition from AI experimentation to sort of scaled adoption, they have to balance the early concerns and the early agenda for business growth, ROI at any price almost. They need to improve productivity of their end users and more efficient processes and what have you. They've gotta balance that need for business growth with AI, with the need for managing the the resiliency and the risks associated with that AI deployment. Now these risks come in a variety of different forms. Some of them are operational. Some of them are data oriented. Some are technology based. There's even some financial risks as we get to spend so much on expensive, dedicated, accelerated AI infrastructure. One interesting thing, I think, is that AI, although it's really the agenda item for this whole discussion, it's also part of the solution. And AIOps is really at the forefront of that story. AIOps is designed to use AI to monitor, manage, and maintain and automate IT processes themselves, and therefore, balance business growth with operational resiliency. Indeed, we think in the really, the net just the next few years, we expect to see IT operations dramatically transformed by a focus on AI and, especially, I'd note, Adjentic AI. In fact, we believe that something like nearly half of day two IT operations will actually be handled by Adjenic AI related tools with human on the loop for guidance. So that story is a very powerful one indeed, and it's what I'd like to drill down a little bit more in the pages ahead. Now operational and the IT data is clearly at the backbone of successful digital transformation to take advantage of AI within the enterprise. But the fact of the matter is, you know, with the maths of telemetry data, log data, event data, operational data, all of it, that it can quickly overwhelm the average IT department. I mean, most companies will have, you know, for example, they may have half a dozen different observability systems on their existing operational data. Not surprisingly, this process huge challenges and huge integration as well as costs and really gets in the way of unlocking insights at scale from the data that they've gathered. So it starts really with prioritizing insights from the data that you already have. It also requires a certain degree of intelligence about the data that you've gathered. And we actually see both insights, gathering insights, and intelligence on top of those insights as in top investments areas as companies start to scale their investment in data and operational data and their AI ops. Of course, intelligence or insights are never ends in their right. Rather, they are the means to make better decisions. And this is where Genetec AI starts to become most important. The extent to which companies can use AI ops to support better decisioning about operations is gonna be the extent to which they can achieve positive business outcomes and business impact. Clearly, we see that data management, data intelligence, governance, and analytics are really fundamental to making sure that IT operations does indeed become data driven. Not a nice to have, but rather a requirement as we move towards an AI world. The goal is, of course, to move from reactive to more proactive IT operations. No surprise that something like three quarters of planned IT spending just this year are usually focused on keeping the lights on or the engine tuned, as they say, rather than driving innovation. That has really slowed innovation as IT companies start to step up and actually support enterprise AI at scale. AI powered automation, observability, and AIOps is hoped to change that. And it will do that precisely by having a more predictive approach to monitoring and managing incidents, doing, for example, root cause analysis, understanding what is the true cause of the particular event or log data that you happen to see based from your observability systems, and be able to monitor that in near real time suddenly gives a set of capabilities to IT ops teams that they've never had before. It can fuel faster decisions, reduce downtime, cut costs, and ultimately help increase efficiency and make sure that IT ops becomes a business partner rather than just, somebody worried about the picking up the pieces and firefighting. In the area of DevSecOps, we also see AI augmentation playing an important role. It's already happening. IT ops is being used JEDIC AI especially is being used to change DevSecOps fundamentally from the ground up. It's leading to increased agility, improved code quality, test automation, even in some cases, reductions in headcount. We've been certainly talking about 30% reductions in many cases in parts of the IT DevSecOps teams within Asia. But I think more importantly than that is that those people are being reallocated to do different things. IT is gonna be supported for the most part by AI by AI as it augments the various aspects of DevSecOps. About a quarter of Asia Pac organizations are already integrating agentic AI systems across DevSecOps workflows. And the hope is that that will lead to much faster delivery cycle for software and much smarter incident response and fewer security issues as well. Now this is not just important from a purely IT and a DevSecOps perspective, but as organizations as a whole become more like software factories and less like traditional enterprises, what starts to happen is that AI's role in supporting DevSecOps becomes a business necessity rather than a nice to have. When it comes to supporting, autonomous IT ops, we see that AgenTek AI has got an absolutely fundamental role to play. 40% of organizations already say that they're deploying AI agents in Asia Pacific. About 60% of them believe that AI agents will shift IT from away from sort of reactive management to much more self governing systems. And, of course, whenever you start having self governing systems, you start to worry much more about trust, governance, alignment, accountability. All these things become more, not less important. And this is actually increasingly going to be the role that human beings start to play within the IT department. Their job will be to make sure that systems are aligned, that there's consistency, explainability, that the necessary guardrails are in place for AI to be used in an AI upsetting. We see and we certainly follow the the the trend whereby half of the market within IT departments in Asia see agentic AI adoption as really being absolutely crucial to supporting more reactive or more successful and proactive management of their IT operations. And this is gonna have knock on effects both for security, IT operations, as well as compliance. It's happening now in short. The story gets even more complicated when we go to think about the fact that most deployments are increasingly across hybrid, indeed, across edge to cloud deployments as well. You need unified management across these different platforms. You need typically, you're looking to support unified single pane visibility and management across a host of different platforms. You need to worry much more, we think, about data sovereignty, digital sovereignty, and compliance, particularly on a world where we're starting to see much greater national requirements about what IT operations can be done where, what data can be managed and gathered where, maintained where. Worries about resiliency and continuity become more, not less important. Cost optimization, of course, is gonna be more important too as the operations become more expensive and more complex. Cost optimization is really your way to make sure that you do actually deliver on that ROI agenda that, of course, the board is obviously going to push IT to deliver on as it moves towards this agentic AI future. The idea of supporting resilience as well as innovation is really gonna be key too as companies start to worry about how do they balance, for example, opens the use of open source tools with the need for, perhaps, sovereign AI requirements within the enterprise. All of these different pieces are need to be there in order to support every aspect of a hybrid, multi cloud, edge to cloud strategy for enterprise resilience moving forward. So in short, there's sort of four big things I'd like to flag for our listeners today. AI ops, data ops are not nice to have. With AI agents starting to play a bigger role in both, suddenly we're starting to see that AI ops are going to be absolutely fundamental to supporting that balance between resilience and innovation as companies start to deploy AI at scale. There's the people aspect too. We cannot afford to forget how human beings are going to be deployed in the context of AI agents, in the context of AI ops. You've got to be investing in upskilling, collaboration, more adaptable tools that support people as well as supporting the technology itself. It's only with people in the loop that you can actually make sure you can respond to the more crazy, the more weird, the more unusual disruptions that often will happen despite your best intentions. AIOps is gonna be effective most of the time, But sometimes it won't. And when it doesn't, you've gotta have make sure you've got humans in place to give you a backup. Observability is already a focus for many organizations across not just IT, but across every business unit. Now observability is not an end in itself, but rather the basis of developing unified visibility about data and operations across the entire enterprise. We're also thinking about how do we leverage that data to support root cause analysis, anomaly detection, and more autonomous systems. Keep that in mind when you're thinking about how do you deploy agentic AI at scale and the inevitable, data and operational complexities that that will lead to. AI driven analytics, obviously, are gonna be increasingly important as AI ops starts to take over and uses that observability data to see convert it into more actionable intelligence about operations. Now the idea is that you also wanna scale securely and make sure that you can automate and take advantage of AI to improve not just IT workflows, but rather enterprise wide workflows, balancing innovation with efficiency and resiliency. Now I've mentioned too the need to do this from a multi cloud hybrid cloud perspective, from an edge to cloud perspective, integrating not just IT ops across the enterprise, but also things like FinOps, DevSecOps, and Edge Intelligence to make sure that you can build autonomous hybrid ecosystems that are able to respond quickly to any threat the enterprise may face. With that, thank you so much. Thank you for your attention today. Thanks, Chris, for that really insightful sharing on the current trends and how IDC perceives the AI ops landscape. Now we move to the next segment, which will be an executive dialogue between doctor Chris Marshall from IDC and mister Bharat Bedi, vice president at SolarWinds Asia Pacific and Japan. They will be discussing the topic entitled Beyond the Hype, A Future Ready IT Ops. Chris and Bharat, please take it away. Well, thank you again, everyone, for staying with us. Bharat, it's really a pleasure to have the chance to explore with you today. Our focus, which is sort of AI fueled IT ops. And for you to give a bit more, Frank, of an executive or even a a very practical perspective on on what our research is telling us and what your experience is is guiding you in terms of SolarWinds' perspective on this emerging topic. Absolutely, Chris. First of all, thanks, for calling me here. It's it's an honor to to be presenting alongside you. Your research is, very insightful. It looks like there's a lot of work that has gone into it, and you definitely have spoken with a lot of IT, professionals. So looking forward to this, it's quite compelling, and, let's dive right into it. Brilliant. Thank you. Well, let's let's see what your teams are seeing. I mean, one thing we saw on the info brief really was the fact that, you know, something like 60% of APJ companies are really running hybrid environments. And we all know that this is driven by lots of different factors. Obviously, there's the increased AI demands that are really forcing companies to go hybrid in many cases, And also, maybe the more general need to scale up and sometimes scale down. But we all know that with this, you get many, many more integration points, more tools, more processes, more data point data sources to worry about. And quite frankly, it quickly becomes a fairly complex, fragmented mess to be, to be brutal. What's the sort of biggest, most common pain points that your customers are telling you when dealing with this sort of outrageous level of complexity? Yeah. Because the the key word here is complex. Right? I gave a talk earlier sometime, and I said that, every time I use the word complex and if you take a sip of wine, you'll be really drunk by the end of the talk. And that's exactly what's happening in the industry right now. I've not seen any CIO coming in and saying, on a Monday morning that this week, my IT environment, the technologies that I'm using is really a lot more simplified than how it was last year. So that is going to remain. And in fact, I believe it's gonna get even more complex. Now I think the challenge is not the complexity itself, but the cost of complexity. You know, teams are drowning in data, but they're still starving for insight. I mean, they're managing six, sometimes even 16 different tools, and each tool trying to solve a different problem, but also creating 10 other problems now. And over time, do you believe that this patchwork becomes a much bigger complex problem in itself. And so what's worse is the CIOs are telling us that they're spending nearly three quarters of all their budgets and just keeping the lights on. I think that leaves very little room for innovation, AI, or or truly transformation, so to speak. So so I think the challenge is not capability of the tools. It's it's the coherence of these tools. How do different tools in the IT environment work together, and try to give the customers or the CIO, an easier way to find out where the problems are and how do we go ahead and resolve it. So I believe that the become the the industry in itself needs to think about how do we bring everything together, visibility, context, and action in in one unified view, sort of a platform approach. I couldn't agree more. I mean, one thing we saw in the info brief, frankly, was the effort to solve this cost of complexity problem is that companies are really making renewed focus on things like increased automation, standardized architectures, you know, decreasing, as you said, tool sprawl. In fact, 40% of our enterprises that we were talking to actively said they're looking are looking to cut the number of tools that they use, particularly things like observability and like. Complexities, fragmented data. Did I say complex already? Yeah. I couldn't sip that wine just yet. Exactly. Exactly. But but it's more than just the tools. I mean, it's the systems. It's the data, the processes. All this is just a sync for IT budgets. Absolutely. Absolutely. I agree. So that's a kind of a perfect lead in to my next comment or question, about remediation and possible solutions for sort of technology operations remediation. We see a lot of companies dedicating a quarter of their budgets, a quarter of their IT budgets, amazing, to automation and observability. The idea is, of course, this is trying to enable more proactive detection, sort of rapid response and continuous improvement, I suppose, and help those companies go from a very reactive state to their IT operations into a more resilient I hate it's a complicated word. And AIOps is a vehicle to deliver that that improved resilience. But that's kind of what we're seeing that companies are starting to find is most valuable in their AIOps. Something like 43% of organizations we talked to said that AI powered root cause analysis and guided remediation are the most valuable AIOps capabilities. And I I think this really is good evidence that the market understands it. We've gotta go from, you know, backward looking, so what broke, to a more forward looking predictive prevention. And that is the key to that sort of business resilience that we wanna do. So, Barag, what what do you see, and how does Sol Lewin see this predictive approach playing out in practice? I think the the whole predictive approach is absolutely where, you know, the industry is heading right now, and and we are beginning to see it come live in implementations as well. So for most organizations, we believe that detecting isn't enough. It's the question is how quickly can you can you respond and and how quickly can you even adapt. So, you know, when intelligence and and AI are applied, across the full stack, when data comes from intra apps, users, and and correlated automatically, the time it takes to to isolate and resolve issues significantly reduces. You know, what you should take hours now, sometimes even take minutes. You know, but but the shift also requires, a mindset change, Chris. Resilience, you used that word that a lot of us are using, inside SolarWinds and industry wide operational resilience. I don't think that can be built tool by tool. It it needs to be designed as a system, you know, one that unifies observability, smarter incident response, and even service management. And I think that's how you align people, processes, and and technology. So at SolarWinds, you know, that's the that's the philosophy behind, the approach that we have. How do we enable organizations to move from reactive to, predictive and even preventive as we go forward? So I think it's just not about automating tasks anymore. It's about how do we create a a living learning ecosystem that keeps the business running no matter what what happens next. There's also, I think, a bit of a question mark about the human aspect. I mean, you mentioned this is a system, a resilient system we're talking about. It'd be good to get your opinion about the sort of the current state of IT skills and whether they are easy the fact, for example, that we're struggling to get sufficient IT skills in in operations areas that is making this even more difficult, the skills aspect of it. You know, we we we spoke with some CIOs and did some research ourselves to understand what were their top three big pain points, and we thought that they would talk a lot about technology and so on. And they said skill set was becoming a really big challenge. And think about it this way, at the ground level, the IT operations person wants to get to that new certification, which just makes sense, helps their career advance. And when they do get those certifications, sometimes they even move on to other companies, and the IT leaders are thinking, what should I do? So either we we do not have the skill sets right now because the technology is advancing at a pace that it's really hard to catch, or when we do get to that skill set, and we impart the learnings, to the IT staff that we have, then there's a little bit of risk of churn over there. So I, a 100%, agree that skill set is a problem and not just a lack of it, but when you do, deliver that skill set and when you have the skill set in the organization, then how do you retain that? And these are the practical challenges, that we see CIOs talking to us about. I think that's spot on. And there's certain irony in the fact that, you know, across virtually every Asia Pac economy, skill shortages in IT ops especially are are very, very common. But the irony is that all the attention has gone to AI, and I think there's been a bit of a a move away from what are perceived as being a bit more boring, frankly, sort of like the ops skills. And yet they're frankly, in many ways, more essential and the ones that are more pervasive and likely to last the longest. So I think that that question is is so important here. Absolutely. Absolutely. I I wanna turn a little bit to the future now. We mentioned Agentech AI. Everybody's mentioning Agentech AI, quite honestly. And I think there's certainly a lot of early momentum to use Agenetec AI, and we're already seeing, you know, at least 40% of organizations claiming to be already deploying AI agents. We may debate the reality of those numbers. But at the same time, I think there is much more convincing evidence that something like 60 or 70% of organizations believe that AI agents are gonna be foundational to helping do that shift from reactive management to what you mentioned, this sort of resilient self governing IT system. And it seems like this is gonna be foundational to sort of the the move to a greater IT economy. But, honestly, for a lot of companies in Asia, this may sound like a bit of a pipe dream. How do you think Yeah. What what would you talk to the average typical IT leader here in Asia? And how what would you get them to start thinking about turning their AIOps platform into something that is truly self governing, resilient, whatever the the label you wanna use here. Yeah. I mean, let's simplify this agenda here. I don't even know how many times that I heard the term since yesterday, Frank. So, look, you also said AIOps platform. So think of today's AIOps platforms as, like a skilled driver using cruise control. Right? So the system can alert you, if something's wrong, even adjust the speed automatically as well, but you're still behind the wheel. Now AgenTek AI is is a step towards the true self driving car. And to get there, IT leaders have to think about building trusted autonomy. You know, ensuring that the car knows when to slow down, when the sharp turn comes, so costs, or when to obey the traffic rules, so compliances, and when to accelerate for better performance as well. So, you know, we're seeing in GenTech AI, honestly, sometimes even loosely right now in the industry wide, but, we need to think about the the the actual implementation, the use case, and it is just not implementing an agent. It is the overall, ecosystem that has to work with this agent for it to get to that self driving car. You know, and and once these rules, so to speak, are explainable or auditable, the system can then start making a lot of reliable decisions on on its own. Like, you know, the vital not only, avoids accidents, but also take smarter routes or, safer routes. So I think that's how AI ops, will evolve as well from, reactive management to to it being very, self governing. Mhmm. But I think there's always been a tendency in the marketplace to think of even trust, security, broadly defined explainability, transparency. These kind of good responsible AI monikers that we all like to espouse. There's always a temptation, I think, to think of those after the fact. And I I think, to be honest, I think that's particularly true in IT ops where, you know, it is not viewed it's viewed as kind of a lot of micro tasks. How how do you actually build that trust given that if I try a tool, an AI tool, and it doesn't work the way I want it to, I'm not gonna come back to it a second time. I'll do something else. It won't be too much trouble. How do you build that trust, especially in ideals that just can't go wrong to some extent. Right? You know, that that's a tricky one, Chris, because, everybody did talk about AI. But like I said, there's there's systems around AI that, that need to work together. And right now, compliance is and there are many other factors in place right now that need to fall in place for these agents and AI to start taking off the way we are envisioning it, for it to take off. When talking about takeoff, like, let's put an analogy of an airplane over here that passengers don't just trust an airplane just because it flies. They trust it because it is engineered with safety, with governance, with a lot of testing and oversight built in. So at SolarWinds, you know, we're talking about the same approach. We call it AI by design. It builds on our, fundamental, foundation of secure by design principles, and that focuses on four things, which, by the way, is resonating very well with, when we meet with the our existing customers or, the other leaders in the market. So this AI design, then works on, number one, privacy and security. You know, protecting the data that fuels AI. Just like, you know, reinforcing, the airplane's frame. And then there is accountability and fairness. So keeping a human in the cockpit, you you've got to be careful about that because you know, even your best, autopilot systems still need an oversight. They still need the pilots. The third part of our AI by design principle is is transparency. When the decisions are being made by AI, these decisions we believe should be explainable. They should be auditable. You know, we're just at the beginning of this whole motion, so you should be able to explain why the system took a decision that it did, and how did it learn. And we believe that transparency interest part is very important. And the last part is simplicity, frankly, when we think about AI. Sure. We are building a lot of sophisticated systems right now, but but we wanna ensure that everybody, not just engineers, are able to, you know, safely fly the plane, or use, the dashboards with a very intuitive design. So so we're thinking about privacy, accountability, transparency, and, simplicity as four key guiding principles under our AI by design structure. That that makes good sense. I mean, one thing that we certainly do notice is that, companies that think of responsible AI, for for example, after the fact, invariably, don't do a very good job. And more importantly, it stops them actually achieving much value add from their investments in AI anyway. And in one sense, IT ops is almost the perfect place to start when thinking about responsible AI and AI by design and these sort of techniques to make sure that even in well, relatively relatively simpler things or tasks that AI agents can do, you can have a discipline, you can have a, you know, you can have accountability, you can have fairness, transparency, etcetera, trust, even at that micro level. And then think about how you maybe expand that a responsible AI story to the large enterprise moving forward. So, as companies start moving towards, for the sake of I'll call it autonomous AI ops, What do you see as the the the challenges, the the probable hurdles, and the opportunities that that companies might want to start, taking account of when implementing Genic AI, especially in sort of large scale IT environments? It depends on the vision that was sold to these companies as well, to be transparent with you. Think of it as as agent take AI, as a shift from a control room, like a traffic control room to a self driving city. You know, a lot of us are talking about the possibilities of the self driving city. And, surely, there's technology available today that can help us get to that. And but for years, IT has been monitoring dashboards and spotting anomalies, raising tickets. You know, it's like having those traffic cops at the intersection waving flags, but that that's that was what was there earlier. But when you think about the about these, self governing, self driving cities, it didn't take it takes us there. It changes what was versus what can be as as we go forward. So I think it gives that that vehicle, in the environment, service and application, the ability to to sense, communicate, predicts issues, and so on. But I think the real opportunity over here lies in moving from awareness to, to autonomy. We're seeing organizations accelerate their response times quite a bit. You know, sometimes at 30% better and reduce operational cost about 25% depending on how the systems are and how they're structured, maybe even over 60% as well. But, look, autonomy, doesn't come for free. Right? So it it it does have its set of challenges. It is these challenges that we try to resolve in the AI by design principles that I talked about, but let's let's let's point on those, one more time. We believe that this whole implementation of, agentic AI is there there is a challenge of transparency, right now. You know, how do you explain why an AI made a certain decision? I think there is also there there's a challenge about data integrity. You know, sometimes even the smartest data models fail if they're inconsistent or if they're working in silos. And the AI agents, the whole system works on consistent data streams coming in on the basis of which they can make the decision. So data integrity, I believe, is is another challenge. And and and then their skill set, we talked about it. You know, we believe that, you know, about by 2027, 80% of the IT professionals and engineering professionals will have to reskill in the areas like AI. So on one side, we are trying to build the city that just runs on its own, but do we have the data integrity, the transparency, and the skill set for us to get there? I mean, on a positive note, you know, we we are we are heading towards that direction. We've got the technology, like I said, in place. The skill set is getting built up. But, you know, the question is not when will AI take over. It's about how do we design systems that we can trust and that can work alongside, people as of right now. That sounds pretty sensible. I mean, we certainly know that, across the board, companies are looking for their AI ops platforms to really start to embody responsible AI as well as the agenda green eye capabilities across the entire end to end platform. Yeah. I mean, at the end, in the end, autonomy without trust is a chaos. Yeah. Right? So but but but autonomy with accountability then, it is the future of modern IT operations, what we think. Perfect. I'm gonna move to the close a little bit, but I want to sort of pick up on one thing you said about the data aspects and the fact we we can sort of use our current priority, which is usually data oriented or data overload that we face to our advantage a little bit. So, I'm using that in terms of our AI AI ops, it's really about observability. How do we go beyond observability to make it into more actionable kind of operational intelligence? So I I wanna push you in that direction because I wanna get you to answer this question. What what final piece of guidance would you give to a CIO here in Asia Pacific? Based on what we talked about, what what would that be? What do they do tomorrow? If I have to leave one message for them, I would say don't chase visibility in your environment. That's what we've been doing for some time now. I mean, we wanna see everything that is in the environment. Chase understanding in the environment. You know, I believe that the future belongs to leaders who can move from just connecting what they see to what it really means. So I think the real opportunity is not to make IT smarter. It's it's to make IT wiser. So there's one thing to do after this session is to is is to start connecting the dots. Right? Because when you see everything clearly, you don't just react to the future. You really build it. And, you know, that's the part of the conversation that we're loving having this with with with, IT professionals right now. This is resonating, with a lot of IT professionals and CIOs. This is what's keeping us very excited, and that's the place where SolarWinds operates as well right now. Oh, thank you so much for those inspiring final thoughts. I I certainly enjoyed the conversation. I hope our listeners have too. Thank you so much. Thanks a lot for having me, Chris. Alright. Thank you everyone for joining us today. This marks the end of our webcast session. We hope you found this session helpful in and informative. If you have any further questions, please contact us at solarwindsapj.marketing@solarwinds.com. Thank you once again, and we look forward to having you on our next SolarWinds webcast.