3545: LogicMonitor and the Rise of AI Native Observability in Enterprise IT

[00:00:04] - [Speaker 0]
A massive warm welcome back to the Tech Talks Daily podcast. Today, I'm joined by Garth Fort. He's the chief product officer at Logic Monitor, and he's a leader whose career spans Microsoft, AWS, Splunk, and is now one of the most influential players in the observability space. But Garth has stepped into this conversation at a fascinating moment because we're at the moment where enterprise IT is finding itself under pressure from growing complexity, hybrid infrastructure, and rising expectations around AI driven performance. So today, I wanna explore why AI native observability is emerging as the next major shift in enterprise IT and how Logic Monitor is positioning itself for this moment through Edwin AI, which is an intelligent agent designed to cut through the alert noise, tackle alert fatigue, which we've all seen and experienced, pinpoint root causes, and automate resolution paths.

[00:01:10] - [Speaker 0]
And Edwin AI is not just supporting IT teams. It's helping reshape how they understand and respond to system behavior in real time. And we will also examine what is broken in legacy monitoring tools, why traditional approaches are struggling in hybrid environments, and how IT leaders can rethink visibility and operational resilience without overwhelming their teams that are already under pressure. Garth will also share his incredible journey through some of the world's biggest technology organizations and how those experiences have helped shape his perspective and how he plans to use those experiences to drive Logic Monitor's next chapter. So whether you are navigating infrastructure complexity or questioning how AI agents fit into your operational strategy, this conversation will offer some thoughtful insights.

[00:02:05] - [Speaker 0]
Here at the Tech Talks Network, we now have nine podcasts and approaching 4,000 interviews, and that is only possible with some of the great friendships that I've developed over ten years of podcasting. And a company that I'm proud to call friends of the show is Denodo because not only have they been on this podcast multiple times, they also help make sense of the AI data chaos that we're seeing now. Because the data world is louder than ever. AI hype, lake house complexity, and pressure to deliver more with less. These are things that I talk about every day on this show.

[00:02:37] - [Speaker 0]
But Denodo is helping businesses make sense of it all because they provide a unified data foundation for trustworthy AI, lakehouse optimization, and data products that finally bring service to life. Simply visit denodo.com today. But now, it's time for today's interview. Let me introduce you to today's guest. So a massive warm welcome to the show.

[00:03:01] - [Speaker 0]
For everyone listening, hearing about you for the first time, can you tell them a little about who you are and what you do?

[00:03:07] - [Speaker 1]
So I've been in software for going on thirty years. I started my career at Microsoft where I spent about twenty two years there. Did a whole bunch of interesting things, mostly in product, but I also spent some time in the field where I got to spend a lot of quality time with customers. After that, I moved to AWS for about five years and had a couple of different roles there, but I got an opportunity to, you know, run engineering teams and learn firsthand about sort of, like, how observability applies at scale. You know, Amazon, AWS specifically has about 225 globally available web services, and Survivor's responsible for a number of those and, you know, understanding how to keep things healthy twenty four seven in a global environment.

[00:03:44] - [Speaker 1]
Like, I really was a customer of observability as much as anything else. That prompted a move to Splunk where I was a CPO there for a number of years prior to their acquisition by Cisco. And then, I'm now here as the CPO at Logic Monitor. Wow. And it's a fun it's a fun role.

[00:04:01] - [Speaker 1]
The CPO role, I've done it. This is my second sort of go around, and, it's actually a really fun role given sort of like my background. You know, a typical day, I started the morning, today, on a call with our dev team in India looking at sort of the demos of what they've delivered over the last sprint. We deliver in three week intervals typically. And so I spent a lot of time with engineering, but I also get to spend time with customers.

[00:04:23] - [Speaker 1]
I get to spend time with partners. I spent a lot of time, with different functions across the company, just figuring out how the product road map can help accelerate our ambitions overall. So it's a great integrated role. It gives me a very, you know, a very buried diary, if you will. And so every day is a little bit different, and it's just a great a brief place to learn and and have an impact on the company.

[00:04:49] - [Speaker 0]
I love that. And you've been on quite a journey there. Fantastic career. One of the things that stood out about where you are now is it feels like you've stepped into Logic Monera, a point where enterprise IT is feeling under pressure from both AI complexity and some of the rising expectations that come with that. And when I was doing a little research on you, I know you've talked about AI native observability as as almost the next frontier.

[00:05:16] - [Speaker 0]
So what genuinely changes for IT teams compared to the the monitoring models that they have relied on for the the past decade? Because you've seen a lot of changes throughout your career, but what are you seeing at the moment, and what excites you, and what maybe even makes you nervous about the road ahead?

[00:05:33] - [Speaker 1]
I'm I'm less nervous. I'm more kind of excited, to be honest. You know? So my first sort of sort of entry into this domain, I actually ran the management and observability tools for Microsoft, in their security portfolio. This is a long time ago.

[00:05:46] - [Speaker 1]
I'm kinda dating myself, but, you know, we had tools like system center configuration manager, system center operations manager. You know, we bought our first APM tool. It was a little company called AviCode. We did IT automation through a a runbook automation tool called Opalicy we acquired. So I I was kind of, like, steeped in the old world of, like, traditional monitoring, like, fifteen years ago.

[00:06:07] - [Speaker 1]
Fast forward to my time at AWS, none of those tools would really work at the scale we were operating at. And, you know, credit, you know, Twitter had a very famous blog post in the 2003 where they really coined the term observability in the industry. And their point was just like traditional monitor monitoring tools just will not keep up with the complexity of, like, modern you know, I think Twitter only had about a 100,000,000 users at the time they were publishing this. So they were small by today's standards. But even then, they kind of realized, like, you just can't monitor these large scale complex systems using traditional tools.

[00:06:39] - [Speaker 1]
Humans just can't keep up. You know, that was very true at Splunk where we were aggressive in adopting AI into both our security and observability portfolios. And then I took a little bit of time off where I was advising some startups between Splunk and and Logic Monitor. And it just dawned on me, like, as, you know, you're talking to these young companies and watching them to start to apply AI to some hard problems that have, like, you know, been lingering in the industry for twenty years. You know, it became obvious that, you know, a lot of the things that we've been talking about, like self healing, autonomous IT, self configuration, etcetera, these are old ideas that date back to the early two thousands.

[00:07:17] - [Speaker 1]
You know, IBM, you know, famously created a manifesto in 2001. But it's like only now with the with the maturity of IT, you know, and it's still early, I will say that, that it just became obvious that a lot of these hard problems that have that have sort of persisted for decades, you know, were on the precipice of being able to to solve them. Logic monitor really appealed to me. You know, I I met with, you know, Christine and the leadership team. And as I got to know them, you know, they've really kind of embraced AI as the future of the company.

[00:07:47] - [Speaker 1]
They bought DEXTA, which is a was a small startup in London in 2001, and that's really been the nexus of kinda what how we built our AI strategy out. It's the basis of what we call Edwin AI today. But really, you know, we're infusing AI into everything we do, both for customers and the tools that we provide them and also in the way that we actually approach everything from, you know, our legal department, how we deal with, like, pipeline generation with the field, how we actually, like, do engineering. Like, we've got, you know, pick your favorite tool of the day. We've got, you know, Copilot and Cursor and Claude.

[00:08:19] - [Speaker 1]
You know, we have just fully embraced AI, in terms of what we deliver to customers, but also how we run the company. And that was really appealing to me.

[00:08:28] - [Speaker 0]
And it's interesting listening to you there how so much has changed and how so little has changed at the same time. We still have that challenge of trying to keep up. And and present day, legacy monitoring tools, they still dominate many large organizations, yet they often struggle to keep pace with the hybrid and multi cloud environments that they rely on. So I'm curious from your perspective here, what what is fundamentally broken in the way that enterprises approach infrastructure visibility today? And what is there a mind shift a mindset shift that maybe needs to happen before the technology can catch up?

[00:09:04] - [Speaker 0]
Is that the problem? What are you seeing here?

[00:09:07] - [Speaker 1]
Well, I don't know that it's as much a problem. You know, some of the enterprises that we serve today have been around for a hundred years, and they've got, you know, they've got host based systems. A couple of them have mainframes and large financial services. You know, you know, technology moves in waves, but it doesn't, like, leave the old behind. And so, you know, that's part of the complexity of modern enterprise infrastructure is it's very complex.

[00:09:29] - [Speaker 1]
And, you know, when we look over our customers, all of them have a very hybrid footprint. A few of them have made, a singular bet on a single hyperscale provider, but a lot of them actually, like, wanna have a foot in, like, multiple worlds. And so we support Azure and AWS, GCP, and then most recently in h two of this year, we delivered support for know, and collectively, the four hyperscale providers offer over a thousand commercially available web services. And you just think about sort of, like, managing the complexity of that in addition to managing your on premise legacy environments. You know, the data center, you know, in spite of the prognostications that I made as an AWS employee, you know, five years ago, seven years ago, you know, data centers still exist.

[00:10:13] - [Speaker 1]
And a lot of our big enterprises for various reasons from data sovereignty to, like, regulatory requirements, like, they still have a very big on premise footprint. And that's where, like, I'd say, you know, we really got our last boost was when we sort of embraced hybrid as the new normal, and now we're using AI to really accelerate kinda how that's working. You know, so things are not broken, but they are complex. And I don't think that complexity goes away as the world gets increasingly hybrid. You know, it's maybe a quarter of enterprise workloads have migrated to the cloud.

[00:10:45] - [Speaker 1]
There's still more to go. But but I think, you know, the we'll pass off the IT world to our children or our grandchildren. I think the world will be IT IT or hybrid IT for the duration. And so it's how do you manage that complexity? Most of our big enterprise customers, we surveyed about a 100 CIOs recently and they had an average of about 35 different tools across security monitoring and observability.

[00:11:11] - [Speaker 1]
And that's kind of the normal. What we've tried to do is come in with a set of tools that complements the legacy infrastructure, lets you monitor your existing network and data center infrastructure, lets you embrace, you know, hybrid to the extent you wanna go make a bet on a hyperscaler, but managing that complexity becomes overwhelming. You know, the we had a large health care company in The United States and I was meeting with their CIO. You know, they were dealing with, just from logic monitor alone, about 18,000 alerts a week. And this is a mature deployment.

[00:11:41] - [Speaker 1]
They've they've tuned their alerts. They use dynamic thresholding, we allow allow them to basically let the system sort of look at normal behavior and figure out what an anomaly is. And even with all of that technology, they were still overwhelmed by the volume of alerts. When we introduced, Edwin AI into their environment, we took that from 18,000 alerts a week to less than 1,200. The CIO was kind of just blown away by that, you know, because a lot of times you'll see, you know, when you're trying to figure out what's the needle in the haystack, what's the root cause that's causing the alerts across all these related systems, you know, AI has really turned out to be a very powerful tool for doing that alert correlation, which is the first use case that Edwin I AI really went after.

[00:12:23] - [Speaker 1]
So I don't think it's as much. I actually don't find enterprises to be, like, resistant to AI. I I actually think it's a place that people are looking to it as a potential solve for a lot of these, problems that just are inherent in the complex environments that they run.

[00:12:39] - [Speaker 0]
And as you said in your intro there, you bring experience from AWS, Microsoft, and leading Splunk through its cloud transformation. And as you begin shaping Logic Monitor's next chapter, where do you see the the biggest opportunities to apply so many of those lessons in a way that feels distinct from what you've done before? I'm kind of intrigued on on where you see this evolving and taking shape.

[00:13:04] - [Speaker 1]
Yeah. It's a it's a great question. I thought about this pretty deeply when I was taking some when I was doing the advising work. You know, I've been fortunate to work at some of the best tech companies in the world, you know. You when my when I left Microsoft, it was about a 150,000 people.

[00:13:19] - [Speaker 1]
When I left Amazon, it was a 1,500,000 people, you know, employees. I mean, it's one of the largest private employers on the planet. You know? Even Splunk was big. It was publicly traded, but still 7,000 employees when I was there.

[00:13:32] - [Speaker 1]
You know, the opportunity that I thought, you know, Logic Monitor really presented was, you know, you have a leadership team and a board that's completely committed to embracing AI in everything that we do. That was kind of thrilling. There's there's nobody who's like fighting that like, you know, inside the company and we don't find our customers really resisting that as well. But part of the fun is like, how do I take these lessons learned from some of the biggest tech companies in the world and figure out how to right size it and bring it to bear on a company that, you know, Logic Monitor is just north of a thousand employees, you know. The business is growing quickly.

[00:14:04] - [Speaker 1]
It we passed 300,000,000 in ARR, you know. But it's it's it's taking lessons learned from the biggest tech companies in the world and so we're figuring out how to right size it and apply it to a different company that's at a different scale and a much steeper part of the the growth curve, if you will.

[00:14:21] - [Speaker 0]
And the introduction of AI agents like Edwin AI that you've mentioned today signals a a shift from reactive monitoring to something much more not just proactive but autonomous. So how should IT leaders think about that balance between automation and human oversight as these agents become more embedded in our daily operations. One of the big keywords or buzzwords I hear every tech conference this year is human in the loop. But how do you see that?

[00:14:50] - [Speaker 1]
Sure. Yeah. It's, it's interesting. We've introduced some capability recently that, is very familiar to people. You know, automation historically has been what we would call deterministic.

[00:15:01] - [Speaker 1]
It's kind of if this, then that, you know, and that's still got a huge amount of utility in sort of modern environment. So we have this feature called automated diagnostics and remediation. So when alert fires, you can actually run additional diagnostics on the host or the network to figure out what was actually going on. So you can do a deep dive grab, you know, whether it's a, you know, a net trace you wanted to get us a a heap dump or figure out what was going on with that machine. But that can be completely automated.

[00:15:27] - [Speaker 1]
And nobody has any resistance to that because it's very predictable. Like, you know, if this, then that. And then in terms of the remediation, like, it's just a set of scripts, like, whether you're using Groovy or PowerShell or you're using you know, we've got integration that we announced recently with IBM, Red Hat, Ansible, through Edwin. But, you know, deterministic automation is something that I think people are pretty comfortable with because, you know, you can reproduce it. It's gonna behave the same way every time you run the same inputs, you get the same outputs.

[00:15:55] - [Speaker 1]
And so that still has a lot of utility and that kind of automation is people are very comfortable letting that run on its own. Agents are a different thing. You know, AI is by definition is not deterministic. It's stochastic. And so, you know, you're kind of you have to kind of shift away and you have to think about things in terms of, you know, is this gonna be what's the probability from a statistics perspective that the agent's gonna give you the right answer.

[00:16:20] - [Speaker 1]
And that's where I think people are talking about, you know, human in the loop. You want your agent to tell you its plan so that you can have a human review it and say, okay, before you give the agent permission to run. We were with some analysts a few weeks ago and, you know, I said something that I this is kind of off the cuff, but it it it in hindsight kind of like resonated a lot. I think when you get to the point that your agents have as much governance as your humans today, right, people will start to trust them. Know, but that means you gotta have all the governance that you have around role based access controls.

[00:16:55] - [Speaker 1]
You have to have audit logs, you know, that's gotta be auditable. You have to be able to store those things. You have to, you know, go back in in the event that something does go wrong. You would wanna know what happened, who did it, and why. And so you have to build a lot of this governance around these agents that has that has always existed for humans operating in an IT environment, but we're very early in in sort of that.

[00:17:15] - [Speaker 1]
I I believe, you know, we've introduced a bunch of agents in in Edwin AI, as you mentioned. You know, a lot of what they're doing is actually automating fairly routine things. They're fetching data for you from related systems. They're actually going and doing, you know, root cause analysis. They're doing correlation.

[00:17:33] - [Speaker 1]
Like, they're not really operating on the system and taking action. They're just helping operators get faster at their current jobs, and people are very comfortable with that level of automation. And I think we believe, like, in the next three to five years, as we start to build the right governance models around it and it's not just gonna be us. It's gonna be industry consortiums, industry standards that sort of dictate this. You know, we're embracing open standards everywhere we can.

[00:17:56] - [Speaker 1]
We're building our own MCP servers. We're supporting agent to agent protocols, and then we're building all the governance under the covers that make sure that that people have confidence that their agents are actually acting in a way, you know, that's predictable, repeatable, and actually getting the right outcome. So, yeah, human in the loop is kind of a you know, I think that's a point in time statement, and people are kind of reflecting that there is some temerity on the behalf of customers to let agents kind of run amok. But I think in a couple of years, people are gonna get as comfortable with, you know, the stochastic behavior of agents as they are with the the deterministic agents of kind of if then then that type deterministic automation that they've been comfortable with for years.

[00:18:37] - [Speaker 0]
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[00:19:42] - [Speaker 0]
And I think that kind of clarity is why so many high growth companies in Silicon Valley are working with them right now. So you can find out more details at alcor.com/podcast or simply use the link in the show notes. And we will have many people listening around the world that work in organizations that often talk about wanting foresight over hindsight, yet their operational culture often resists change. So what practical steps could IT teams take to to make that move towards more predictive and adaptive operations without overwhelming staff that are already stretched, and some might be a bit fearful that this tech is out to replace them as well. It's it's a tricky balance sometimes, isn't it?

[00:20:26] - [Speaker 1]
Yeah. It is. You know, AI I think the fears that AI is gonna replace every human labor, it's a it's a little bit overblown. Yeah. You know, my daughter oldest daughter is a senior in college.

[00:20:38] - [Speaker 1]
She's graduating with a computer science degree. It's interesting looking at her sort of learning over the last four years in university. They've embraced AI as just a way of actually doing computer science in a more efficient way. So she's had several classes that actually required you know, some of the teachers wanna sort of make sure that students understand core concepts and they forbid the use of AI in solving certain problems. But a lot of the classes were actually teaching students how to apply AI, you know, in in a computer science context to actually make them sort of more efficient engineers.

[00:21:08] - [Speaker 1]
And I think that's, that next generation I think is gonna be much more comfortable with AI. Even in our largest customers that tend to be a little bit, you know, maybe resistant to change as you said, I think people are just at a point like, you know, this healthcare company I mentioned, eight eighteen thousand alerts a week is just fundamentally unmanageable. And I think people are looking to AI with some optimism to really think about, like, how that's gonna make their jobs a lot easier. Nobody in if you talk to an l one engineer, you know, a level one person who's doing initial triage on this stuff, nobody likes the fact that they have this massive backlog and they can't make it through even a day's worth of work, etcetera. And so everybody wants to be better at their job.

[00:21:49] - [Speaker 1]
They wanna get rid of the lot of the mundane stuff that they have to do, you know, and automation actually gives you a way of, like, saying, yep. I've seen this problem before. I can tell you the last five times I saw it. Here's how we actually fixed it, and I can actually write an Ansible script to go and fix it for you. That's a real use case, by the way, that we showcased with IBM recently where, you know, AI is the ability not just to understand the state of the systems, but to kinda understand the state of the knowledge within the organization.

[00:22:15] - [Speaker 1]
And so it's being fed with, you know, Jira tickets. It's being we we have a bidirectional integration with ServiceNow so we can see all previous tickets. We can sort of, like, really kind of accelerate root cause analysis by saying, hey. This is a problem that we've seen five times in the last six months and, like, here's how we fixed it each time. And then actually get you ahead of that.

[00:22:35] - [Speaker 1]
That's the predictive part that you were talking about. How do you make sure that if you've seen the same incident five times in the last six months, how do you make sure you see it zero times in the next six months? And I think AI really kinda gives people, you know, the promise of being able to, you know, sort of like outsource, if you will, a lot of the mundane operations, you know, in security and IT and engineering and, you know, development and, you know, DevOps. And like really kind of like let the humans focus on what they're really good at solving and let AI do the kind of more routine routine things that are often very, you know, mundane and frankly boring for human operators to do over and over again.

[00:23:13] - [Speaker 0]
And enterprise IT spend, that's also evolving as fast as boards continue to wrestle with AI investments, data center transformation, not to mention risk management as well. I'm curious from what you're seeing here, especially, when during the year, there's been a big focus on ROI of every tech project. What is shaping IT budgets right now, and and how are you at Logicmony, aligning your roadmap with with some of these shifting priorities?

[00:23:43] - [Speaker 1]
Yeah. It's it's interesting. You know, in, you know, economic upturns and downturns, one of the most, resilient parts of IT spend is security. You know, nobody wants to skimp on security. And so, you know, generally, those budgets have been fairly safe.

[00:24:00] - [Speaker 1]
You wanna make sure that your enterprise is safe, your customers are protected, you know, your your, you know, confidential company data is secure. And so security tends to be fairly resilient in up up up cycles and down cycles, and that's that's been true for decades. And we believe that'll be con continue to be true in the future. AI is really interesting. AI is kind of getting a little bit of a, you know, get out of jail free card.

[00:24:23] - [Speaker 1]
Everybody wants to be investing in AI, and so there's you know, and a lot of our big companies, we have, like, specific line items that are assigned to AI before they even know what they wanna do with it. As we start to introduce Edwin AI into more and more of our accounts, the interesting thing is that, you know, the the governance process around sort of like adopting AI has a little bit more friction in it than I would say traditional IT tools. You know, I work with a customer a number of years ago, they had this thing they called the technical risk assessment program, for all IT spend above a certain threshold. And I kinda joked. It's like, oh, you really call it the trap?

[00:24:59] - [Speaker 1]
You know, your engineers wanna buy a tool. You send them to the trap, the technical risk assessment program. Procurement person thought that was a little bit funny, but we laughed about it. But it's it's serious. Like, IT tools go through security reviews.

[00:25:12] - [Speaker 1]
They go you know, the legal team wants to look at the contracts. They wanna kinda understand, you know, like, are they signing up for any terms that are gonna put the company at disadvantage, etcetera. And all that's pretty normal in terms of IT procurement. What we're seeing when we introduce AI into these companies is even though the budget is there, the governance hasn't really been figured out. And so very often, we run into customers where there's somebody on the board who actually is like an AI governance role.

[00:25:35] - [Speaker 1]
So it's not a person in day to day kind of operational role, but their job at the board level is to really understand how AI is impacting, you know, not just IT, but every function in the company. And so there there are, I'd say today, you know, there's a lot of budget that's allocated to AI, but sort of getting at that budget, you know, both internal to enterprises and then for vendors like us is a little bit more, it's more rigorous, you know. And so we have to go through reviews. There's often, you know, in addition to the I would call it the traditional, you know, technical risk assessment programs that have been around for decades, you actually have these new AI governance boards that are often, you know, people from legal and finance and and different parts of the company that really don't understand IT, but they're helping kind of the company navigate sort of this journey into the future. So the budgets are certainly there.

[00:26:25] - [Speaker 1]
When we look ahead to our road map over the next three years, you know, we have you know, we'll still continue to invest, you know, the majority of our r and d resources into the tools that, you know, people use on a day to day basis to run their hybrid IT. And so that's a that's a core part of our investment, and that's not going away. But we're shifting more of our r and d investment towards assets like Edwin AI, towards things like automated remediation that I talked about before, you know, really kind of embracing this idea that, like, we believe automation and autonomous agents are gonna be driving IT, you know, somewhere in the next three years. And so that's where we've kind of, you know, put our engineering bets for 2026 and beyond.

[00:27:05] - [Speaker 0]
And there's also a growing conversation around observability moving from just a technical function to more of a strategic business capability. So how do you see the role of the CPO and product teams evolving as observability maybe becomes central to wider business performance and resilience? Because it feels like a big moment here.

[00:27:26] - [Speaker 1]
Yeah. It does. You know, and I I I could make the case that that's not a new trend. Yeah. You know, you know, even fifteen years ago when when I was sort of doing what we would now call observability at Microsoft, you know, people aren't monitoring a server, a host, you know, you know, a middleware component because it's fun to watch, you know, metrics on a dashboard.

[00:27:48] - [Speaker 1]
Right? Nobody wants to admire dashboards. You know, it's always been in service of keeping the core business processes running. You know? But but as we embrace, you know, we have, you know, that this large health care provider, a different one that I talked about before, you know, they are very they're critical path in supplying, you know, life saving drugs to tens of thousands of clinics and hospitals around the world.

[00:28:09] - [Speaker 1]
You know, if an IT system goes down and they lose the ability to sort of, like, monitor the temperature of a vaccine, you know, for even a small period of time, like a transient performance issue means that they have to throw the whole dose out because, you know, the FDA here requires, like, a, you know, really secure supply chain, and you wanna make sure that, you know, the drugs that you're delivering are, you know, gonna be lifesaving and, you know, and and that's that requires a lot of these IT systems to be working in tip top shape. You know, we work with a large global, hospital group that's got hundreds of thousands of properties around the world or excuse me, not a hospital, a hotel group. You know? And they're really focused on, like, everything about the guest experience, some further ability to kind of check availability and book a reservation to sort of making sure the check-in goes well, making sure the bill gets paid, you know, they the, you know, the guests all have a wonderful experience while they're there. All of that is underpinned by, you know, by IT systems.

[00:29:02] - [Speaker 1]
We've got a lot of large airline companies in The United States, you know. You know, they're monitoring all these systems from, you know, baggage checking to sort of, you know, you know, getting through the security, getting your ticket, making sure the tickets on time, making sure the plane leaves on time and all those things, you know, fundamentally impact business systems. So, you know, I'd say it's maybe more critical. You know, we we've kind of embraced this. We introduced this notion of a dynamic service insight in Logic Monitor, says, look, You know you know, we have one customer that's managing well over a 130,000 network devices.

[00:29:37] - [Speaker 1]
And, you know, you wanna make sure that packets are flowing and buffers are not overrun and all that good stuff. But, you know, ultimately, what you're doing is you're, you know, you're providing visibility into the process outcome. So it's what I call business process observability, and that's really enabled by this notion of dynamic service insights. So the service is actually provided by a host of infrastructure that might be running in your data center, might be running partially in the cloud, you know, could be running at a third party like a partner infrastructure. And so being able to make sure that the business process is healthy, that's really what Dynamic Service Insights is about.

[00:30:09] - [Speaker 1]
And so you're monitoring at the service level or kind of at the employee or the customer experience and making sure that, like, everything is, like you know, the green lights across the board don't don't mean your customers and employees are actually experiencing green lights. And so you have to move up a a level in the semantic hierarchy, if you will, to monitor the performance of these, services as opposed to monitoring the components of the infrastructure. And, you know, you know, it's maybe become more critical, but that's always been the reason people have monitored IT, to make make sure business processes are operating well.

[00:30:40] - [Speaker 0]
And, again, looking at your career journey and your new role in the present day, what what would you say to tech leaders who might be listening who are trying to future proof their infrastructure decisions today, but also feel caught between rapid innovation and the reality of legacy systems they simply cannot walk away from. This is a topic we've been talking about for as long as we both probably can remember, but I suppose that with the speed of technological change, it becomes even more important. But but what would you advise here?

[00:31:11] - [Speaker 1]
Yeah. It's it's interesting, you know, in my career, and I think you've been around for many of these transitions, I lived through the transition to the Internet kind of in the mid nineties at Microsoft. You know, we you had the transition to search, which was just sort of a a massive change in the way that we all interact with computers every day from our phones to our PCs to our Macs. You know, then we went through kind of this transformation to cloud, which was was huge, you know, was led in many ways by AWS, but, you know, every big IT company kind of followed suit relatively quickly. You know?

[00:31:43] - [Speaker 1]
And then I would say, you know, I I would argue that sort of in all these transformations that I've seen over nearly thirty years, like this one with AI is happening, you know, faster than any of the previous ones. Like the Internet, if you think about it, like the Internet crashed for the first time in November 1980. Like, was called ARPANET at the time, but it went down for four hours and, you know, it it led to all these, like, postmortems and root cause analysis and, you know, that was actually you know, the ISO stepped in and, you know, we came up with industry standards that we now accept like SNMP and other things like that. Those all originated from a crash of the Internet that happened in 1980. It wasn't until fifteen years later that, like, the Internet started to become a thing that really kind of, like, was at scale.

[00:32:27] - [Speaker 1]
But when you think about AI, the world saw ChatGPT for the first time three years ago this month. You know? So if ChatGPT was a human, it'd be a toddler. I mean, it's walking, it's talking, it's starting to put its first words together. But when you think about what's happened in the last thirty six months, like, this transformation is happening faster than just about any that I can remember.

[00:32:47] - [Speaker 1]
One of the things that intrigued me about Logic Monitor, you know, yeah, we support customers that have legacy environments that, like I said, some of our enterprises are over a 100 years old. And so there's always gonna be legacy stuff, and I think that's what customers rely on Logic Monitor for. Like, we can monitor all of your legacy stuff. We have 3,000 out of the box integrations, you know, with products that are discontinued and vendors you might not even remember. But we still support monitoring them sort of in a hybrid environment.

[00:33:15] - [Speaker 1]
But, like, within Logic Monitor, like, we've embraced AI kind of wholeheartedly. I would say for IT leaders, the the risk of experimentation is pretty low. You know, like, we we get Copilot. We're we're using an enterprise license of ChatGPT internally. Like, the the cost of experimentation is very low right now, and I don't think there's any reason why you shouldn't be playing around with it today.

[00:33:41] - [Speaker 1]
You know, the the you know, yeah, I I I do acknowledge, like, even my daughter going into, you know, her first job out of college was worried, is AI gonna completely replace computer science engineers? Turns out that's not the case. But it's certainly like, you know, the the generation that's coming into the workforce today is coming off of an education that will be largely based in sort of AI complemented learning. And so I think that next generation is gonna be much more fluent and and willing to adopt. You know, I I you know, I would just say like you have to just embrace a learning mindset and you really have to just kind of like, you know, throw yourself in, start playing with it.

[00:34:19] - [Speaker 1]
You know, for senior leaders, you know, know, we did a a fun little example. We had an what we call an enterprise advisory board. So we had about 10 of our largest customers. You know, these are senior executives, CIOs, and maybe CIO minus one, and we're all together in a in a hotel conference room. And we did some just fun sessions together, like playing with GPT at a break.

[00:34:39] - [Speaker 1]
And it was just sort of like, you know, some of them had never really put hands on keyboards and actually experimented with ChatGPT before. But when we got into it, it was actually kind of fun and cost us nothing. I mean, ChatGPT is free. You can buy a pro license for $200 a month, but you don't need it to start experimenting with it. So it's low cost and low risk.

[00:34:57] - [Speaker 1]
And then, you know, what we've done inside of LM is is, you know, we do have enterprise license for all of our employees, and we actually watch how people use it. And we look for people that are, you know, early adopters. You you know, ChatTPT gives you the ability to see who's using it, how frequently do they use it, how many chats do they do per week, per month, etcetera. You know? And, you know, we're not using it as sort of a a hard benchmark that we hold people to, but it's just we're encouraging everybody to to use it as part of their, you know, their daily work.

[00:35:26] - [Speaker 1]
You know, there's one person on my team who's just been just a rabid early adopter. I think he does he's doing on order of 25 to 30 chat GPTs per week, different chats that he starts up on different topics. Topics. He He he happens to be one of my most productive product managers just because he's so much faster at sort of like getting to the right answer, you know, assisted by AI. But you know, the the cost of experimentation is zero.

[00:35:51] - [Speaker 1]
Yeah. So go for it.

[00:35:54] - [Speaker 0]
So many great points there. And you talked about the importance of having that learning mindset, embracing it. And there is a real pressure on us all to be in a state of continuous learning now. So on a a personal note here, especially at a time where everyone's starting to think about New Year's resolutions and what they're gonna be doing differently, where or how do you self educate on a personal level? And maybe it's a tip that somebody else could pick up listening as well.

[00:36:21] - [Speaker 0]
But how do you keep up to speed with everything?

[00:36:23] - [Speaker 1]
Yeah. You know, you and I both grew up in the era where we lugged hardcover textbooks to to school every day and, know, big rucksacks and, you know, that was that's a different world that our children certainly are growing up in. You know, I've got sitting behind me a library of about there's probably 300 different books back there. So I still like reading the book. The tactile feel of a book is great and I'm a pretty voracious reader.

[00:36:46] - [Speaker 1]
And this time of year is great because everybody's coming out with their best 10, best 100, you know, like, the all the best books of 2025. And so whether you like fiction or nonfiction or romance or whatever, like, this is a great time of the year to start building your reading list, you know, to your point on New Year's resolution. Something that I do every December is I start putting together a reading list for the next year. But just on a personal note that, you know, I've got my girls home from college this week for The US holiday with Thanksgiving. And, you know, we've been sitting around the dinner table and, you know, you know, different topics come up.

[00:37:17] - [Speaker 1]
I've got, you know, kids from 18 to 22. And it's it's just fun sitting around there and, you know, I still have muscle memory and so somebody will ask a question I don't know and I'll I'll go to Google just out of muscle memory and I'll start kinda trying to figure stuff out. And then my 18 year old will actually pull up ChatGPT and she gets to a hell of a lot more refined answer in like a quarter of the time that I would get to it just kind of doing the old fashioned search way. And so just, you know, I actually have found ChatGPT is is just such a just a great tool for learning and research. You know, our family's recently gotten is it we're a Seattle based family, so we're really big into our coffee and we just upgraded to a new fancy espresso machine and all that fun stuff.

[00:37:59] - [Speaker 1]
But as we've kind of been learning about how to use this machine and just, you know, become better baristas at home, you know, ChatGPT has been indispensable in the last couple of weeks in in teaching all of us to learn how to brew better coffee. That's a small kind of trivial example, but, you know, I'd say it applies to almost any new topic that you wanna go learn. ChatGPT is just one of the most, you know, fundamental. It is not just ChatGPT, there's Gemini. There's a whole lot of other tools out there, but it's just such a fun way to learn and explore new topics and do research.

[00:38:28] - [Speaker 1]
And it just it it just, you know, it puts you on the steep part of the learning curve as long as you wanna be there. So it's it's actually really fun.

[00:38:36] - [Speaker 0]
Yeah. I spoke to a guest recently and he he was on a three hour road trip on his own and he just spent about three hours talking to chat GPT going drilling down onto a particular topic he wanted to learn about, and then asked him to provide a a PDF of all the main points and everything. So just something simple like that makes it so easy to to pick up information. But back to logic monitor, before I let you go, anyone listening, they wanna find out more about Edwin AI that we mentioned today, the work you're doing at logic monitor or connect with you or your team. Where's the best starting point for everything?

[00:39:12] - [Speaker 1]
Yeah. We've got we've got a fairly packed kinda annual calendar when you think about it. I've been here about six months, so I got the tail end of kind of what our annual calendar looks like. But there's a bunch of us. They're gonna be at re:Invent next week.

[00:39:24] - [Speaker 1]
Excited. It's one of the largest tech conferences in North America, and so we'll have a lot of our team there. I know we've got lots of meetings lined up with customers and partners. That's exciting. You know, I'm garth.fort@logicmonitor.com.

[00:39:35] - [Speaker 1]
If you wanna meet with me at Reinvent, you know, hit me up today or tomorrow because my diary is filling up quickly, but we'll have a lot of people there who can do that. We also have a series of events that we run around the world for customers called Elevate. So we did one in Dallas and London and Sydney this year, and we'll kind of do that again next year. But we also have, like, you know, any industry event that you think of. You know, Gartner's got a conference the week after AWS in Las Vegas.

[00:39:58] - [Speaker 1]
Our CEO, Christina, is gonna be at Davos in January, you know, and we've got, you know, people all over the world. So, you know, we're a global company and we're engaged with customer events, nearly continuously. So, you know, I I thrive on spending time with customers and partners, you know, that you know, I spent nearly a decade kind of in the field, you know, kind of in a product role, but, you know, in a customer facing capacity, you know, and I know that the truth really lives in the customer. And so I look for every opportunity I can to spend time with customers whether that's, you know, at events that we own and operate like Elevate, you know, or at a big event like AWS. I think those are great opportunities to come meet with us and figure out kinda what we're thinking and, you know, tell us how to shape the way we're thinking in a way that can help you solve problems better.

[00:40:42] - [Speaker 1]
So

[00:40:43] - [Speaker 0]
Awesome. Well, I would add links to everything you mentioned there. I'll also be keeping a lookout for you because I too will be at AWS re:Invent next week, so it'd be great to meet face to face. And more than anything, I've just loved hearing more about your story, the journey you've been on, and why AI native observability is the next frontier in enterprise I IT, and also how Logic Monitor is positioning itself at the forefront. So much gold in your answers today, but just thank you for sharing your story.

[00:41:11] - [Speaker 1]
Thank you, Neil. I'll be at the Venetian and then spending a lot of time in the wind, etcetera. We'll be running around Vegas together. Hopefully, we can, find a time to meet face to face. Thank you for the time today.

[00:41:21] - [Speaker 1]
This has been fun.

[00:41:22] - [Speaker 0]
So a big thank you to Goth. I generally enjoyed our conversation today from his journey through Microsoft, AWS, and Splunk to the work he's now leading at Logic Monitor. I think for me, it was fascinating to hear how these experiences are shaping his approach to AI native observability and evolving role of IT operations. And his perspective on Edwin AI and the rise of intelligence agents, I think, brings real clarity to a topic that many organizations are still trying to make sense of, whether it is reducing alert fatigue, improving root cause analysis, or merely supporting teams through this phase of automation. It's clear that this shift is about enabling people rather than replacing them.

[00:42:11] - [Speaker 0]
And I also appreciate his honest take on enterprise IT spend, governance challenges, and the balance between innovation and legacy systems. Because these are the very real issues facing leaders right now, And his insights help bring structure and perspective to a very complex landscape. So a big thank you to my guest's time, but over to everybody listening. I wanna hear from you now. You are a big part of the Tech Talks Daily and Tech Talks Network community.

[00:42:44] - [Speaker 0]
So pop over to techtalksnetwork.com, record an audio message, let me know your thoughts and takeaways and perspectives that you have, or simply send me a message on LinkedIn, x, or Instagram. Just at Neil c Hughes. But it's time for me to go now. I'll be back again tomorrow with another guest, but thank you as always for listening, and I'll speak with you again tomorrow. Bye for now.