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{"podcast_details": {"podcast_title": "The Tech Talks Daily Podcast", "episode_title": "2481: Zenoss - From Cloud to AI: The Evolution of IT Infrastructure", "episode_image": "https://ssl-static.libsyn.com/p/assets/9/d/2/0/9d20c43ed1f13f50e5bbc093207a2619/techtalkdailyfinal.png", "episode_transcript": " Welcome tech enthusiasts and curious minds to another episode of the Tech Talks Daily podcast your one-stop platform for all the latest insights and deep dives into the world of technology. I'm your host, Neil C Hughes, and I've dedicated my time to outlining everyone with knowledge straight from the leaders in tech and business. And today we've got an industry veteran with a wealth of experience spanning over two decades. He's a leader in artificial intelligence, cloud computing and cyber security. His name is Trent Fitz and he's also the Chief Product Officer at Zenos or Zenos if you're from the US and they're a pioneer in the realm of AIOps. And from his rich career trajectory working with firms from Nimbox to SailPoint Technologies, TrustWave and even IBM Global Services, I've invited him on here to share his insider's perspective on the dynamics of IT, AI and AIOps. And in today's episode we'll also unpack topics like the recent transformation in IT, the realities and future of AI, Trent's journey in tech and so much more. So if you're wondering what's buzzing in AI and how AI manages complex infrastructures or considering a career in technology, this should be a conversation you don't want to miss. So buckle up and hold on tight because no matter where you are right now, it's time for me to beam your ears all the way to Austin, Texas, where Trent is waiting to share his story. So hey Massey, well welcome to the show, Trent. Can you tell everyone listening a little about who you are and what you do? Yeah, Neil, thank you for having me today. I appreciate it very much. A little bit about my background. My education is in computer engineering. Out of college, not to go back too far, but out of college I became a network architect for IBM back in the day. And then after that I went into the startup world. My first role was in sales engineering, then I got into product management and that's how I remained in product since then. My roles have been in companies around cybersecurity, cloud computing, and I think what we're going to talk about here today artificial intelligence. In fact, my first dabbling with AI was back in 1998 in college when my senior project was working on a Motorola microcontroller that was the first one that they produced that was AI enabled. And so that was my first interaction with AI. And now 25 years later, this is quite the topic. Wow. It's a pleasure to have you on the podcast today. And with that, over 25 years of experience in high tech industries, I'm curious, have you seen the landscape of IT infrastructure management evolve particularly with the rise of artificial intelligence in the last six, seven months alone? But I'm curious the kind of big changes that you've seen throughout your career. Obviously, over that time, we've lived through the emergence of mobile cloud computing and now artificial intelligence. We've seen where mobile and cloud have gone. I think we're just at the beginning of AI and I think it's going to be the biggest of those three. I think it's going to have the most impact. I think this will dramatically change our lives and change business as we know it. You know, 20, 25 years ago, infrastructure management seemed like a difficult task, but we had no idea what was to come. And I think that innovation has been born out of necessity and over time, every company has been forced to become a technology company. And so the response to that is that the level of innovation and the rate of innovation has accelerated to a degree that I don't know if we could have predicted 25 years ago. When I was reading up on you, I noticed you've also got extensive experience in cloud computing throughout the years. So how has that knowledge influenced your work with AIOps, particularly given the trend of increasing complexity in cloud-based infrastructure, which I suspect is something you've seen over and over again, right? Yeah, I mean, when we've really got into cloud, the complexity there was obviously something not seen before. But one of the ways that I think my experience in cloud computing affects my approach to AI is just over time, you learn to recognize what is a kind of an incremental trend versus an industry-changing trend. If you look at something like virtual desktop infrastructure or VDI, or look at hyperconverged infrastructure, I was a part of companies in both of those areas. Those were at best an improvement on something that was really already being done. They were innovations for sure, but not world-changing. And cloud computing and now artificial intelligence, these are world changers. Increasing complexity, to me as a technologist, this is the key commonality between cloud and AI. It's not just a technological shift. It's also driving a massive shift in human skill sets required. So it's not just changing how we approach technology. People in the industry or the demographics of those people are changing by their skill sets. In cloud computing, you needed, worn out of cloud computing were things like DevOps and DevSecOps and SRE. And AI is influencing a massive change in things like data science. And so it's not just changing the technology, but it's changing the human beings that are enabling that technology. And one of the things I try to do on this podcast every day is talk about how technology is impacting businesses. But in a language that everyone can understand. And one of the reasons I invited you on here is because I think the world of AIOps still seems like a big mystery to a lot of people listening, especially business leaders. So could you possibly help demystify this field by describing some practical, yet real-world applications of AIOps that you've been a part of or witnessed or worked on? Yeah, so one of the things that I do when I'm talking about AIOps is try to draw some analogies around things that people already understand. And so if you look at the early 2000s, there was a technology emerging that was called SEM, Security Information and Event Management. So this is in cybersecurity. The idea was that the centralization and consolidation of log and event data would help with cybersecurity use cases, that you've got events and logs everywhere, consolidating those and correlating the events would be able to produce insights around security breaches or exposures. The reality is, and SEM became a core part of compliance mandates. So pretty much every, at least medium and large size company has some kind of SEM deployment, but they never really reached the promise. They never fulfilled the promise of what they would be able to do. The first generation of AIOps, which was a term that was coined by Gartner in 2016, this was really a SEM approach to IT operations. It was saying, let's take all of these events and bring them into a central location. And rather than using it for identifying security issues, the goal is how do we identify health and performance issues in the infrastructure and applications? Another key part of that is, whereas in the world of security, you not only have the endpoints and servers and whatnot in the infrastructure, but you've got security technologies. And so events were coming in from all of these systems to be correlated. In IT operations, you're looking up and down the stack and applying that same thing to, again, to health and performance. You've also got monitoring tools that are already in place. And most organizations have dozens of monitoring tools. So the first generation of AIOps was really, the goal was to deal with this problem of having dozens of monitoring tools and all of the data is in silos. And so we'll just send that data, the event data to the central platform and use machine learning to derive insights from that. In fact, the original AIOps tools before Gartner coined the term AIOps, they called themselves event management for IT operations. And even when Gartner coined that term, it did not stand for artificial intelligence. AIOps stood for algorithmic IT operations. So initially, this was not intended to be an AI thing. It was just using algorithms to correlate data and try to replicate the approach of SIM. You can't really have an acronym that starts with AI and expect the masses to not call it artificial intelligence. And so it's interesting Gartner just acquiesced finally and said, okay, it doesn't stand for algorithmic IT operations anymore. It stands for artificial intelligence for IT operations. But what turned out over the next two to three years is the world saw that AIOps faced the same challenges that the SIM tools did. The deployment times took years. They simply weren't able to reliably root cause problems in the complex infrastructures that we have today. And so somewhere around 2018, 2019, you saw a second generation of AIOps platforms. And these platforms typically emerged from monitoring vendors. So it was vendors who were already collecting more data. They were ingesting data on topology. They were collecting metrics, streaming data, et cetera. And then using machine learning and these algorithms on the backend to try to derive insights, and this made all the difference in the world to provide more context to be able to reliably identify issues. And so when done right, this is the real world application of AIOps first and foremost. It is root causing issues in IT environments. In this second iteration of AIOps, the real world is having a massive, extremely complex infrastructure and having complete visibility into health and performance of every part of that infrastructure and being able to understand which things are connected to which things, which things are dependent upon one another, et cetera. And that's crucial for the success here. And so that's the core real use case for AIOps, but there's also innovation around things like self-healing. You'd be surprised at what organizations are already doing as far as automation and being able to remediate issues that arise. But the real potential for AIOps going forward is to dramatically change a company's efficiency and really help separate themselves from their competitors, which is the key business endeavor that every company pursues. And I suspect many people listening to our conversation today, we may have opened up their curiosity. Maybe they're intrigued by the potential benefits of AIOps, but equally they might be some that are concerned about some of the potential pitfalls. So what do you see as the key challenges for widespread adoption of AIOps and how can they be better mitigated in the business environment? Yeah, I mean, honestly, the key pitfall here is, this may seem pedestrian, but the key pitfall is just not using common sense. Throughout time, vendors see a buzzword, they latch onto it because they want to catch the wave. They pivot. It's just like the, hey, we're event management, but now Gartner say AIOps, so we're AIOps. When you go to the grocery store and say, I'm going to go get some apples, you go where the apples are, you see that there are many different types to choose from, but they're all apples. This is completely not the case when you go searching for AIOps tools. You can go do your research and there are websites that all say that they're apples, but they could be oranges or bananas or even broccoli or spam. It's very difficult for customers to discern one type of AIOps tool from another. And so just like with every major project that you embark on, customers just need to examine their own use cases and match those to the vendor's capabilities. It's too easy to get enamored with a vendor's messaging or even analyst research that is complimentary about certain vendors. You really just need to do your research. You need to create a short list of vendors and do proof of concepts, whatever due diligence you need to do. The best way to avoid pitfalls is simply making sure that you're not doing what the rest of the world is doing. You're pursuing the technology that is the right fit for your use cases. The worst thing you can do is choose a vendor and have a two-year project and have no results to show for it. And this is what happened with the original generation of AIOps tools. And once you do that, you've kind of poisoned the well and you're unlikely in your organization to get support for the AIOps magic beans in the foreseeable future. And given your experience within cybersecurity, how does AIOps intersect with the realm of things like data security and compliance, which again are big topics? How is AI aiding in some of these areas? Yeah, so I mentioned the SIEM corollary before. It's kind of like a boomerang. Now that AIOps is becoming a more mature space and we're seeing tangible results, now we're seeing this applied back to other areas of the business, security being first and foremost. The idea was born out of security. And so the approach has been, I wouldn't say perfected, but absolutely refined. And that's now being applied to security and compliance use cases. And so where all of the conversations or research or messaging that you see maybe around IT operations, the vendors who are on the forefront of this are saying, okay, now how do we go back and apply this to other use cases? And that's not just security and compliance, but to any area of the business. It can be for sales, marketing, risk management, et cetera. And also, I think we should highlight as Chief Product Officer at Xenos, you guys are helping some of the world's largest organizations ensure that IT services and applications are always on. So if we were to look into a virtual crystal ball, how do you envision the future of AIOps and what can we expect to see in terms of new developments, trends or innovations in this space? So AIOps is, the way I view it, it's one example of a real sort of productized manifestation of AI. It started with better event correlation, anomaly detection was introduced, and now it's become an incredible tool for automating root cause analysis in these environments that simply can't be managed by humans. It's not a nice to have, it is required now for businesses to be efficient and successful. And so, again, going back to the previous question, now we're looking at other ways to apply it and that's what I see happening in the coming years. And so, in areas of automation, in cloud cost management, we talked about security, other parts of the business. At the highest level, AIOps will begin to deliver business insights instead of just technology insights. And it's going to act as you might infer, it's going to act more like a powerful human brain. Using generative AI, we get into something that the industry is calling observability, which is the platform being able to answer questions from a human being. So this is sort of a chat GPT scenario where your IT operator can simply go to the platform and say, hey, how does the latency of this application affect user signups? And as an example, another example, does improving the page load time on this page increase retention times? And so, people can piece together reports, they can try to pull these things together and do their own analysis. But now we've got tools that are beginning to be able to answer these questions for you and it's whatever questions you can think of. And as I said, it's kind of a chat GPT scenario where you can sit and ask whatever questions you want. It has the ability to search massive volumes of information and come back and give you the best answer that it can. And this is something that is happening in reality now, but it's getting dramatically better as every week goes by. And there is a well-documented tech skills shortage at the moment, which I always think creates opportunities for people for or from non-tech careers that maybe they want to continue the pivoting into the world of tech, but they don't have a lot of transferable skills, maybe they've been involved in tech projects in the workplace. And for people equally inside of IT, maybe they're starting to consider their options around specializing in key areas like AIOps. And as someone that's had this remarkable journey through various roles in tech, for those people listening, considering a career in tech, specifically areas like AIOps, is there any advice that you'd give based on your own experiences in your career and insights you've gained along the way? Yeah. I mean, the simplest advice for someone very early on in their journey, go get a data science degree. That's how you're going to be successful in this world moving forward. But many people like me are a little bit beyond that. Maybe it's not worth going back to college. You know, when I started off with IBM, it was truly a great experience, but it's really when this is advice that I give to various people that I come across. When I began working for smaller companies, that's when I really started to grow. You get to see all areas of the business. You can have an impact on all areas of the business and showing up every day matters. When you work for a company that has 100,000 people in it, you really are a cog in a huge machine. In a small company, you get to see so much. I really encourage everyone to consider this path. It's not for everyone, but to be able to grow in your career and be a part of engineering and sales and marketing and product management, et cetera, I think it makes you a more well-rounded person. Not just for myself, but what I see around me is people with technical chops are more able to be successful in other areas. You don't need a computer engineering degree. You don't need a data science degree to be successful, but embracing technology and learning everything you can about it will help you be successful in really any area of the business. We have people on our marketing team that can sit with any customer and go toe to toe on technology, and it's because they've embraced it. They understand what we do. They're not just creating ads. It makes them better at what they do. I just really encourage everyone to embrace technology and not take the mentality of, I'll just stay in my lane and I'll do my marketing job or I'll do my finance job, et cetera. Fantastic advice. I love having you on here. I cannot thank you enough for sharing your times and some of your insights today. Before I let you go, I'm going to ask you to leave one final gift to everyone listening. That is a book that has inspired you or means something to you that we can share on our Amazon wishlist for listeners to check out. All I'm going to ask is, what book would you like to add and why? The book I chose is The Innovator's Dilemma, When New Technologies Cause Great Firms to Fail. This could be one that's been suggested a million times or it could be one that was written long enough ago that everyone doesn't know about it. There are lessons from a book that was written 25 years ago that continue to ring true. It's really about how large companies lose market share because they're focused on listening to their customers and they're not innovating. There's a lot of good insight. There are good recommendations in this book on how to not fall into that trap. Again, it's something that was true years ago. It remains even more true today that we're taught from the beginning, the customer is always right. But one time early in my career, I said that to a customer, the customer is always right. He said, Trent, that's bullshit. I'm looking to you to tell me where to go with this. It turns out that listening to your customers is not always the right thing to do. This book is a great foundation for understanding that. Wow. I love that. I'm going to have to check that book out. It reminds me of an interview I read with Noel Gallagher from Oasis recently. He was talking about how focus groups are ruining music and how every new artist has to be, before they get played on the radio, has to be given through these focus groups. If they don't, then they don't make it on air. He was saying that back in the day, focus groups would have rejected Pink Floyd, The Beatles, Sex Pistols. They wouldn't have got it, but they were given something new and they loved it as a result. It's so important, isn't it? We don't just rely on those focus groups and customers because, as Steve Jobs once said, customers don't want what they want. It's true. The gentleman who wrote this book, he coined the phrase disruptive technologies. If we look across any industry, the things that have become the most wildly successful are the things that were disruptive and not just trying to appease status quo and not just trying to improve on things that we already know and understand. It's breaking out and innovating and doing something different. Fantastic. A great moment to end on. For anyone listening that would like to connect with you, find out more about your work or contact your team. What's the best starting point for everything? The best place to start is Zenos.com. You can also find us on socials. Encourage you to engage. Someone will absolutely engage with you if you're interested in talking. Well, I love chatting with you today. We've covered so much from what has changed in the world of IT in recent years. I mean, there's a lot of buzz around AI these days. So sorting out some of the fact from fiction is incredibly useful, but also your great story how you got into the world of technology and also offering a bit of advice for what other people should be doing considering a career in tech. I'd love to stay in touch with you, get you back on here later in the year or early next year. But more than anything, just thank you for coming on today, Trent. Thank you, Neil. It's been a pleasure. I think today we explored the evolution of IT, the dawn of AI ops, and also the role of AI in shaping our future. Learning more about the challenges and the benefits of AI, its intersection with data security compliance and its burgeoning role in so many different sectors, it's incredibly exciting. We also discussed the importance of embracing technology, even for those venturing into tech from non-tech backgrounds. I think people do have so many different transferable skills working in the corporate space. Maybe they've been a project resource or something like that. Maybe they're considering going into tech. If that is you, please go for it. I am going to check out the innovator's dilemma. It seems like a profound insight into the intricacies of innovation and market dynamics. That line he mentioned there about how large companies often lose market share by focusing too much on listening to customers and not innovating. As Steve Jobs once said, if you ask a customer what they want, they're only going to want more of the same. So I'd be interested in anybody's thoughts on anything we've talked about today. Whether you've read that book, The Innovator's Dilemma, maybe you want to check it out, whatever it is or even if you've got a question, simply email me techblogwriteratlook.com, Twitter, LinkedIn, Instagram, just at Neil C Hughes. And I'll be back again, bright and early tomorrow, lurking in your podcast feeds with more insights and in-depth discussions about the ever exciting world of technology. So stay curious, stay informed, but more than anything, thank you for listening and until next time, don't be a stranger."}, "podcast_summary": "In this podcast episode, Neil Hughes interviews Trent Fitz, the Chief Product Officer at Zenos, a pioneer in AIOps. They discuss the evolution of IT infrastructure management, the rise of artificial intelligence (AI), and the challenges and future of AIOps. Trent shares his insights on the need for businesses to become technology companies, the increasing complexity of cloud-based infrastructure, and the practical applications of AIOps in root-cause analysis, automation, and business efficiency. He also emphasizes the importance of understanding vendor capabilities and matching them to use cases, and how AI is intersecting with data security, compliance, and other areas of the business. Trent envisions that the future of AIOps will involve delivering business insights, increased automation, and the ability to answer questions using generative AI. He concludes with advice for a career in tech, such as pursuing a data science degree or gaining experience in small companies to gain a well-rounded understanding of technology. The episode ends with a book recommendation: \"The Innovator's Dilemma\" by Clayton M. Christensen, which explores how new technologies cause established firms to fail and provides insights on innovation and market dynamics.", "podcast_guest": {"name": "Trent Fitz", "org": "", "title": "Chief Product Officer", "summary": "Lean startup is a methodology for developing businesses and products that aims to shorten product development cycles and rapidly discover if a proposed business model is viable; this is achieved by adopting a combination of business-hypothesis-driven experimentation, iterative product releases, and validated learning. Lean startup emphasizes customer feedback over intuition and flexibility over planning. This methodology enables recovery from failures more often than traditional ways of product development.Central to the lean startup methodology is the assumption that when startup companies invest their time into iteratively building products or services to meet the needs of early customers, the company can reduce market risks and sidestep the need for large amounts of initial project funding and expensive product launches and financial failures. While the events leading up to the launch can make or break a new business, it is important to start with the end in mind. This means thinking about the direction in which you want your business to grow and how to put all the right pieces in place to make this possible."}, "podcast_highlights": "Highlight 1: \"AI is going to be the biggest innovation and will have the most impact compared to mobile and cloud computing.\"\nExplanation: The guest, Trent Fitz, highlights that while mobile and cloud computing have had significant impacts on technology, AI is expected to be even bigger and have a greater influence on our lives and businesses.\n\nHighlight 2: \"Innovation in IT has accelerated at an unforeseen rate, forcing every company to become a technology company.\"\nExplanation: Trent Fitz discusses how the need to keep up with technological advancements has pushed every company to become a technology company, leading to a rapid acceleration of innovation in the IT industry.\n\nHighlight 3: \"AIOps has transitioned from algorithmic IT operations to artificial intelligence for IT operations.\"\nExplanation: The term AIOps, coined by Gartner, originally stood for algorithmic IT operations. However, it now represents artificial intelligence for IT operations as AI is being applied to derive insights and automate root cause analysis in complex IT environments.\n\nHighlight 4: \"AIOps can provide business insights, acting as a powerful human brain and answering questions from IT operators.\"\nExplanation: The future of AIOps involves the use of generative AI to deliver business insights rather than just technology insights. The platform can act as a chatbot, providing answers to questions about various aspects of the business and helping with decision-making.\n\nHighlight 5: \"The biggest challenge in adopting AIOps is choosing the right vendor and ensuring the tool aligns with specific use cases.\"\nExplanation: Trent Fitz emphasizes the importance of thorough research and testing when choosing an AIOps vendor. Many vendors claim to offer AIOps tools, but it is crucial to match their capabilities with specific use cases to avoid disappointment and failed projects."}