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The Future of Automation

Insights from an interview with Sean McDermott and Andi Mann on the AIOps Evolution Podcast

Sean McDermott, CEO of Windward Consulting Group sat down with Andi Mann from Splunk to discuss the future of AIOps, artificial intelligence, machine learning, and automation. They discuss the importance of implementing machine learning, as well as address the fear regarding change. Andi gives insights into where Artificial Intelligence for IT Operations is headed and how to be ahead of the game.

Why is AIOps important?

Sean and Andi begin to talk about the importance of AIOps as a whole. Andi points out that AIOps is fundamentally the marriage of data and automation. When you apply analytics and AI techniques, data management becomes “faster, better, and easier.”

There is so much data these days that humans cannot possibly process and sort through it on their own and each time you add a component to an application, it adds an exponential amount of data. Therefore, AIOps is used to process that gap so humans can focus on decisions making around that data.

Andi then goes on to discuss the specifics of how this new concept is particularly useful. For example, even slight website glitches have been proven to direct traffic away within 3 seconds. So that one moment where it lagged could cause you to lose a high-value customer forever without even knowing. With AIOps, the outage can be avoided through pattern recognition and potentially fix a problem before it even happens. This saves you time, money and keeps potential customers. Even better, machine learning enables IT teams to predict outages before they occur. Prediction of outages will become the new norm with this technology.

Where is AIOps going?

Splunk specializes in event management and incident resolution, as well as turning “data into doing.” Their goal is not to just find the problem, but to also fix the problem. In the future, they would like to see more automation, more toolsets, more out-of-the-box machine learning, and more automation.

This will allow people to save time and get more insight into how to act on their data. It will ultimately reduce costs and increase efficiency. Andi predicts that not having in-stream processing will put companies behind competitors. IT leaders should focus on these three goals:

  • What is going to make our customers more successful?
  • What will make our products best in the world?
  • What will attract the best workers in the world?

If you do this you will be able to achieve great things. Andi makes the point that if you are not failing you are not creating enough new things.

Don’t be scared of AI

As humans, we are psychologically and biologically resistant to change. However in the end, new technologies always replace old technologies, and by using AI, we can make lives better, customers happier, and unlock things that we don’t even have or can predict today.

For people who are worried about losing their jobs to AI, you can turn that fear into motivation to up your skill sets. Get ahead of the opportunity AI presents for IT teams. AI may ultimately replace some jobs, however it will also create whole new industries. For example, 15 years ago, data scientists did not exist and now they are considered critical in companies. As we all step forward in exploring this new terrain and driving the next evolution of technology, we have an opportunity to replace the mundane with something new and exciting.

Want to dive deeper? Watch the full episode below, listen here, or view the transcript below to catch the highlights.

Show Notes:

Andi Mann from Splunk

https://www.linkedin.com/in/andimann/

https://www.splunk.com/

Transcript:

Speaker 1 (00:01):

Welcome to the AIOps evolution podcast. This series features visionary IT leaders who are paving the way for the next evolution of the IT industry. Discover the truth about where we are in the adoption of artificial intelligence for IT operations and actionable tips that you can implement to become a more effective IT change agent. In this episode, we are joined by Andy man. He is recognized and awarded as a leading technology analyst and Explorer with varied experience, delivering business results, through technology excellence and innovation. As a trusted advisor, he has proven success guiding other executives and diverse teams in technology strategy, product innovation, business development, mergers, and acquisitions. He is a published author, compelling speaker, and motivational leader for global conferences, events, panels, social media, press sales enablement, and more. And he currently serves as the chief technology advocate at Splunk incorporated the global leader in bringing data to every part of your organization. Now let’s join your host, Sean McDermott, a mission-driven serial entrepreneur, IT engineer, and AIOps visionary for this exciting discussion. So welcome back to the house.

Speaker 2 (01:20):

My guest today is Andy Mann. Uh, Andy is the chief technology advocate at Splunk is an accomplished digital business executive with extensive global experience as a strategist, technologist, innovator, marketer, and communicator for over 30 years across five continents. Andy has built success with startups, enterprises, vendors, governments, and a leading research analyst. He’s also been published a number of times and he’s on a whole bunch of lists and a coauthor, a couple of books called visible ops private cloud. He’s been the innovative CIO and he also talks and tweets on Andy Mann on Twitter. So Andy, welcome to the show. It’s great to

Speaker 1 (02:07):

Be. I tell you what, I could’ve come up with a bit of boy

Speaker 2 (02:09):

On if I wrote it myself. Exactly. Well, you and I have known each other for a gosh. I don’t even know 20 years. I mean, I think we back in the EMA days, the analyst days, right. Um, so, uh, and you are, uh, you are not, uh, you are not one to mince words, so I’m really, um, this is going to be a fun show for us. So, um, so as you know, this is really a show about talking about apps and the evolution and, and how helping companies move towards AIOps as a strategy. So I’d like to love to hear your, I mean, you’ve been all over the place. Like, I mean, you’ve been at all these different vendors, you’ve worked for an analyst firm. You’ve worked for startups. Uh, I’d love to get your perspective on AIOps, just so let’s just start by talking about how you see AIOps and why is it important?

Speaker 3 (03:02):

Yeah, so AIOps is actually hugely important. I’m seeing this directly from some of the experiences in our customers. Uh, AIOps is fundamentally the marriage of data and automation and things, you know, data analytics practices. So the idea is that you get data out of your business. You probably apply analytics, machine learning, AI, artificial intelligence, uh, and then you apply that to the activity of IT operations. So especially these days where you’re getting massive data sets coming through from real-time metrics out of cloud services, for example, potentially even out of age devices and so forth. The idea is that you apply AI techniques, machine learning essentially, but advanced analytics and you use the outcomes of that to automate processing. So it makes the process of troubleshooting, triaging, fix repair ticketing, all of that faster, better, easier.

Speaker 2 (04:04):

Yeah. I think one of the things that we talk about a lot is, and we’ll get to a question. I ask everybody and it’s going to be even more relevant in the period of time we’re dealing with right now. But it’s, there’s so much data coming in and you guys are a data company, right? I mean, you guys consume massive amounts of data, but you know, as we see it, there’s so much data coming in that the humans can’t comprehend, they can’t process it anymore. Right? So there’s this huge gap between what we can do as a human and the amount of data coming in. And it’s an AIOps is really about processing that gap. Right. And allowing the humans to focus on the decisions around that data, as opposed to trying to call through the data, because it’s just, it’s virtually impossible.

Speaker 3 (04:52):

Yeah, exactly. You know, you’re talking about, especially when we think about modern development techniques, you think about cloud native development, microservice architectures, you know, containerization, the idea of using individual components. Now, every time you break a large monolith application down into individual components, you’re creating extra traffic just between those components, if nothing else, but if every extra component or, you know, a microservice, for example, that you add to your application, you’re not adding one more connection. You’re adding an exponential growth in data connections. So this is the thing where you’re doing cloud native development especially. The idea that a human can process all these signals and understand them. It’s just not even feasible. And so the idea is that you apply the machines to these levels of volume. And you know, when you’re looking at, especially online, I’ve gotten so much of cloud native development is around online application development.

Speaker 3 (05:49):

It’s about direct connections to your customer. So AIOps becomes really critical in a couple of ways. There one is that any slight outage is now affecting a direct customer. So you’ve got to know about it. You got to know about it early because customers, we all know if a customer doesn’t get a response from your web application within about three seconds, they are gone. It is almost a linear progression. One second delay, you will lose 10% of your business for 30 seconds, 30%, nine seconds. 90% of your customers will bounce out of your website. So you’ve got to know, but there’s so much data coming out of your website. The humans cannot process it. So what we’ve done in the past is things like sampling. So we’ll look at a sample of application traffic or response time or something like that. Problem areas are that if you miss that one data point, which is a really high-value customer waiting for nine seconds and bouncing off your website, you’ve lost that customer forever.

Speaker 3 (06:47):

And you didn’t even know about it. So AIOps puts the machine to work doing what machines are really good at processing that huge volume of data. So this, let me give you a really concrete example, Sean, we’ve got one customer financial services. So we all know financial services, outages variation on estimates only around a hundred thousand dollars a minute up to millions of dollars. Literally a second. You think about these high-frequency traders, for example. Absolutely. And so what this company did was they were able to use machine learning to detect patterns of behavior in their operations environment. What does a good transaction flow look like? What does a bad error-prone, failing transaction look like using these patterns able to detect what a pattern looked like that will now teach. So we’re talking now about getting ahead of outages. Now, this company was able to get ahead of the announcement by 12 seconds.

Speaker 3 (07:49):

Now 12 seconds sounds like nothing, but if you’re an IT ops, you know that getting a 12-second early warning on a major outage, in a financial system that will cost you hundreds of thousands or millions of dollars a minute or a second, then all of a sudden a ten-second window is everything you avoid. All the trash. If you tied into automation to do automated fixes of known problems, which is very much at the heart of AIOps, then the problem gets fixed before it even happens. So not only do you avoid the outage, you avoid all the costs. Your customer never knows there was a problem so that it’s not like bounce on your website. They keep doing business. So really significant outcomes measurable in the real world early, but important.

Speaker 1 (08:39):

We’d like to take this opportunity to tell you a little bit more about our sponsor Windward consulting group for over 20 years, Winward has been helping it. Leaders run complex global networks and data centers to deliver impactful business services. Windward is the full package with leading solutions, architects, and integrators, with certifications, partnerships, and deep expertise to integrate leading ITSs and AIOps tools with leading technologies like service now and Splunk incorporated from technology implementation to integration, to managed services. Windward has your needs covered with Windward. You get strategic thinking and real-world results. Do you want to see if Windward’s ATM is a good fit for your needs? Go to get.winward.com/AIOps podcast to book your discovery meeting today,

Speaker 2 (09:31):

We do a lot of work on wall street, right? And your example is, is spot on because you think about everything’s going electronic now, electronic trading and things like that when trades are happening in, in a second or microseconds, right? And you got a delay of two or three seconds, you may have a delay of four or 500% of your competitor. That’s buying or selling or trading, you know, stocks, bonds, or whatever, other kinds of instruments. So these are, they sound like you said, they sound like small numbers, but they’re, they have their massive, massive differences in certain market segments. Right?

Speaker 3 (10:10):

Absolutely. And especially right when, when you’ve got businesses that historically have done things like build new networks to take milliseconds off transaction times if you can get a hit on an outage enormous.

Speaker 2 (10:25):

Well, and the interesting thing is, is that you could sit there, you know, some people wouldn’t say, well, I’m not into financial services. We don’t have that kind of, uh, of response time, but that’s actually not true, right? Because if you’re in any kind of digital business, which everyone’s, you know, digital transformation, everyone’s moving and you’ve got customers that are sitting there trying to get a response for three or four seconds, you know, they don’t want to hang out for that. Right. And you know, if there are new customers that are, that you’re trying to sign up for your service, let’s say you’re, you’re, you’re a, um, B to C or B2B SAS application. And you’re saying, and someone comes to your site and say, Hey, I think I’m going to try this out and they’re going to sign up for it. And it takes 10 minutes for them to sign in. And this is, you get into a spinning ball. They’re gone. Like he lost a couple of, like you said, you lost a customer. We didn’t know it. If you, if you have existing customers that are logging in and have, you know, two or three second, four second delays, they’re just going to be frustrated, right. You’re going to be that application that’s slow and nobody really wants to use.

Speaker 3 (11:26):

Employees as well. Sean. So when in the modern era, the idea of attracting and retaining talent, I’ve done research myself, personal research on the idea of the differences between technology at home versus at work. You know, we looked at a couple of years ago and the ideas around consumerization of it. And if your work, if, if your applications at work are horrible and slowly you think they’re going to recommend that as a place to work to their friends and their talented colleagues, they’re going to say, Oh, my company, we got great technology. You might want to get some grills. Right. So it’s all sort of employees, a little photo, but I think the customer engagement side is more important, but this spreads throughout the business.

Speaker 2 (12:05):

Yeah, exactly. So let’s talk about Splunk a little bit, right? So what’s Splunk’s view and of the current status of AIOps in the marketplace?

Speaker 3 (12:16):

Yeah. Look, uh, thank you. It’s one of my favorite topics. Uh, so w at Splunk where we’re absolutely committed to AIOps, um, we’re saying this is an early-stage market. Um, you know, for example, uh, the gotten a group, the analysts for them, they haven’t done a full magic quadrant on AIOps yet they’ve only done a market scape, um, good resource by the way, to have a look at all the vendors that are doing this, not just Splunk. Um, so it’s definitely a growing and emerging market. It’s having a good impact. We’re seeing that in our customers, right from retail to financial services, healthcare as well, to be able to respond quickly to incidents and problems, your big impact. So we talked about things like mean time to detect meantime, to investigate meantime, to repair the different phases of that repair life cycle. When you find problems are all being impacted by the ability to get them.

Speaker 3 (13:11):

So we’re seeing a lot of customers starting to save money, make money. You know, we did research just recently with another analyst from an enterprise strategy group that showed that a more effective use of data can add 5% to revenue and take 5% off cost. So 10% on your bottom line from using data bits. And that sort of paradigm is applying more and more AIOps as well, huge adoption in small segments, by the way, within the DevOps community, for example. So dev ops, cloud, native development, the SRE community solidly adopting data analytics, machine learning techniques and applying them to the IT marketplace. Um, some are businesses starting to get to what we’re seeing as negative MTTR. This is where you are able to solve the problem before it happens using predictive analytics. So, you know, we’re investing heavily in AIOps. We are fundamental believers in it because it helps our customers do more and do better.

Speaker 3 (14:12):

And so it’s, it’s going to continue to grow. It’s relatively early days right now. And, you know, they, AIOps originally got an acquaintance to an algorithm. IT operations, honestly, that’s a better name for it in terms of being descriptive, but everyone saw AI in a, you know, in an acronym and just assumed it was artificial intelligence. So they eventually changed it. I don’t know that there’s actually that much going on right now. I’m not sure we have fooling Turing test, but advanced analytics, machine learning, applying AI techniques to operations. This is the future of IT ops. All right.

Speaker 2 (14:48):

Yeah. I’ve been a bit outspoken about the fact that we really aren’t doing AI right. AI is a, is really a marketing term. I mean, we’re solidly doing machine learning. Right. And we have a long way to go before we get to AI. And so I think that just so that people understand that and, and it’s good to hear that you say that too, because, uh, I don’t want people to get, because also AI has got this kind of weird connotation right now. Right. I mean, not necessarily in a positive way, you know, and especially when you have people like Elon Musk coming out saying AI is, you know, the biggest threat to humanity. And people equate it to jobs and, you know, being replaced. And when we can, we got it.

Speaker 2 (15:41):

I got a whole question on that later, but, um, but yeah, we’re, we’re clearly in the machine learning space and it’s, and we’re early on that. Right. And so it’s going to be very interesting to see where this is going. So, so Splunk has this history, right. You know, you start in basically log management, you know what I mean, what 2003, right. It was in the company founded in 2003, um, and was, became the market leader in log management, like log processing and things like that. Um, obviously it’s been incredibly successful at that and expanding in the security and it operations and things like that. So how does, how does Splunk look at AIOps in the context of your overall product portfolio? And, and the reason I ask this question is that if you have someone, uh, who’s heavy into Splunk enterprise security, right? What is, what should they be thinking about AIOps in regards to their Splunk footprint and portfolio?

Speaker 3 (16:46):

Yeah, interesting. Right. Cause AIOps is very much centered around using the analytics and advanced analytics to feed information into some activity. Um, and so, you know, the activities tend to meld at the edges of what design, you know, the IOPS circle, maybe it’s overlapping Venn diagrams or something, but the idea is that you’ve used the animal analysis that you do and the insight that you uncover to feed into other systems. So you’re feeding into maybe a service desk system where know maybe it sits in a queue and eventually gets picked up. You know, what we think is that, you know, the service desk is maybe not where problems get solved. It’s where knowledge gets stored. There’s a lot of upsides to a service desk, but we certainly at Splunk believe that what you want to do with divers take action. And so taking that data from the AIOps toolsets from the analytics, taking the outputs, and feeding it into automated toolsets is a really important part of what AIOps does.

Speaker 3 (17:44):

So for example, in the, in the, uh, the event management space, taking thousands of events, figuring out which was the root cause or the proximate causes of the problem, and then using automation routines to solve that problem because it’s a known non, so what security professionals can do is follow that same practice. So you get this idea around insecurity, uh, automated response, the idea that there are no knowns in the security and penetration world, that if certain patterns happen, that indicates a nefarious action, a malicious actor. And so you can then use that to take action. So you might be able to change a config shutdown, a port redirector or an inquiry into a, uh, a honeypot instead of your production network. Uh, there are all sorts of things you can do get another toolset to start taking actions like redefining their credentials or something like this. So the AIOps concepts, the ability to type data, analyze it tight, use that for any decision includes the security operations as well. For sure.

Speaker 2 (18:56):

Yeah. Yeah. It sounds like an integration platform that does automation would be useful now, right?

Speaker 3 (19:04):

Yeah, exactly. That’s the thing, Sean, none of this is particularly used and you see all these vendors going out and talking about AI, we’ll find information for you or show you the problem. Well, that’s great, but why are you going to do about me,

Speaker 2 (19:20):

But for the audience that didn’t catch that joke that I started a company in 2004 and sold it to one of your competitors, um, that does automation and orchestration, which would be a perfect use case now of integrating all this, all these sources like, um, to then take action on it, but that’s a whole other conversation we’ll have. Um, so, um, so, so let’s talk about, um, your spunk strategy. So what, what is Splunk’s strategy around AIOps currently? Where do you see things are going? Um, I know that you’re a publicly-traded company, so you have to be careful about, you know, roadmaps and all that other stuff. So stay within the confines of that, but kind of give us an idea of where, where you see things going, like where we’re, um, and what makes, what makes unique in this?

Speaker 3 (20:12):

Yeah. Good one. Let me, let me take the second one first. I’ll talk about today and what we’re doing right now, why it’s unique. So, um, one of the things we’re doing quite a lot of work in, and this is what I’m really getting traction with our customers is around event management and incident resolution. The idea that you’re every operator knows the event store, right? When you get a thousand dollars that all pop up, I used to get this, Oh my goodness. Uh, overnight 3:00 AM. Uh, my entire console is locked because I’ve got all of these priority messages. Now it’s all caused by one thing, right? It might be a switch, goes down a storage control of files, and the vape alerts cascade from that one, immediate proximate cause. So using event analytics to find the problem, uh, applying our expertise, you said it yourself.

Speaker 3 (21:01):

We started a long time ago. We’ve been doing this game for a long, long time. We know what we talk about. So applying expertise, applying our algorithms to make it easier for our customers. So they don’t have to be data scientists, um, applying service views to these, to this AIOps environment. So it’s not just a graph of metrics from a server. It’s not just a, uh, a whole bunch of pings. It’s actually, what does this mean to my order entry system? How is my online web store responding to your business metrics? Am I losing revenue because I’m taking too long to process transactions? So the service insight, any data. So we’re taking inputs from databases. You mentioned log analytics, absolutely Splunk’s core strength, but we’re doing databases, metrics, network, traffic, API APIs, uh, traces. We’re taking data in from places like Hadoop, uh, other big data stores, um, any location.

Speaker 3 (21:58):

So on-prem on SAS honors your own kugel on as your on-prem anywhere you want. Um, it’s open. So we’re at what part of our strategy is to let our customers add their expertise too, so you can improve what algorithms into our IOPS technology, uh, use your own manipulate owls really important point here. It’s not about black box it’s trust, but verify you can see the working so many systems don’t do that. They’re just black boxes. Um, so it’s all about turning data into doing right. Not just show the problem, but solve the problem. Don’t just find the problem, fix the problem. So what we’re doing in the future is our strategy and how this plays out are big bets, more knowledge. We want to improve bid more of our knowledge and let customers embed more of their knowledge into machine learning and AI routines. Um, so inside out of the box, better integrations, more data sources, more automation, um, more toolsets integrated into that one control plane.

Speaker 3 (23:02):

Um, we want to do more. We want more data and do it into, take it into more, doing so better actions, better automation, more out of the box, more machine learning we provide with our expertise as well, but also more automation and better integration with external integration tools as well. Um, and just faster, bigger, and better showing up, taking more data using string processing, processing data in the stream rather than waiting for it to come into the central store. Um, uh, no sampling technology. So we not missing any data points. We’re getting every single data point. We’re not doing any sampling. So some of these things are just fundamentally different ways of looking at AI. And I think it’s going to set us aside and make our customers so much more successful.

Speaker 1 (23:49):

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Speaker 2 (24:51):

You guys are unique in that you actually are the data source. I mean, you’re not the, you’re not the very source of data that the object is right, that you’re managing, but you’re the closest thing to that object, right? So you’re getting data directly off that microservice or, or, or physical switch or something like that. And the ability for you guys, what you just hit upon, which I think is really interesting in stream processing right before it actually even gets to the machine learning engine and the algorithms to be able to do processing before that, to make the algorithms more efficient is pretty interesting. Right. And if you don’t have that data source, if, if you don’t control that data source, you can’t do that. Which so that’s, that’s a very unique thing.

Speaker 3 (25:40):

Yeah. The instrument processing is something that we’ve been developing over the last 18 months or so, and it’s, it’s fantastic. If you can get it, I think you can get on the beta program still. Um, it’s uh, yeah, you’re going to save time, right. As the big thing. Um, because all of a sudden you’re going to see some of these events and other notifications early because you’re looking at it in the stream, not in the platform. Um, you’re also going to be able to get more insight because what we can do now is we can process multiple streams. So nothing backlogged you anymore. You don’t have a constraint at the central platform because you’re putting in so much data, you’re actually able to process it. And pre-process it, by the way, it also is going to reduce costs because you have storage in the central platform ingest to the central platform, bandwidth to the central platform is all reduced by string processing. Um, yeah, it’s a really cool technology I think is going to really just find out if you don’t have in-stream processing, you are going to be behind your competitors. You’re going to be much slower. You’re going to see less data. You’re gonna be able to do less with it.

Speaker 2 (26:45):

Well, it’s interesting, right? Because a lot of platforms are integrating to you guys too, for the data sources, but you guys have the data source, so you were able to do things and take advantage of, so that’s interesting. So you talked about making big bets, you know, any, any other big bets you guys are looking at?

Speaker 3 (27:05):

Uh, not that I can talk about as, you know, as you we’re a public company. So, you know, always go and look at our website, uh, or currently available products and information, check out violins on ACC of course. Um, right. But right now, no, the strategy is very much around any data from any location to any problem for any answer. Um, you know, the idea of data to everything. We are the data to everything platform and our overriding strategy is built around, you know, core principles or what is going to make our customers more successful. What will make our products the best in the world, what will make our people and enable us to attract the best talent in the world as a company, that’s our core guiding light. And so when we look at AIOps strategy, it’s about a product that is better than anyone else in the marketplace.

Speaker 3 (27:54):

You know, we’re already leaders in so many areas in the magic quadrant for IBM in the IDC market share information. Uh, AIOps, uh, out of multiple analysts who are doing rankings and writings were already illegal, but were never accompanied by the sit in their loans, right? So getting better, always better, always better, and responding to customers. What do our customers tell us? Do they need innovations? You know, you’re, you’re a great innovator, innovator. Innovation is always a balance between responding to your market demand and your ideas. And that’s fundamentally what we’re looking at, trying to do.

Speaker 2 (28:28):

Yeah, no, I mean, yeah. I mean, I’ve started a few software companies and you, you gotta, you gotta really be listening to your customers, right? And the customers will guide you because you can’t, you, you can sit in, you considered and design these things and come up with great ideas and, and certainly you guys have, but until you get it out there and customers start playing with it, they’re going to give you so much feedback. And, and it’s all that’s invaluable for your roadmaps and where you’re going. And, and you’ll be right on some things, and you can double down on those and you’re going to be wrong on some things, right?

Speaker 3 (28:58):

Sure. Being at Splunk has really invaded invigorating. And you mentioned, I wrote a book on the innovative CIO. I’ve been studying innovation for a long time. I’ve built a couple of products myself, um, and, and being at a company like Splunk, where we can follow these innovative practices. We can file fast files, file fast, fail, cheap, fail forward. Um, if I literally, I guess, and the ability to innovate and try these new things, Splunk is very open. I actually have a team of advisory engineers and I want to give a shout-out to them because they’re incredible. Uh, hi, hi, acting people who experiment and try new things all the time. They’re building connectors to new data sources. They’re building new ways of processing. They’re creating new algorithms, the ability for us to innovate and try new things. And yeah, sometimes we get a file. That’s okay. If we’re not failing, you’re not learning. You’re not trying enough. And you stop. So yeah, it’s really, I just want to say, it’s, it’s an amazing thing to work at a company where innovation is done, so right.

Speaker 2 (29:58):

That’s awesome. Well, um, so I always ask this one question, um, and then we can kind of wrap up the, um, so when we talked, we touched on this earlier about AI, right. And this concept, of people scared of AI. Right. So, what’s your, what’s your kind of position on that, or how do people look at AI as a positive? How do people, um, not be scared that AI is gonna replace their jobs? You know, what’s what, what’s your just general thought process on that?

Speaker 3 (30:31):

Yeah. There’s a couple of things. I mean, I’m really bullish on, uh, uh, in a lot of ways. I think it’s going to create a whole new industry. It’s going to make some things more productive, you know, over the history of the world, humans, fear change. Right. We’re not good in a lot of ways that accept change, um, you know, psychologically, there’s all sorts of proven studies and stuff to show that. And so as we, as we grow into,

Speaker 2 (30:54):

I think there’s a lot of biology that proves that too. Right,

Speaker 3 (30:57):

Exactly right. Yeah. Um, and so I think the future of AI is, is both contributory and scary. You say, people are afraid of losing their jobs. You know what? They probably should be to an extent. I know we’re not meant to say things like this, but, you know, automation replaces jobs. It just does. It always has. Um, you know, I don’t know a lot of buggy whip manufacturers anymore, right? Because new technologies replace old technologies. That’s how it works. Um, you know, coal mining has gone through a great downturn, not through a lot of things that people might think, but mostly through automation, through machines, manufacturing in the US through machines and manufacturing, the labor base has gone down. So you know what? You should be a little worried, but that’s a motivator. You know what? I’ve automated myself out of a job multiple times because if I didn’t do it to myself, someone would do it to me.

Speaker 3 (31:48):

And so for people in ops, if you’re not adopting these machine learning and AIOps techniques, you’ll be automated out of a job. If you adopt the techniques and become the expert, you become the one who automates the system and the process. Now there’ll be other jobs that get created as well. By the way, I’m a data scientist that was not a job 15 years ago. Really wasn’t, I mean, maybe esoterically in universities and stuff. Now I go to a supermarket and they’ve got a data scientist I’m talking to, literally I’ve got supermarket customers that have teams of data scientists analyzing traffic patterns and all sorts of stuff. I think the middle ground is where are we going to settle? Typically, it’s always the middle ground. We said, well, we have these pendulums where we freak out at both aims, or we tend to settle in the middle.

Speaker 3 (32:35):

And I think we’re going to settle on a positive spin around augmented intelligence. AI, at this point, and to my mind in the near future, doesn’t replace humans at all. It all means it makes them better. If your skill sets are low enough, that AI is going to replace them. They don’t. I think you need to find training opportunities to up your skill sets, maybe become an AI expert, maybe become an automation engineer. We’ve seen this in DevOps and SRE the idea of a SyS Admin. It doesn’t really exist because you need to add more value to be a DevOps engineer, you become a site, reliability engineer. I think the same thing will happen with AI over time, we will get scared. People will lose their job, but we will use it to make our lives better, to find problems faster, to make customers happier. And ultimately we will find other activities, professions that we don’t even know about today that will continue to employ people.

Speaker 2 (33:37):

Yeah. I, 100% agree with that. So with that, I will, uh, I think we will end it on that note. Um, I, again, it’s, uh, always a pleasure talking to you, Andy. And, uh, we, we don’t get to see each other live very often, usually at a conference here, there, but, um, it’s been a pleasure and knowing you all these years and looking forward to talking more in the future where all this is going.

Speaker 3 (34:01):

Yeah. Thanks. So I appreciate you inviting me on your show. Um, always enjoy talking to you. I know it’s been almost 20 years. We’ve known each other enjoy having a conversation with you, mate. Thank you so much.

Speaker 2 (34:13):

Yeah. Now we can’t do it over a beer though.

Speaker 3 (34:16):

I know. I hope soon. So

Speaker 2 (34:20):

All right, my friend, uh, good luck and thanks for being on the show and we appreciate

Speaker 1 (34:25):

Thank you for joining us in paving the way for the evolution of AIOps and the next generation of it. Innovation. If you are inspired to dive deeper into the movement, check out all the resources available@aiopsevolution.com.

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