Windward Insights

3 misconceptions about AIOps in our modern world

Published
Written by Katie McDermott

video screencap of Rich Lane

Insights from an interview with Sean McDermott and Rich Lane in the AIOPs Evolution Podcast

Sean McDermott, CEO of Windward Consulting Group sat down with Forrester Analyst Rich Lane to discuss the future of artificial intelligence, augmented intelligence and machine learning for IT Operations in the AIOps Evolution Podcast. It is an eye-opening discussion on the realities of where the IT industry truly is with artificial intelligence and highlights key opportunities for IT leaders to join one of the most innovative evolution IT industry has experienced since the advent of the internet and email.

AIOps is a strategy, not a platform

AIOps is not a product or a marketplace. It is taking all of the business’s data and using very powerful tools and algorithms to analyze massive amounts of data to quickly identify meaningful patterns. The combination of platforms using machine learning and augmented intelligence should continue to learn and become more adept not just at identifying issues, but in predicting issues before they happen.

The 2020 COVID-19 Information Technology Economic Impact Study revealed that 84% of Fortune 1000 IT teams have eliminated new spending during the most rapid acceleration of digital transformation in our history. This means IT leaders will need to find ways to increase productivity without increasing headcount. Deploying automation strategies and leaning into AIOps early will provide the efficiency gains they need in 2021.

Artificial Intelligence is NOT replacing humans

Certain influencers of the technology world have put this idea in place that AIOps is replacing human labor, but Sean and Rich contest that this is not applicable to the IT industry and here is why. The overall premise of AIOps is to automate high-volume low-complexity tests, that are done over and over again.

Extremists could warp this idea into the rise of the next robot apocalypse, but fortunately, in the IT industry things are not that deep. AIOps allows for our engineers to focus more time on things like digital transformation, upgrading systems, thinking about future architecture, and discovering new technologies instead of spending all of their time “Firefighting” because they don’t have automation in place.

Thinking AIOps and ROI are not related is a myth

This statement is completely false. A lot of the numbers we have come up with over the past couple of years are meaningless. People need to understand the gaps in revenue that are caused by not implementing certain software.

“I contend that IT leaders really have a couple major things that they are responsible for; maintaining high reliability of your services, and reducing their risk footprint” stated Sean McDermott.

AIOps comes into both of those targets by being able to help deliver high level services to your internal customers, or iIdentifying issues in latency very quickly. As an industry, we need to understand that there are a lot of things going on in our environment that are possible risks and AIOps can help you look at those trends and anomalies to figure out if you have risk factors and how to mitigate them.

As IT leaders set the stage for the role artificial intelligence, augmented intelligence and machine learning will play in their organizations it is important to understand that there isn’t a single platform that will be the answer. While AI has gained a reputation for being a job killer, it will actually be an innovation enabler that allows the humans in the business to focus on things humans do best while offloading mundane, repetitive tasks to machines.

This is expected to be a breath of fresh air for IT teams who love to explore new technologies and will finally have the bandwidth to replace legacy systems. AIOps will empower organizations to deliver ALWAYS-ON service availability and identify issues quickly to reduce MTTR.

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

Show Notes:

Forrester Analyst Rich Lane

https://www.linkedin.com/in/rich-lane-23182aa/https://go.forrester.com/

Transcript:

Speaker 1 (00:01):

Welcome to the AI ops 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 rich lane. Rich’s a senior research analyst at Forrester focused on infrastructure and operations. He specializes in helping enterprises, leverage operations intelligence, including advanced analytics, machine learning, and AI-related techniques for application infrastructure services, performance monitoring, and management service delivery and business insights. Now let’s join your host, Sean McDermott, a mission-driven serial entrepreneur, IT engineer an AI ops visionary for this exciting discussion.

Speaker 2 (00:56):

So welcome everybody to the AI ops evolution podcasts. Uh, today my, my featured guest is Rich Lane with Forrester. Welcome rich. Ah, thank you. Thanks for having me glad to be here. Your bio is very impressive. So I’m going to read it so I don’t get it wrong, but, um, so rich serves as infrastructure and operations professional as an infrastructure operations professional Forester and a senior analyst at Forrester. His expertise is focused on operations intelligence, including advanced analytics, machine learning, AI-related techniques for enterprise applications, infrastructure and services, performance monitoring, and management. That’s a mouthful service delivery, business insights. He has a passion for automation as I do and machine learning and is, um, uh, has spent a lot of time in making IT services for valuables for businesses. He’s got, uh, he’s worked in multiple verticals like pharma finance and public utilities. He’s uh, had previous positions at bank capital and Marsh and McLennan, and Centrify a, uh, former United States Marine.

Speaker 2 (02:07):

So welcome to the podcast. Awesome. Thanks. So, uh, I’ll start off real quick. So you and I have talked in the past and had some, some really interesting conversations and I really wanted to have you on the podcast to talk. Cause I think you’ve got some great insights on this, and you’re also talking to a lot of customers out there who are calling you, asking for advice and, and telling you what their issues are and what they’re hoping to get out of AI ops. But let’s, let’s kind of start off at the beginning, in your mind. What is AI ops and why should anyone care about it? Yeah, I mean, it’s a good starting point because I think there’s a lot of confusion over that. You know, I, I keep telling people that AI ops isn’t a product, it’s not a marketplace. Um, it’s, it’s basically just taking all the company’s data, you know, and we used to think of just it data in our own

Speaker 3 (03:00):

Little silo on it, but it’s really all business data coming from marketing sales, because everything digital now putting it in a data Lake and then using really smart, intelligent algorithms against that data to understand how this is forming defined faults within our systems, hosted a number of things. And it’s that capability that has brought a different viewpoint of how we, how we view our, our business. You know, most companies are doing things, um, online now and they’re, they’re driving some part of their revenue through digital services. They’re finding the customers, uh, but they have insight into that. They don’t understand a day in the life of a digital user. No, and we know there’s no such thing as brand loyalty anymore. If you have a bad customer experience with a digital service from walmart.com, you gotta go ahead and target.com. That’s just what people do.

Speaker 3 (03:53):

Um, so we need to understand how the people are interacting with it? Is it a good service? Are they happy with it? Is it performance, the whole host of things? We can do that by bringing all the data together and putting in a context around it and then analyzing that data. The problem is we used to do it as humans. You know, we had the old day where we just kind of web server application server database server, something would happen. We’d very easily be able to say, Oh, okay, it’s the webserver that is doing X, Y, and Z. We can’t do that anymore. You think about the number of things that I know teams have to monitor today compared to even two years ago, probably doubled in size. And when I go to conferences and I talk to people and give speeches, I always asked the same question.

Speaker 3 (04:35):

You know, how many leaders here hired more people will last two years, zero show of hands. How many people have more stuff they have to monitor than they did a couple of years ago, all the hands? So there’s a gap there. And the only way to fill that, how many people got more budget? Yeah, exactly. And you know, the gap that’s there. The only way you can fill that gap is if the tools get smarter and the way we use tooling gets smarter. And we, that we use in the nation, is more prevalent. And, uh, it’s, it’s, it’s interesting from the standpoint that I, I put automation into two groupings, right? If you look at what this is trying to do with automation through things like RPA, um, you know, that’s designed to reduce headcount, right? I mean, regardless of what RPA vendors say, I believe it’s solely to reduce headcount. Um, but if you look at what we’re trying to do in it for the business overall through automation, and simply because things have become so complex and so, you know, resilient and horizontally scalable that we can’t do that. Almost we have automation, we can’t maintain all these.

Speaker 1 (05:41):

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Speaker 3 (06:32):

I contend that the amount of data coming in now with all these different components, like you said, that, uh, doubled in the last few years is creating exponential amounts of data, right? So the data is it’s, I’m blown away by the vast amount of data that’s being collected today compared to 10 years ago. Like, it’s just incredible and there’s this. So this brings up a really good topic and you and I had a very instant conversation about this. Let’s go ahead and address it right upfront. How do you answer the question of people who are concerned about AI replacing humans? Right. Elon Musk is out there talking about AI as the biggest threat to humanity. And, and you talk, you got Andrew Yang talking about automation. Uh, I think there are elements of truth in all of this, but from an IT perspective, how do you answer a question or how do you address it if people think, well, I’m just going to be replaced.

Speaker 3 (07:29):

AI is replacing me. Yeah. And that’s where I think the conversation is different for us than it is that, you know, we’re using it to sort of automate the low complexity, high volume tasks that we have that we’re doing over and over again. Why shouldn’t those be automated? Um, when you think about, you know, your typical, you know, it engineer, they should be a lot higher task, they’re paid a lot and they should be doing the complex things like digital transformation, you know, upgrading systems, uh, thinking about future architectures, bringing in new technologies, but they’re spending all their day firefighting because they don’t have the automation in place to do that. Um, I just don’t see that we’ll be able to automate very much of it. Uh, the core of it, that we do, the tricky stuff. The reason for that is, you know, if you look at, um, you know, basically incidents that you have every day that come in, you may have the same exact build on 2000 servers and you’ll have 10 different servers at any given day, give the exact same symptoms, what the problems are, but the remediation to fix those problems tends to be 10 different remediations.

Speaker 3 (08:33):

Um, just because things look similar doesn’t mean they have the same fix, you know, I just don’t see how you ever automate that kind of thing. And I have this conversation with it, leaders that, yeah, automation is going to affect a lot of different areas of our business. Um, but I just, we have domain knowledge and a specific set of skills. I don’t think a lot of things can be automated. And if, look at what happened over time. Sure. There there’s been jobs with the past 30 years automated out of it, you know, think of tape operators, backups, you know, then the robots came along and removed the tapes around. Do we just get rid of those people? No. A lot of those people have gone through two or three evolutions of their career on it. They just moved on to something else. So I don’t see it as big of a threat to us, uh, as it is to a lot of sort of like, you know, very low hanging fruit, like, you know, contract processing, things like that, uh, customer service, you know, different things like, uh, getting forms to people that email in ask or something that can be automated, but 90, I think it’s a much harder thing to do.

Speaker 2 (09:33):

Yeah. And I think that I think there’s also an evolution of skill sets, right? In that, you know, there are jobs, there are jobs that are going to exist, you know, next year. And the next few years that don’t exist today. And, and a good example is, you know, a data scientist in the IT department, you know, five years ago, seven years ago that that job didn’t exist. Now. Now there’s a lot of companies out there that are trying to hire data scientists for their IT infrastructure faster, and they can’t find them. Right. There’s a, there’s, there’s a glut. So I think there’s a, there’s a real opportunity for people who want to improve themselves, improve their careers and, and to, there will be a lot of opportunities for them. So,

Speaker 3 (10:14):

Yeah, I agree. I, I think, you know, if you think of the history of where it comes from, look, you know, before VMware became a thing, we just had windows admins and Unix admins, then VMware came around. It’s like, well, they didn’t just, we didn’t just grow them out of the ground. You know, it was like guys that were Linux admins and windows admins became VMware engineers. Then the cloud came along and we just didn’t grow. Cloud engineers were VMware guys, like, what’s this virtualization, I know how to do that. I’ll go learn, you know, Amazon or Azure or something like that. So I think there is like you said, a logical progression for people to follow.

Speaker 2 (10:46):

Yeah. All those guys that had those MSCs right now are all AWS certified. Right. And they just kind of move along the chain if you, so, so what is the, what do you, how do you, what do you feel about the current state of AI ops right now? It’s like, w what do you think is going on in the marketplace?

Speaker 3 (11:02):

I mean, I think it’s, it’s still sort of early days, even though we’ve been talking about it for a couple of years, I think a lot of enterprises are sort of, you know, stuck in the 10 years ago mindset and don’t really understand what it brings to them. And, you know, they’re struggling with things, but they don’t understand the way forward. Um, getting more traction. I think more people are asking about it, for sure. They’re asking me about it, but the biggest sort of two roadblocks are, uh, trying to show the ROI back to the check signer. You know, it’d be at the CIO or the SVP of ops, you know, if I need to go out and make us capital spend, what is it going to return to me? Um, and it’s a cultural shift to, you know, people, if you bring in, you know, an enterprise-level platform, you do 80, 85% of your monitoring and your business analytics and all the host of other things.

Speaker 3 (11:51):

Um, that means you’re likely going to deprecate a lot of other tools out of the toolchain. Um, and people don’t like that. People feel a sense of if they give up the tool that they like, what does that mean for them in the future? And we have to kind of break that mindset and thing. You know, you’re still going to have a tool is just going to be a different tool and it may be run by some other team. Um, but you’re still gonna have access to your data back. Now, you’re gonna have access to a whole lot of other data. That’s going to give you a better picture of how to run your department. Um, so we, we have to break that cultural mindset, that legacy thinking, and we have to be able to show like why this is a good idea for a dollars and cents standpoint.

Speaker 2 (12:29):

Yeah. And I think that’s that the cultural mindset fundamentally goes back to, um, personal survival instincts, right. Uh, we just talked about people are going to get replaced, and we’ve been hearing that for 20 years of, you know, you try to get rid of a tool and people just can’t get rid of the tools. And it’s not necessarily a technical capability issue. It’s a, uh, people are just, you know, they built their careers around that. Right. They’ve gotten trained, they’ve got, they’ve got all kinds of, um, uh, skills around that. And they’re afraid that they’re going to be, you know, become obsolete and not need it. But, you know, you gotta take a different mindset on that. Well, we’ll come back to the whole tools thing, which I think is a whole other conversation, but let’s, let’s, let’s talk about the ROI. Right. So, uh, how would you, how would you help people that are struggling with the, with the ROI on this? What would you tell them to do?

Speaker 3 (13:20):

Yeah, so it’s good. Good tee up without even asking. Um, I’ve actually done some research on this now, just because I get so many questions on it. Um, and I think it’s, there are two or three parts to it, how you go about it, there’s it. And we’ve always been terrible in infrastructure and operations at quantifying ROI on anything. Um, we’ve been our own worst enemies in that way, but I think there are ways to do it now. You know,

Speaker 2 (13:44):

I think it’s, I think it’s us in marketing, you know, we just have the hardest time trying to come up with ROI on things.

Speaker 3 (13:49):

Yeah. And, uh, and a lot of the numbers we’ve come up with over the years have been pretty meaningless. There’ve been like, you know, I get it like, you know, incident avoidance and noise reduction. Those are great terms to throw around, but you have to put dollars and cents around those things, uh, for, for the, for the purchasers, you know, the people that hold the purse strings to really catch their eye. Um, but I think there are some things around, you know, if, if you do, you know, I call it, uh, kind of a working title about, uh, I mean, it’s of lost productivity, right? If you have a call center that realizes that an application that goes down and say once every three months for an hour, well, you know, that affects the people that work there, you know, how much they make per hour on average.

Speaker 3 (14:30):

And you can roll that up into a dollars and cents figure. And you can say, Hey, listen, by not re-platforming this product or something, um, where we’re losing money, you know, we’re actually losing money. Um, a lot of the smarter companies now actually do know how much they have a slowdown or something on there. There, you know, an online retail portal that costs them revenue or mileage per hour or a one retailer I talked to who said, we get a half, a second to latency on our website. That’s an 11% drop in revenue for us, our second latency. Yeah. Half a second latency. Amazing. Um, and that’s incredible to know, and that’s, that’s why they need to be, they need to know about before it happens. They need to know things that, you know, there’s a male acting component of the application stack. That’s going to cause a problem but hasn’t yet that they can get in front of.

Speaker 3 (15:21):

And that’s why you know the modern AI ops platforms can CA can work with that. They know what anomalous behavior is, and you could see it building up. Um, you know, the other thing I say is that there are things you can do around, you know, if you’re going to deprecate, you know, most, most companies I talked to about 30 and 35 monitoring tools and say, well, if you re-platform, you can get down to five or eight, so it’s gonna be an ecosystem. You know, don’t worry about that, but take that amount. That’s another piece of the slice. If you deprecate all those contracts, how much does that bridge turn back to funding the project? And then it’s, it’s lost revenue opportunities. It’s, it’s working in the digital age. How much, how many performance issues are you having, the caution, you revenue on your online digital services that you didn’t know were happening?

Speaker 3 (16:07):

You know, I talked to one client that, uh, didn’t have a sort of a digital experience management solution so that they weren’t doing roam or synthetics or anything like that, went out and got the solution. And they were just like blown away by how many things he’s like, there were so many problems we had out there in the environment that we didn’t know about. And it makes you wonder how many customers were just dropping away and abandoning, um, because of this. Uh, um, and, and, and that’s, that’s actual, tangible, you know, money, you can return to the bottom line. Um, so I, I’m still working it out. I think there’s going to be two or three ways. I mean, it’s going to be like the time you returned back from workers, not, not doing low hanging fruit, low complexity cast and be automated, um, because it workers do make a good salary, um, add that back in the cost of like tools that you don’t need anymore, add that back in, and then the, uh, the revenue that you returned to the business, that good customer experience. Um, I think the three pillars of it,

Speaker 4 (17:03):

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Speaker 2 (18:05):

I think the challenge that we’ve had in it since, I mean since I’ve been in it since 1990, the early nineties, is that we talk a lot about tying ROI to the business and to revenue, but in the end, when decisions are made, it’s usually about cutting costs. Yeah. Right. And I think, I think we need to shift that mindset. Uh, I think that you know, I contend that IT leaders really have, there’s a couple of things, major things that they’re responsible for. Um, but the two that really pop out to me the most are maintaining high reliability of your services and reducing your risk footprint. Right. And I think AI ops comes into both of those, of being able to help you deliver services. You know, higher-level services to your internal customers, say if you’re a bank and a trading desk or something like that, or like you, the example you gave about, you know, optimizing your website and, and, you know, and identifying issues and latency very, very quickly because, you know, losing, losing 11% for an hour can be a lot of money for some of these high volume players.

Speaker 2 (19:23):

And I would say if you’re, if you’re worried about half, half a second latency, you’re a high volume, high transaction retail online retailer. But then the other side of it is, is that there’s a lot of things that are going on inside your IT environment that, that are really exposures to risk. And AI ops can really kind of help you look at those trends and anomalies to try and figure out are we, do we have risk points in our, in our, in our state and how do we plug those holes? And I, unfortunately, think that a lot of companies have a lot of holes when it comes, to risk. And we’re seeing that now. I mean, you’re hearing reports now about everyone going remote and VPN is not being fully secure because they run it really tested and, you know, companies, you know, having login issues and, and, you know, and, and we’ve been, we’re a pretty remote company.

Speaker 2 (20:16):

So, we handle all that. And we have two-factor authentication for our applications and integrated cloud application security and things like that, but we’re smaller. Right. And we’re, we were, we were made to be remote. Whereas, you know, you get a large global enterprise working, remote being fortunate five days working remote when you’re, maybe at best used to teleworking is, is a massive shift for them. And it’s, there’s going to be, it’s going to be a lot of opportunities for security vendors coming up. And I think it’s a good opportunity for AI ops. So I’m really interested in your research when you come out, to talk to customers about that. Um, okay, good. So where, um, let’s talk, let’s do a little bit of a shift here, right? Uh, there are vendors coming out now. So I was at an industry analysts show in the fall, and I think there were 130 vendors they’re presenting, you know, in their exhibit hall.

Speaker 2 (21:14):

And I would say a good third of them had AI ops on their, on their, on their boards. So everyone’s talking about AI ops now. Right. And I think there’s a, we went from really not a lot of people talking about it to a lot of people talking about it, and now it’s really, really noisy. So what are, what should people be thinking about when they think about AI ops? And if they’re going to start making investments now, what kind of tools are they, what categories, I mean, you have, you have independent vendors, you have, um, framework vendors, you know, same thing as we’ve always had. Right? Yeah. Um, so what’s the state of AI ops in the marketplace today?

Speaker 3 (21:56):

Yeah. I mean, I always tell customers, say, you know, the first thing you do is you sit down and you go, okay, what are all the pain points we have today that, that recur over and over again, that we feel like we don’t have visibility into thinking like, well, our, our tooling, whatever we have a process, um, we keep missing these things before we even look at products or platforms or anything. And then you got to ask too, like, where are you trying to get to, you know, where are you trying to get to for, for a business and an IT organization and back in, how are you going to help the business itself? Uh, answer questions because, you know, it has always been very, it centric, you know, all the lights are green. Um, you know, so we’re doing our job, right. Uh, I said that that’s not how we should be thinking more, should be thinking, like, how are we supporting our business peers?

Speaker 3 (22:44):

I’ll be it, sales, marketing, compliance, whoever it is, and how can we get data to them as quickly as possible and how they’re performing. So I look for systems that are starting in and starting to see a trend here where it’s great. We can monitor all the stuff in the data center, in the cloud, all the things, the applications that’s great. Um, but how are we helping the company be more competitive against their competitor? And that’s the level of sort of business analytics part of it. You know, if you look at some of the BI tools that are out there now, right. They can tell you that sales dropped off at Tuesday at noontime. And that’s it. If now what I’m seeing is from the AI ops standpoint. Well, yeah, we can tell you that sales dropped off Tuesday at noontime. We can also tell you why we can connect it to an event.

Speaker 3 (23:32):

And I think that’s something we haven’t been able to do before. We’ve always had to do a lot of manual, you know, own calling, asking people what happened at that time. Well, I’ve got to go back and check logs. Now we can. Yeah. In one of the best use case of this, I saw it was a really funny story, cause this is not my company, but, um, it was, uh, online, you know, a, I think it was a clothing apparel store based in New York, a lot of brick and mortars, but also do a lot of online business. And all of a sudden, you know, sales cratered just got flat in New York, like what is going on? And so sales, you know, they had their own dashboards to check conversion rates and compare that against marketing campaigns and email blasts. And they worked really well.

Speaker 3 (24:14):

It said, Hey, operations, you know, what’s going on in sales, in New York buttons, zero they’re like, everything is fantastic. Everything is fine. Everything is super performant right now. I don’t know what to tell you. And they were quickly able to ascertain that they upload product skews all day long, disparaging pricing models and things like that, that somebody in a spreadsheet had spelled New York wrong. And he couldn’t search in New York anymore. But, and I talked to this company and they said, I said, how long would it have taken you to file that if sales and marketing hadn’t had their own dashboard and be watching this, like, it could have been hours, days who knows he goes, but we’re, you know, cause we were thinking about it from a sales marketing standpoint like it has to be something, the data and we’re able to find it in no time and make effects. So I thought that was an unusual case of like now the business is becoming more involved because Hey, our business is coming from online sales. Now we need to all have these stakeholders in this and watching it. Um, which I think is the future.

Speaker 2 (25:10):

Good. So, so what’s um, so as we kind of wrap up here, where, what do you w where do customers start now? Right. So you and I have had a lot of conversations that AI ops is a strategy. Right. And, um, I’ve got my own opinions on this, but I’d love to hear your side. W if you’re hearing the term AI ops, right. And your, your VP of INO, and you’re, you’re saying we are hearing this all the time. What do you think the next two or three moves are for somebody who’s hearing about AI ops?

Speaker 3 (25:46):

Yeah. The biggest thing is I encourage enterprise to do is like, go back and look at your process, go back and look at your skill sets and your people in your process. Um, because this is a different world. Um, you need to think about things differently. People are going to be doing jobs in a different way. Um, in the process itself is different. If you look at, you know, swim lanes of incident management process for most enterprises, there’s, you know, two-thirds of it is manual. Um, a lot of that’s going to change their AI ops. You can change for the better, um, but, but you have to do that work upfront, and you have to work with a trusted partner that you select to say, how do I streamline this? And what can the Sims do for me? And you see that amazing term I was in pieces just, just becomes like, that’s not even a manual touch anymore.

Speaker 3 (26:31):

You know, be it tickets, or, you know, just alerting or suppression of alerts or whatever, filtering of alerts. Uh, that’s all done automatically now, which returns a lot of value to the frontline workers actually fixing the problem versus running around, trying to find answers. Sure. Um, so I encourage them to say, you know, understand that it’s not like the old days where it was, you know, 12, 18-month sort of, you know, roll out of a new product is a high time to value. Now don’t worry that it’s going to drag on and on. I’ve seen large-scale enterprises bringing in AIOps enabled platforms in, you know, somewhere between 30 and 90 days, fully up and running on it and turning off the old system. And then sure. I’m getting all these insights now. Um, so I, I say don’t hesitate, you know, if you’re on a ten-year-old install of something, um, you probably are already behind the times if you’re not evaluating your strategy. And that, that can be different depending on how your business model is run, you know, where you start, you know, a process, but you should be thinking about it and talking to people. For sure.

Speaker 2 (27:38):

Yeah. So, so it sounds like one of the things you should start looking at is finding a partner, right? Because somebody who really understands how what AI ops is about how it can be deployed. Um, what, what are some of the, um, what is some of the gotchas that you think people should be looking out for right now?

Speaker 3 (27:58):

I mean, I think the biggest thing is, you know, bringing it in and, and not, not making the commitment to it. I think that’s one of the biggest things is that it has, the equipment to come from the top. If one business unit, you know, brings in a new system, it’ll be good for them, but it’s not good for the entire company. It’s not a strategy at that point. You’re just buying another toolset. And I see companies do that, that at a very large scale global enterprises, they’re like, well, we just, weren’t putting this one in business unit wherever, like, but that’s, that’s not a strategy. That’s not a, you know, you’re, you’re still hamstringing yourself and not seeing the overall agenda. I think also now more than ever people leave out the digital performance management piece, um, you know, and I always ask customers say, well, what’s your strategy?

Speaker 3 (28:43):

They’re like, what do you mean? Like, well, you have to understand the user that’s using your service, how they’re interacting with it, how they feel about it, you know, is it, is it good? Is it make them happy? They get angry with it. Um, and if you don’t measure that you’re missing a huge piece of your overall sort of AI ops strategy. That’s the real power of it is from the second someone that comes to interact with you gets behind the keyboard and type something into your system, into the far end that it goes through to get that answer and return to them. We need to understand that entire pipeline. Um, and if you don’t have all the data in one place, you can’t do that. So I try to tell people, it’s like, it’s a different way of looking at the service you’re providing. It’s not a siloed method. It’s a complete end-to-end

Speaker 2 (29:27):

Yeah. Start with the user, right? Yeah. Put yourself in the user’s shoes. Yeah. Yeah. And then everything, everything will get a lot more clear when you actually go from the user side and you start looking at what you’ve deployed and you realize that it’s not meeting the needs and you can see the clunkiness and things like that. But I think we, IT in general has, has a reputation for just kind of deploying technology out there as they see it, as they see the need or meeting requirements without really having that, that side of understanding how it’s going to be used the user experience of it. And it’s one of the things that I talk about a lot of looking at it, it projects as a product management process, much like a software company, you’ve got product managers that are out there talking to customers, trying to understand what customers want. So they could feed that back into the engineering organization to build the product, according to what customers want, what customers need. Uh, we in it don’t have a really good track record of doing it that way. Right. So,

Speaker 3 (30:30):

Yeah, we talk a lot about that, about, you know, now to touch upon what we talked earlier about, you know, the organizational change that has to happen. And that’s the thing we have to do. We have to build teams, that’s product-focused teams, not just by silo disciplines of what you do, you know, if your Linux admin or windows app, and you have to be part of a product team. Um, it is really because, at the end of the day, it’s all about delivering a great customer experience. I mean, that’s what we’re in the customer experience business now in it. And I think a lot of IT leaders don’t understand that yet.

Speaker 2 (31:02):

So if you’re an IT leader, let me, let me throw a hypothesis out there. If you’re an IT leader and you don’t really understand all this technology, right? Because you’re leading large teams, maybe 500,000 people. Um, and you’re hearing this podcast and you’re hearing about AI ops. Is it a good test to basically go to your staff and say, what tools are we using for monitoring today? And how old are they? And if they come back and they say they’re more than five or seven years old, you know, you’re not going to get to an AI ops strategy that way. So you can say, okay, we’ve got to start doing things differently. That’s the starting point. Is that, is that a, is that a good, you know, first gate,

Speaker 3 (31:49):

That is a very good, simple way of looking at it, you know, because those five, seven-year-old tools aren’t going to take account, um, you know, cloud-native technologies. Sure. Um, you know, or they’re not going to get the support dev-ops initiative. Uh, so I think that’s a fantastic starting point.

Speaker 2 (32:07):

Good, good, awesome. So, um, so how do, how do people learn more about you? You’re obviously publishing a lot of stuff on Forester. I mean, do you have to be a forced subscription or subscription to get access to, to some of your, your muses?

Speaker 3 (32:22):

Uh, unfortunately, that’s how I get paid. Um, but, uh, there is, we do think called reprints. So I have a piece of work that comes out that resonates with, you know, a particular group of customers. Um, they’ll publish it through their website. I’m sure we’ve all seen it and, you know, download the Gardner report, download the Forrester report. What has, yeah. Um, so there are mine floating out in the wild. There’ll be more to come. Sure. But, uh, yeah, the vast bulk of my research is from Forrester clients.

Speaker 2 (32:50):

Well, I’m really looking forward to your, uh, your next set of research around AI ops and, and what’s going to be coming out of there. So rich, I appreciate you coming on the podcast today and taking the time out. I know you have a busy schedule and get back to helping all these customers figure out how to, how to do AI ops.

Speaker 3 (33:08):

No, it’s been great. I love having this conversation. You know, Andy time, I can get information out there and to answer the hands with other people that used to do my job, um, or I did their job, whichever it is. Um, it’s always good. That’s why I took this job to try to be as helpful to my old crew back in the operations teams. So, uh, hopefully, be more good stuff.

Speaker 2 (33:30):

Awesome. Excellent. That was great seeing you and, uh, we’ll connect up soon.

Speaker 1 (33:34):

Thank you for joining us in paving the way for the evolution of AI ops 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.