3101: ServiceNow on Observability, AI Ops, and IT Innovation

[00:00:04] Are you prepared to unlock the deeper understanding of observability and its significant influence on IT operations management?

[00:00:13] Well, today we're going to engage in a fantastic conversation with Gab Menachem.

[00:00:20] And he's the Vice President of Product from ServiceNow.

[00:00:24] And with extensive experience in evaluating IT operational visibility through cutting edge observability solutions,

[00:00:32] his insights are invaluable.

[00:00:35] And as organizations aim for high-end efficiency,

[00:00:40] grasping the nuances of effective IT operations and the influence of technologies like AI is becoming impossible to ignore.

[00:00:48] So today we'll discuss how observability goes beyond mere monitoring

[00:00:53] to offering essential insights into IT environments,

[00:00:57] enhancing decision-making and diminishing operational noise,

[00:01:01] and we all know what that's like.

[00:01:03] And we'll also talk about integrating AI into observability tools

[00:01:08] and how we can simplify processes and reduce alert fatigue, which is a huge problem too.

[00:01:16] So today we're going to discuss the evolving realm of IT operations management

[00:01:21] and the transformative impact of technology.

[00:01:23] But enough scene setting for me. Let's get today's guest on.

[00:01:28] So a massive warm welcome to the show.

[00:01:31] Can you tell everyone listening a little about who you are and what you do?

[00:01:35] Thanks for having me, Neil.

[00:01:36] My name is Gabby Menachem, but most people call me Gab.

[00:01:40] I'm a VP of product at ITOM and ServiceNow, where I oversee our IT operations management,

[00:01:47] visibility, AI ops, and cloud observability products.

[00:01:51] At ServiceNow in general, we boost productivity with the Now platform,

[00:01:56] which is a unified platform that connects people, processes, data, and devices for seamless workflow management.

[00:02:05] ServiceNow ITOM provides full visibility and governance over complex IT environments from on-premise to cloud,

[00:02:15] and we use predictive analytics and generative AI to help customers proactively resolve issues,

[00:02:22] saving time, costs, and ensuring reliable service.

[00:02:26] My passion throughout my career has been about using technology to empower people,

[00:02:31] and I'm excited about what we're doing at ServiceNow to make the world better from that perspective.

[00:02:38] And that's one of the reasons I invited you on the podcast today.

[00:02:41] I love that ethos of using technology to empower people.

[00:02:45] It's something that we don't talk about enough or certainly not promote enough.

[00:02:50] And one of the things I try and do on this podcast every single day is demystify some of the top tech trends

[00:02:56] that we keep hearing and reading about and maybe bust a few myths and misconceptions along the way.

[00:03:02] And one of the big trends at the moment is observability, and that market is set to grow rapidly.

[00:03:08] So I've got to ask, I mean, to set the scene tonight, what do you believe are the most common misconceptions IT leaders have about implementing observability solutions?

[00:03:18] There's probably a few, but maybe we can lay them to rest today.

[00:03:22] What would you say they are?

[00:03:23] Well, that's a good question because it's very timely.

[00:03:26] I think the market is definitely shifting on observability.

[00:03:29] And probably the first misconception I would point out is that observability is not the same as monitoring.

[00:03:37] Many of our customers talk to us about this.

[00:03:41] And while both provides insights into system performance, we find that observability goes a step further.

[00:03:48] It doesn't only tell you what went wrong, but also provides the context needed to understand why things happen.

[00:03:57] And this is essential when troubleshooting complex environments.

[00:04:01] Many of our customers at ServiceNow manage daily.

[00:04:04] Another misconception is that more tools will automatically lead to better outcomes.

[00:04:10] So many customers talk about buying another tools or a set of tools because there's a new technology and they believe that adding multiple observability tools across their infrastructure and applications will give them better insights.

[00:04:28] What we find in real life is that more tools lead to data silos, complexity and alert fatigue.

[00:04:36] And the real value, even if the new tools have a lot of new capabilities, is by consolidating that data across these platforms and integrating the tools into a cohesive unified system.

[00:04:53] What most customers call a single pane of glass, which is kind of the epitome of success of that idea.

[00:05:02] And then probably another one is that observability is not only necessary for large enterprises or cloud native environments.

[00:05:14] Many, many customers believe that they only need observability if they are cloud native.

[00:05:20] But what we really see is it's valuable for organizations of all sizes and in the cloud and also on premise or in a hybrid situation, which a lot of our customers are.

[00:05:33] And as systems grow and become more interconnected, even smaller IT environments can benefit from observability.

[00:05:42] And I suspect there's people listening all around the world that work in this space, nodding in agreement, saying, yeah, at last, but you know, so we've got somebody that gets this.

[00:05:51] And I was reading about you before you came on the podcast.

[00:05:54] I see that you also emphasize the importance of using existing tools effectively.

[00:06:00] And if we don't, you keep piling more tools on top of that, of course, I would imagine it would add to technical debt as well.

[00:06:07] But what strategies do you think organizations should be using to maximize the value of their current observability investments rather than just simply adding more tools on top?

[00:06:18] I love that question because it's very real world to many customers.

[00:06:22] ServiceNow serves north of 10,000 enterprise customers.

[00:06:28] And I think when we look at their current estate before absorbing any new tools or transforming themselves, there's always the first question of how do you maximize the existing investments and leveraging them?

[00:06:44] So I think the effectiveness of observability really depends on the tools you have and how to integrate them in a way that centralizes the data and improves the team collaboration.

[00:06:58] Most of the solutions I see in this space that really boost team productivity are around collaboration and the opportunity to fully integrate and align to business goals.

[00:07:12] So having an observability tool, and that's the language you use a lot, is about getting the instrumentation of the environment, gathering the data, and bringing it into the centralized place where you can integrate these observability tools into what the market calls AIOps.

[00:07:34] And that's the place where we correlate these events, make sense out of them, and also take the action to start resolving and appropriate the right people.

[00:07:46] So investing in process and being able to think about observability as an instrumentation and a way to take action is the more holistic approach, the one I see customers being much more successful with.

[00:08:00] And then lastly, I can't go without saying it's really important to implement any kind of change of your IT environment or methods of work while thinking about success of implementation.

[00:08:17] Meaning, take the approach of focusing on one business process that you really care about, that is worth a lot to you, make the change across the board, and then reap the benefits, be able to say to people, what was the business benefit of this?

[00:08:36] Articulate it in a really defined and sharp way.

[00:08:41] That way, that way, it's easy to transform an organization.

[00:08:46] Any kind of horizontal approach that we see where customers just implement technologies is finding a lot of detractors within big organization, and that's very important to notice as well.

[00:09:00] And in my former IT life, I did spend a lot of time with teams whose day began with those infamous morning checks and alert fatigue meant that many of those alerts often got ignored for a whole variety of reasons.

[00:09:13] So, how do you think integrating AI and automation into observability can improve decision-making and maybe even finally reduce alert fatigue?

[00:09:24] Are there any real-world examples of maybe how this has benefited a business as well?

[00:09:29] And the reason I say that is there's a lot of hype around AI and automation, but there's also a lot of business leaders struggling with that ROI question and value and what problem we're solving here.

[00:09:39] So, are there any real-world examples with that question as well?

[00:09:42] Certainly.

[00:09:44] And I think that's a perfect question for, again, a real-world scenario.

[00:09:50] I was really excited when ChatGPT came out.

[00:09:54] I felt like it will create a new wave of people believing in the AI hype because all of a sudden, everyone was able to see what AI can actually do for you and how systems and machines can actually think and help you do everyday chores.

[00:10:14] So, it made for a very big transformation in the market.

[00:10:20] I think now people are more accepting of the fact that AI in IT can be much more than it was in the past.

[00:10:28] So, to answer your specific question, I think the integration of AIOps and observability enhance how organizations manage and optimize their systems.

[00:10:39] AIOps specifically, as a term that was coined by Gartner, I think, can analyze large volumes of data and help extract critical insights more quickly.

[00:10:50] And in the majority of use cases, it acts as a foundation, filling the gaps, ensuring that all relevant data is available to make informed decisions.

[00:11:00] That's important because in a big enterprise with many disparate tools for observability and other contexts, it's hard to do this as a person for every issue within IT.

[00:11:14] Now, one of our customers, you've asked for an example, one of our customers within the IT industry transformed their employee and customer experiences while boosting productivity using ServiceNow.

[00:11:30] Initially, they struggled with application sprawl and relied on disconnected tools to manage a wide range of customer services.

[00:11:41] And their employees faced challenges due to absence of unified support system and limited knowledge sharing capabilities.

[00:11:50] And it made it very difficult to access help efficiently.

[00:11:55] And this usually, you pay attention to it because you're looking at resolution times and those become lengthy and it leads to significant user dissatisfaction and practically complaints to IT.

[00:12:13] The company also mentioned that several applications that are updated sporadically without a clear lifecycle management strategy are suffering from all sorts of outages that could have been prevented by a better process.

[00:12:29] So, after adopting several of our customers like ITSM, IT operations management where I am from and a risk management and governance, our customer was able to ensure service availability and empower employees to better serve customers all on the ServiceNow platform.

[00:12:50] When you look at a problem like IT.

[00:12:54] This is very central to many of our customers.

[00:12:56] customers.

[00:12:57] They come to us with this dissatisfaction that they see from customers and employees around their IT.

[00:13:04] And building a process into a system where the system is making you adhere to the process is central to being successful.

[00:13:15] Observability and AIOps in particular are one area where it's really important that you use AI to be successful because the breadth of data is just enormous.

[00:13:29] It's really hard to do as a person.

[00:13:32] So, I find that customers and this customer in particular as a good example are able to start very quickly get value from using AIOps and using observability tools connecting to the AIOps system.

[00:13:49] And that's my real world example.

[00:13:53] Absolutely love that.

[00:13:54] And also, I think in a corporate world, it doesn't matter if you're in the UK where I am, where you are in the US or indeed anywhere where anybody listening is.

[00:14:04] One thing that we all share and can possibly agree on is there are way too many silos out there.

[00:14:11] One of the things that put ServiceNow back on my radar is how you advocate for a unified approach to observability.

[00:14:19] So, how can businesses achieve that single pane of glass to integrate data sources and get to that utopia of enhancing operational visibility and finally removing some of those silos?

[00:14:30] I find that most customers that talk to me daily are looking for a service level integration of their tools.

[00:14:40] And by that, I mean that their holistic view, single pane of glass, as you call it, is centered around a specific service or application.

[00:14:51] So, when you walk in the hallway and you say to someone else or maybe these days over Zoom, I have a problem right now.

[00:15:01] Usually, you're referring to the service that you're getting, something like my SAP has an issue or I couldn't log into this system.

[00:15:10] And that designation is the common denominator around many of our customers need to consolidate.

[00:15:20] So, as you look at how to build an effective single pane of glass, most of what we see is the need to bring all the data sources.

[00:15:30] And in this case, we're talking about observability, but other data sources that are important are organizational.

[00:15:37] Who's working on this?

[00:15:38] The business.

[00:15:40] How important is this application to keep up?

[00:15:43] What are the costs associated with it?

[00:15:45] Where does it run?

[00:15:47] What is the risk posture of this?

[00:15:49] And this is basically, in a back-end world, this is what we call the CMDB or our system of record for IT, the term that ServiceNow really pushed into the market and is so widely accepted.

[00:16:07] When you go into the user interface part of this, building a single pane of glass as an approach, help companies harness AI and automation in a way that maximizes the value of these observability data points.

[00:16:25] So, in the case for ServiceNow, we have AI models that continuously learn from the operational environment, not just observability, but also these disparate data sources.

[00:16:38] Not just our own capabilities, but any vendor that you have leveraging your existing investments.

[00:16:46] And then using various workflows like triaging, root cause analysis, our machine learning algorithms help you analyze incoming data and identify patterns and correlate the events across the IT landscape.

[00:17:01] So, you're much more effective at discerning what is important to solve, who is impacted, who should I contact to solve this issue, and all of that in a system that adheres to a process and the governance guidelines that you want to have.

[00:17:18] And as an exchange manager, I must admit, I still get flashbacks from how change management often poses huge challenges, especially for large-scale IT transformation.

[00:17:29] So, to tackle this problem, because I know it's going to be very important to a lot of IT teams out there, what advice would you give to organizations struggling to integrate AI opts into observability into that existing infrastructure?

[00:17:44] I suppose, again, this is a question you get a lot, but any advice or tips you can offer around this?

[00:17:51] So, coming back to something I said a little bit earlier, I think the most important thing is to start an implementation with a clear goal of what you're hoping to achieve and make it incremental in the sense that first you need to be successful with the full gamut of the capabilities.

[00:18:14] on one business unit or business outcome that matters to leadership.

[00:18:22] I see that as the most important part because when implementations are successful, companies start proliferating that into more places and people are excited to work with you.

[00:18:37] Otherwise, it makes for a pretty hard exercise if that takes too long.

[00:18:44] And in just the nature of business in a big enterprise, everything takes a little bit too long.

[00:18:49] So, it's important to have that clear success story in mind and as a way to show everyone it's possible.

[00:18:59] Then second, I think thinking about automation and automating routine tasks as a priority is another thing I see as a trend these days.

[00:19:13] Beyond thinking about automation as a first-class citizen, I think there needs to be a data layer.

[00:19:19] And nowadays with Gen.ai, everyone is about having as much data as they can, saving it for improving their processes and believing that data is an asset.

[00:19:30] I think it's a much easier thing to do and promote.

[00:19:35] But then how do you take the action to build automation in an effective way?

[00:19:42] There's a right set of tools.

[00:19:45] And ServiceNow, by the way, has a few of these tools to look at your past incidents, to look at your processes and help you with process optimization, help you with understanding what is worthy of an automation,

[00:20:01] ingest the data from legacy systems, cloud platforms, third-party tools, and basically make the determination, where should I put my efforts to get a business benefit?

[00:20:14] So I guess the overall theme of how I'm thinking of this is lead with the business benefit, try to use the systems you have and the data you have to understand where to start,

[00:20:29] and then stop at every step of the implementation to articulate the value of already created as a motivation for the next step.

[00:20:41] And I think observability and AI ops are often just seen as technical solutions and business stakeholders may be guilty of dismissing it as an IT problem,

[00:20:52] and they just want their apps to work and very little interest in that stuff.

[00:20:55] So how can organizations better ensure that alignment between those IT tech teams and the business leaders around the organization as well to create that shared understanding of the tools impact?

[00:21:09] Again, is this a challenge that you see a lot?

[00:21:11] I think this really touches deep in my heart because, as I said in the beginning, being on the front of trying to empower people with technology,

[00:21:21] really the first place you meet them is being able to explain to them how their lives are going to be different when they have this technology in their hands and in their daily lives.

[00:21:35] So when I talk to leaders about demonstrating tangible value of AI ops and observability,

[00:21:43] the majority of customers are asking for ways to exemplify and articulate that value.

[00:21:51] I think clearly communicating and demonstrating the value of AI ops and observability for non-technical stakeholders is a key concept.

[00:22:00] And from my experience, it really goes into three specific places.

[00:22:10] First, there's a faster incident resolution cycle that reduces the revenue loss during an outage.

[00:22:17] That is something that business leaders understand.

[00:22:21] They feel it when there's an outage, the need and urgency to solve it as fast as possible.

[00:22:28] And by being able to measure these things and the percentage of improvement, that's definitely something that speaks to business stakeholders.

[00:22:38] Second, preventing disruptions that impact the customer satisfaction has to do with reputation,

[00:22:47] has to do with the kind of calls they get from customers that are either on how good their service is or how bad it is.

[00:22:55] Definitely speak to business stakeholders and are, again, something that they intuitively understand.

[00:23:03] And then lastly, I think reducing operational costs as a way to prevent outages and put resources to do proactive work as opposed to reacting to incidents.

[00:23:19] That's, again, a place where we're always in business seeking a way to improve efficiency, to reduce cost.

[00:23:27] So any way to articulate reducing of operational costs is something that business stakeholders are excited about.

[00:23:36] Now, coming back to how do you do all of this in observability and AIOps, I really find that a lot of the vendors in this space are good at articulating value in these terms I just mentioned.

[00:23:49] But I also think when someone goes out to implement any kind of solution, they need to be able to use their existing data to make the dollar value projection of the implementation ahead of time using their own data.

[00:24:10] That's something that we do a lot with service now customers because many of them use many of our capabilities.

[00:24:19] We have a lot of their data to be able to analyze and show them how much value they're going to get.

[00:24:25] And then demonstrating it in dollar terms is the right currency to talk to leadership about.

[00:24:34] And I think we should also highlight that the problems that we're talking about in this episode today are becoming increasingly complicated, especially as we see more businesses shifting to hybrid cloud environments.

[00:24:46] So what role do you see observability and AIOps playing in managing these increasingly complex infrastructures?

[00:24:53] In today's data-driven landscape and with growing complexity of these systems, as you mentioned, hybrid and also all sorts of new innovations in kind of infrastructure that people run their applications on.

[00:25:10] I think AIOps simplifies this process by quickly pinpointing root causes and allowing for faster resolution.

[00:25:18] Observability really offers the deep visibility into the entire IT environment by collecting and correlating and analyzing data from disparate sources like logs, metrics, traces across multiple systems.

[00:25:33] In hybrid environments where workloads shift between on-premise infrastructure and various cloud services, observability really helps you build a unified view of all the dependencies, performance, and system health.

[00:25:51] And that's critical to understand whether the business is running in the right way or not.

[00:25:56] Now, these sources of information do not live only by themselves.

[00:26:05] You really need to mesh them with your org structure, with your processes such as root cause and remediation and automation.

[00:26:15] So being able to do that across hybrid environments requires a platform that can usually take action through various third parties.

[00:26:25] That's where integration is really key.

[00:26:28] Instead of thinking just about one solution that solves everything, I think the better outcomes come with being able to use AIOps and observability from different vendors and then connecting those through a platform like ServiceNow to orchestrate across all of them with the service in mind.

[00:26:52] Like I said before, service is the common denominator of all of our customers.

[00:26:57] Thinking in terms of service and business benefit is the right language to have a successful implementation.

[00:27:05] And then being able to take disparate sources and vendors into one platform is where the value really comes out and in a faster way.

[00:27:16] And as we race towards 2025, lots of business leaders listening will be beginning to look ahead and how they can work differently or change things up for the next year.

[00:27:27] So are there any trends or innovations that you see shaping the future observability and IT operations management in the next few years, particularly as the world of AI continues to evolve?

[00:27:40] I understand it is almost impossible to predict right now because things are moving that fast.

[00:27:44] But are there any trends that you're seeing, whether that be in technology or just the kind of conversations that are happening out there?

[00:27:51] Oh, that's the question that's going to get me.

[00:27:55] I feel like Gen.AI has been such a big disruptor of every industry and definitely within observability in AIOps, we're seeing that ideas like unified observability that have been in the market for a while and have been interesting to customers are now being disrupted heavily by this idea of Gen.AI.

[00:28:20] I look in at my data and AI playing a crucial role in consolidating this data, providing a more comprehensive view of system health and performance.

[00:28:32] I believe that AI is here to help us with the tasks that are not just done by people today in a way to do automation, but also in a way to bring the action part closer to the resolution.

[00:28:49] Meaning if today the part of triage and taking action is really connected asynchronously through systems with tickets.

[00:29:01] I believe the future is much more integrated in the sense that AI will be used to help get to the root cause and also recommend a resolution.

[00:29:15] In many cases also take the remediative action through a third party system that can take the action and do everything the process dictates in a big company, like provide the right records for change management, run the approval chain, do everything that is important from a risk posture and compliance.

[00:29:39] All of these are really important for observability and AI ops to be better integrated in an enterprise environment.

[00:29:48] And I believe the future is super bright for what Gen.AI can help us do because a lot of these processes that are critical to running business effectively were reducing the pace of how enterprises are able to innovate.

[00:30:04] I think Gen.AI these days being able to do so many things for us is going to help us move much faster and is bringing bigger companies into the realm of innovation and effective innovation, which I'm super excited about.

[00:30:20] And I think one final challenge that everyone is facing right now, no matter where they're listening in the world, is that almost pressure to be in a state of continuous learning.

[00:30:30] Now, as someone that's almost ahead of the curve most of the time, I've got to ask, where or how do you self-educate?

[00:30:38] How do you keep up to speed with the pace of change, especially the pace of technological change?

[00:30:44] Any tips or advice you can share around that?

[00:30:48] Yeah, I prioritize continuous learning by testing emerging technology like Gen.AI in real world challenges,

[00:30:57] like planning a trip for my family to a national park or doing something at work that requires a lot of manual work.

[00:31:07] I find that this hands-on approach really drives my motivation to see how technology can solve practical problems and what the limitations are.

[00:31:17] So I really lead with action to learn.

[00:31:23] As I learn about something new, I immediately try to use it in something mundane for a daily task.

[00:31:30] And that keeps me both interested about the outcomes, but also makes a real-world scenario much more vivid for me and keeps the motivation high.

[00:31:42] And then to keep a healthy balance to it, I combine the tech exploration with outdoor activities like camping with friends and family.

[00:31:54] I think sharing what I've learned around a campfire really sparks new perspective and makes learning a shared experience, which I love.

[00:32:04] I think that's a perfect setting to learn with friends and with colleagues.

[00:32:11] And then lastly, obviously, I love to listen to podcasts like this one.

[00:32:16] Oh, fantastic.

[00:32:18] You know, I love especially about how you just said that.

[00:32:20] I think it's very easy to get carried away with just the technology side of things.

[00:32:24] But I love how you mentioned camping, sitting around a fire, sharing stories.

[00:32:28] And I think there's something quite beautiful in that.

[00:32:30] And it almost brings us full circle as we began talking about using technology to empower humans and certainly not replace them like we see in some headlines out there.

[00:32:40] And I think you started a real inspiring conversation here.

[00:32:45] And for anyone listening that would just like to find out more information about anything we talked about today or connect with you or your team,

[00:32:53] where would you like to point everyone?

[00:32:56] I think anyone wants to learn more about ServiceNow can visit ServiceNow.com to learn more about our products and capabilities.

[00:33:05] I'm also super active on LinkedIn.

[00:33:06] So please reach out to me if you're interested in anything we do.

[00:33:11] And we'll be looking at the comments as well.

[00:33:14] So very interested in what people have to say about our call today.

[00:33:18] Awesome.

[00:33:19] Well, I'll get the links added so people can find that nice and easily.

[00:33:22] We covered so much there about hiring effective observability and how it isn't about accumulating more tools,

[00:33:29] but rather how you use your existing ones that you're already paying for.

[00:33:33] I'd love to find out more about what people listening think about integrating observability with AI and automation to accelerate information,

[00:33:43] relay, reduce alert volumes and connect disparate components for more accurate insights.

[00:33:48] So many big talking points there, but just thank you for starting this conversation today.

[00:33:53] Thanks, Neil.

[00:33:55] Thanks for having me.

[00:33:55] Well, today we traverse through the realms of AI integration, alert reduction,

[00:34:01] and the pursuit of unified, of a unified observability approach.

[00:34:06] And as I find myself reflecting on our discussion today,

[00:34:11] I want you all to consider how the insight shared today could maybe help or even transform your organisation's IT operations.

[00:34:19] But of course, the big question is, it's not just about passive listening.

[00:34:23] What steps will you take to integrate these advancements into your own strategy to ultimately enhance operational efficiency and decision making?

[00:34:33] Yep, that's right. This is where I invite you to continue the conversation with us and share your thoughts,

[00:34:39] your experiences on how you're planning to apply today's insights into your professional setting.

[00:34:46] So as always, techblogwriteroutlook.com, Twitter, LinkedIn, Instagram, just at Neil C. Hughes.

[00:34:51] Let me know your thoughts.

[00:34:53] What are we going to talk about tomorrow?

[00:34:55] Well, I don't give away spoilers that easily.

[00:34:58] All I ask is you join me again tomorrow.

[00:35:00] We'll discuss something completely different and have a little fun along the way.

[00:35:05] So hopefully I will speak with you all then.

[00:35:06] Well, bye for now.