ServiceNow, Inc. (NOW) Earnings Call Transcript & Summary

Welcome and thank you for joining us for today's event. Before we get started, we have a few housekeeping tips that will help make your experience more enjoyable. First, today's session is being recorded and you are currently in a listen-only mode. The ON24 room allows you to adjust resize all panels that appear on your screen. [Operator Instructions] We'll take questions -- as many questions as we have time for. And if we happen to run out of time, we'll circle back with you after the session with an answer. We also invite you to check out items available to you in the resource list panel available at the bottom of your screen. And if you experience any technical difficulties during today's event, please enter a question into the Q&A panel and we'll be happy to help resolve any issues you may be experiencing. One more thing. Your feedback is extremely valuable to us. So please fill out the survey by clicking the icon that looks like a clipboard below. We'll remind you about completing the survey at the end of the webinar. Finally, thanks again for joining us. We hope you enjoy today's webinar. Now let's get started.

Good morning, everyone and thank you for joining today's webinar on Modernize and Automate Technology Service Operations with Observability. We are so excited to be here today with all of you to talk through ways to transform your software and modernize processes. So let's go. So this presentation may contain forward-looking statements that reflects the current beliefs of ServiceNow and are based on current information available. These forward-looking statements should not be relied upon in making purchase decisions. So today, we will discuss 3 steps to help you revolutionize your digital transformation journey. We will introduce solutions that will allow you to transform the way you work, operate and service customers. Regardless of where you are on your transformation journey, these solutions will be key to helping overcome complexity, drive productivity and accelerate innovation. With that, let me -- let's introduce ourselves. So today, I'd like to introduce you to our ServiceNow team. My name is Heather Archambault, I am a Senior Product Marketing Manager at ServiceNow and I've specialized with -- on Cloud Observability and our software innovation solution.

Thanks, Heather. My name is Mike Hansen, I'm a Senior Product Marketing Solution Manager with ITOM and the CMDB. And Denis, you might be on mute.

Yes. Good morning, everyone. My name is Denis Guyadeen. I'm the Senior Staff Outbound Product Manager in the Cloud Observability business unit. I focus primarily on Cloud Observability as well as its integration with the broader ServiceNow platform.

Great. Thanks, everyone, for introducing yourselves. Now let's move on. So to kick off, I wanted to start with a polling question. So what are the biggest challenges you face within your IT landscape. Please select all that apply. Is it, a; you have too many complex tools and systems? Is it slow MTTR for outages and incidents, difficulty with cloud migration and management, lack of visibility to infrastructure and services, increased constraints on budget and labor and/or buggy or slow code releases?

I was working with a client recently, Heather, that had difficulty tracking down who the application owners were. So I guess that kind of crosses several of these. They had the ownership information, too many systems. They also had those labor constraints because part of the root cause of the issue was that they couldn't figure out who owned the applications.

Yes. I mean, we hear from so many customers that have faced a lot of challenges within their landscape. All of these, of course, are very common in things we hear constantly. Fear not, everyone, we do have lots to say about all of these items, which we will cover throughout this presentation.

So we're getting near the end of the poll. Let's go ahead and close that up and we'll move to the results. So it looks like, from what I'm seeing here, that too many complex systems is a huge one, as well as lack of visibility to infrastructure and services. Beyond that, I guess it's a tie for slow MTTR, difficulty with cloud and increased constraints on budget and labor, less concerned about buggy or slow code releases, that may be because the audience is mainly focused on the service delivery. But thanks for everyone for participating. I'm going to take us to the next slide. We just finished Knowledge for this year, in Las Vegas and we're very excited to share everything that happened in Vegas, including, among those things was an announcement of a partnership between NVIDIA, their Founder and CEO, Jensen Huang, was there with our CEO, Bill McDermott. And they announced a partnership, we'll be utilizing NVIDIA's GPU supercomputing capability to scale large language models that are trained on our industry-leading IT processes and automation. We'll be working together to make those models at sizes where they don't have exposure external to the enterprise. We're so excited about that because that will allow us to bring you the same kind of generative AI that everyone's talking about and ChatGPT and Google Bard, for example. But on your platform using your own CMDB and other ServiceNow data. And that's going to supercharge our user experience. We'll prioritize your customer data, the privacy of your customer data and your security. But the first external Gen AI connectors are already available in the innovation labs. And we're excited for what that's going to do for our user experience, excited for the new solutions that's going to unlock for us. But let's start with how we can get prepared for those upcoming innovations by transforming our digital technology. We move forward to the next slide here. I have worked in the partner ecosystem for almost 12 years. And during that time, I have helped deploy AI and other cloud capabilities to help our customers achieve their digital transformation goals. I've had the privilege of helping dozens of customers in the U.S. federal government, retail, finance, manufacturing indices to leverage the power of the platform to streamline their customer workflows, to transition to the cloud and to balance their budgets. But we also hear a lot that digital transformation is difficult. We've heard from thousands of customers. And the challenges that they face are pretty consistent; limited visibility, isolated and reactive processes. While they're anxious to embrace the cloud and AI and all the new potential, there's a concern they might not be able to take full advantage of those innovations. And we predict that with the surge in digital services, incidents generated by machines and applications could increase by as much as tenfold. More data, more incidents for monitoring, logging and performance tools, mean a complexity that just humans won't be able to handle alone or at a scale. And additional stand-alone tools will only add to the complexity, they don't really solve it. So if that's where you're at. If you're grappling with the automation and machine learning, if you're grappling with the complexity of the tools, are looking for ways to automate that incident resolution, we're here to help. We really want to help you resolve those issues before they become incidents, before they affect your customers or your business. And the best part will be that you can do it all on the existing ServiceNow platform. You don't need to add additional tools. We also understand that the targets you're aiming are not static. As you transition from traditional IT infrastructure like data centers to the cloud and even cloud native, you're finding more flexible solutions but also ones that are more ephemeral and decentralized. And you'll need more than just visibility. You'll need true observability, which we'll talk about in a minute. A way to trace digital transactions all the way through the constantly fluctuating infrastructure. That has to be delivered, of course, to both your service and operations team so that they can effectively collaborate on them. Anticipating this moment, ServiceNow acquired Lightstep, a company at the forefront of using traces and logs for observability. They've now been transitioned to Cloud Observability. And I'll turn the baton back over to Heather, to share more about our solutions for cloud, hybrid and cloud-native architectures.

Thank you, Michael. So today's organizations are deploying service operation solutions to deliver extraordinary experiences and keep their digital services running 24/7. At the same time, they need to make sure they are building capabilities to manage governance, cost, risk and security. For IT operations teams, managing a complex enterprise, they have a lot of on-prem internal, employee-facing and customer-facing applications. And some of these applications are staying where they are, in their data center but many have transitioned to the cloud either to a SaaS provider or they were lifted and shifted to the cloud. For example, moving to a SaaS vendor -- provider might have meant moving from an on-prem exchange environment to Office 365, where a lift and shift might be moving enterprise applications and databases from on-prem to cloud. But there's a fourth category of application as well, which is cloud native. And while the lift and shift of workloads to the cloud is mostly about shutting down data centers, the motivation to move cloud native is much more than that. From a business standpoint, the goal is not just about moving to the cloud, it's really about optimizing for parallel innovation. And that means a move to distributed teams, Kubernetes, microservices and a new software development life cycle paradigm. And really, the question becomes, what is the interplay between cloud-native software development life cycle and service operations? How does that work? And it turns out, it's very problematic. Cloud native is independent by design, which has its both benefits and its drawbacks. Technology leaders want their DevOps and AppDev teams to be able to create and scale their services without asking anyone for permission. They want to be able to manage their own incidents and make thousands of changes per month. Many ServiceNow Cloud Observability customers are deploying 5,000 to 10,000x a month compared to previous generations of technology where they would only deploy once every 2 weeks or maybe once every month. The problem is that this independence is very risky and falls out of alignment with service operations in enterprise-wide governance. So from a service operation standpoint, if the cloud native seems running off in another direction than the technology organization, they end up with a lack of visibility into what's running, what's happening from a microservice standpoint. What makes this particularly challenging is that our cloud-native services are dependent on the legacy technology estate, so not so independent. And when the dependencies from these services are unknown, this often leads to a parallel stream of incidents. So you will have incident response happening and then the cloud-native teams have their own tools, totally separate and segregated from IT operations where major incidents are also being managed, leading to war rooms, with hundreds of people on a call, which is completely dysfunctional and problematic, resulting in wasted time, money and resources. And then, of course, policies you have that are meant to enforce governance, security, risk and enterprise-wide are not being applied to these environments because the push towards independence and autonomy has gone a bit too far. To contend with this complex dynamic and ephemeral nature of modern cloud environments, enterprise organizations need technology at a level unmet by tools designed for the cloud transition era of lift and shift. They need to get to a scale larger than ever before. By integrating Cloud Observability into your cloud-native environment, you are natively harmonizing with the ServiceNow platform, which means that disconnect we talked about on the previous slide, is replaced by automation around cloud native that's visible in the service operations landscape. And technology organizations now understand the customer, end user and [ spend ] impact of their microservices. And you can align their centrally managed governance policies all the way into their cloud-native application teams. I'll hand it over to Michael for the next section.

Thanks, Heather. It sounds like you've got some stuff going on in the background there. So the first step that we want to talk about is modernizing and integrating your service and operations today. This is about where you need to be right now in getting your services and operations working together. And I'll take us to the next slide. Starting your digital transformation journey, it really involves breaking down those traditional IT silos, regardless of your infrastructure model, consolidating your technology services and operations onto a single platform is a crucial first step. The pandemic was a catalyst, 78% of all global organizations adopted a digital-first strategy according to IDC. On a personal level, we all kind of experienced during the pandemic, the friction and issues that arose from using a 100 -- using hundreds of separate technology tool. When IT services and operations teams are siloed, issues will arise. Service teams struggle to handle the influx of incidents, requests causing employee frustration. Operations teams can't predict or prevent issues leading to productivity and revenue loss. These teams often fail to communicate effectively together, which is crucial for maintaining mission-critical applications. Bringing services and operations together conversely on a unified platform simplifies those processes. For example, your organization can use service operations and AI to enable accurate searches, deflect issues with self-service virtual agents and prevent outages with AI-powered analysis. Additionally, the combination of change management and predictive AI ops can quickly identify and correct changes causing service issues. Alerts flow into the incidents and are routed to the appropriate teams or vendors for resolution, whether that's in services or the operations side. Discovery configuration items and service maps from IT operations management contribute to a service-aware CI. We often talk with our customers about a goal of 3 zeros. It's a goal that our own ServiceNow IT department has set for itself. It's aspirational but it's still very powerful. #1, we want to drive incidents as close to zero as is possible by delivering great user -- delivering great end user experience and self-service to deflect incidents with AI-enabled chat. Second, we want to get to zero outages and that means getting to 100% availability. We can accomplish this by using AI and ML to predict and prevent issues before customers or employees are impacted. Third, we aim to have a zero physical footprint, fully running IT workloads on a cloud environment to gain flexibility and scalability that comes from the cloud. We have the same goals for you. We know that AI will form an ever larger role for companies to achieve their strategy and we want to help you get to trust it and clear visibility about your technology and tools. In preparation for this webinar today, I asked ChatGPT about -- well, I asked it for a hippie quote about generative AI and data quality and visibility. The initial response was, it fell a little flat. But when I told it to be clever and use some symbolism, it delivered what I think is an impressive and memorable quote "Using flawed and incomplete data to train me is like tuning a piano with a sledgehammer. The notes you get out won't be the hippie quotes you're expecting." So even ChatGPT understands its own dependence on quality training data. To take advantage of the amazing outcomes on the horizon, your organizations need to prioritize good data to fine-tune those models, understand how your services and technology will integrate is the first crucial step. It's the line really between chatty AI and a truly intelligent or even wise AI. Consider a user that's faced with a slow response from your ordering system, you can try this yourself, go out to ChatGPT, ask it, what could be causing a slow web system. You'll be overwhelmed with possibilities, server capacity, inefficient coding, lack of caching and more. While it will be comprehensive, it won't be particularly useful. On the other hand, advanced Gen AI trained on your company CMDB will be able to pinpoint the slow response of the [indiscernible] order system you are using, is responding slow due to increased response time from our European AWS site, likely a result of a recent storage architecture change. The issue has been identified, a resolution is underway and based on similar past issues, it should be about 5 minutes to resolve. The difference between the first and the second, of course, is context and good data, fully informed by the system architecture of the company, including how the services fit together with the infrastructure as well as the current health, status, open incidents and changes. And this now with observability includes both traces, logs and metrics associated with the stack. I know some of you are thinking, that's fine and good but we don't even have a good map of even one of our systems, much less, all of them. Most IT organizations, including all the ones that I have worked with have really struggled to get that level of visibility. There are so many tools feeding information, different spreadsheets and database and they're not sure which tools are actually up to date, which leads to a lack of trust. In addition, agents and operators typically can't quickly identify the business context, which hampers prioritization when issues arise or changes are needed. ServiceNow has introduced machine language enabled service mapping, which makes this much simpler and gives you that visibility you need on a unified platform. It will work across your hybrid estate, both for physical and virtual workloads and for newer containerized applications, which Heather will get back to in just a minute. And that's the slide. Now all of this rests on kind of a classic idle concept, which is that of the CMDB. Modern diagnostic and observability platforms excel at answering the question why or how as in how efficiently our networks process information or how responsive processes are. However, they often fall short of answering the question, what, like what systems are affected and what might go down if the performance issues continue? That kind of gap exists because the tools aren't rooted in enduring services that the company provides, as well as the applications and systems that support them. To answer the what question, you need a blueprint of your core business services and the technologies that run them, related to all of the components. And that's the CMDB. Now the CMDB was an idea that was ahead of its time. Because of that, many organizations struggled with how to get their CMDB populated with data, how to keep that data accurate, consistent and trusted. But the issues were never really with the CMDB, that was always needed. They just didn't have the tools and the automations necessary to make it work. I'm here to tell you that this is not your father's CMDB or your grandfather's CMDB. This is a new generation of tools. Our current ITOM Discovery runs on both agent based and agent less Discovery. There are Service Graph Connectors that allow you to quickly take advantage of valuable data, including the one for OTel that we'll be discussing today. Once the CMDB is automated and trusted the system can import external and manage a lot of the data transformation work itself and make that data then available to every other form, team and workflow. Everyone has access to the same information via single, common CMDB and that's going to enable some exciting business outcomes. Look at some of those. We often talk about this in 3 steps. Modernize your service operations onto a single platform that will help you expand and improve the services you offer. Companies that are ready to modernize understand that they must stand up the foundation to build high fidelity maps for their infrastructure to integrate that data into their service experience and enrich their service operations data with additional context from the enterprise that enables cost reduction, as in the case of software licenses or streamlined services. The second step is to take that high fidelity and then automate processes along with AI to improve service operations. This includes empowering self-service and predicting and preventing incidents before they affect users of your business. Our AI routines already work in predictive AI operations to pinpoint the root cause, to reduce noise by up to 98%, to correlate alerts and even to run automation playbooks to automatically resolve issues. The third step then is to take that data that you built and then modernize, to take the automation that you build in the next step and really to optimize, identify those process bottlenecks, where your process isn't working smoothly, align people to task, make sure that you've got the right labor and resources identified and then automate compliance to regulatory requirements and to assure adherence to high-performance SLAs and standards. So to recap and to kind of summarize, service operations is all about leveraging a modern IT service and operations department. And let's actually introduce one of the terms that's common in the industry. That term is AI operations or AI-powered service operations. ServiceNow has been identified as #1 in AI operations by Gartner and a leader in the Forrester AIOps process-centric way. For people-generated incidents, AI-powered service operations to flex work from ever getting to your service teams by leveraging AI and automating service fulfillment. For a couple of examples of this, you can automatically reset passwords using self-service, where you can use virtual agents to help employees request common services so that a human agent never has to be involved. And of course, you can escalate issues across the different service teams or between the service and operation teams using intelligent routing as well as the changes coming from DevOps, so they can focus on building the new technology and getting the code right, not submitting change forms. To better handle machines that involve or to better handle instances that involve machines, we can automate common operations and predict outages or service issues with the help of AI to reduce those noise, detect anomalies and identify root cause for faster automation. And then we also use this prebuilt workbook or work playbooks to automate the resolution with workflows. The bottom line for all of us who need to get service operations up to the best standards available today, is managing digital service issues between a unified service and operation team and to get those data -- to get that data processed and AI capabilities under 1 platform, capable of being ready for the technology of tomorrow, including cloud native, which Heather will now discuss for us.

Thank you, Michael. So the next step is really how do we build our IT landscape of tomorrow. So in this section, I'm going to discuss how we can expand visibility into your hybrid environments. Let's just start another poll question. So can you, please, what does your tech estate currently look like? Select all that apply. Is that you have on-premise, private cloud such as VMware and OpenStack, public cloud such as AWS, Azure and GCP, cloud native, Kubernetes and/or serverless?

If it's all right, Heather, I just want to take time to remind people that if you've got any questions during today's webinar, please submit them via the Q&A panel. You can also access and download resources in the resources panel at the bottom of your screen. We'd also appreciate it if you could submit the survey at the end of the webinar. Let's look at the results. So most of you, 84% had some on-premise infrastructure still left and I see a lot of you still have the private cloud such as VMware. But public cloud, wow, if we would have asked this question 2 years ago, I wonder what the percentage would have been, 82% now. And fully, a 1/4 of folks are using some sort of cloud native, Kubernetes or serverless, so you're starting to see that wave in turn.

Great. So as Michael discussed in the previous section, the key to managing digital service issues at scale is by automating service operations and having all your data processes and AI capabilities on 1 platform. But to gain complete visibility, unification and resiliency within your IT landscape, observability will be key. This illustration shows a good way to assess where you are in your journey to achieving unification and resiliency across your hybrid estate. Let's dig into the various levels of the IT transformation journey with observability. Level 1, we have extended visibility into cloud-native environments alongside the traditional IT infrastructure with Service Graph Connector for OpenTelemetry. This provides service operations teams more control over their technical ecosystem, eliminating visibility gaps as their applications traverse IT estates. Level 2 is implementing a modern unified observability platform, enabling teams to improve security, workflows, collaboration, customer and employee experiences and ROI. With ServiceNow Cloud Observability you can gain insights to detect and quickly respond to changes in cloud native and monolithic applications. Level 3, we have -- what we have here is a single service operations and observability platform. Gaining real-time visibility of the health of your cloud-native services and underlying cloud infrastructure avoids outages and restores services faster when there is an issue. What this does is, it dramatically improves CMDB health by incorporating cloud-native apps and context dynamically and improves reliability of cloud-native environments with better governance, visibility and monitoring. The last level, Level 4 is really the holy grail for all IT organizations. It is self-healing. In this level, companies are laser-focused on a single platform for IT, where they have operational and business data to drive outcomes. They can predict and prevent issues, leveraging machine learning and AI. They have automated playbooks to remediate issues across groups such as DevOps and Security. For this section, we're primarily focused on Level 1, expanding your visibility. Before we go any further, let me introduce a framework you may or may not know about. The OpenTelemetry project was founded in 2019 through the merging of OpenTracing and OpenCensus, tracing and metrics respectively. And is powered by organizations, individuals who support the goal of creating high-quality, ubiquitous and portable telemetry to enable effective observability. It is the second largest project in the CNCF, which is the Cloud Native Computing Foundation behind only Kubernetes, which from the poll, a lot of we are using. The goal, as I mentioned, is getting data out of the applications to power the observability back end of your choice. Cloud Observability recently announced an industry-first intent to deepen its commitment to the OpenTelemetry project, allowing organizations to maintain a consistent view of application health and performance with total telemetry pipeline ownership. That is owning the end-to-end process of extracting data from application and transmitting that to an observability back end of your choice. Cloud Observability is the first observability company to give customers total telemetry pipeline ownership. We are enabling customers to run non-vendor specific components across environments when they adopt native OpenTelemetry tooling for complete telemetry portability end-to-end. So this brings us to the Service Graph Connector or SGC for OpenTelemetry. For ServiceNow customers, the SGC for OpenTelemetry will improve visibility into their cloud-native environments. ServiceNow customers have a couple of options for getting visibility into their cloud native, specifically Kubernetes environments. Agentless discovery for Kubernetes patterns, automated Kubernetes discovery scheduling, cloud-native operations for observability and now the newest option, the SGC for OpenTelemetry. The SGC for OTel is a great option for organizations who have invested in OpenTelemetry as we are leveraging this investment in the open source framework to provide visibility into the performance of their cloud-native environments. The SGC for OTel will automatically discover cloud-native applications and Kubernetes objects based on the telemetry data sent to the observability back end, allowing customers to assess the impact of planned or unplanned changes through more accurate topology maps within their service operations workspace. Take for example, in an organization that has invested heavily in ServiceNow, OpenTelemetry and in another monitoring solution. For the operations team who are leveraging their ServiceNow workspace to keep track of performance of their infrastructure, they have -- they may have limited visibility into the cloud-native applications. They may have the SGC for a monitoring vendor installed and configured but that doesn't pull in the Kubernetes objects into the CMDB. Their visibility is limited to see how their cloud-native apps connect to their traditional estate. They're going to have to jump into it -- into the other monitoring vendors' UI to understand performance. This is a problem as they may not be an expert in that solution or may not know how to navigate to find the data they are looking for. With the SGC for OTel, we are bringing that data into the workspace they are familiar with, allowing them to diagnose and triage without having to contract switch. we are leveraging the investments this organization has already made into service ops and OpenTelemetry. Early in our discussion, Michael, walked you through the power of a modern, healthy CMDB. I wanted to quickly highlight that the SGC for OpenTelemetry is an innovative and additional method for easily augmenting your CMDB with rich context, connecting cloud native to the rest of your estate. By integrating context across your entire hybrid environment, you will improve efficiency, collaboration and reduce risk for your teams. And up-to-date CMDB supports informed decision-making, streamlining changes and upgrades, while reducing time, effort and risks associated with errors of downtime. A unified source of information that CMDB fosters cross-functional collaboration, breaking down silos across teams and departments. A comprehensive CMDB enables proactive risk management as teams can make informed decisions based on relevant captured information in the cloud-native environments. Now let me hand it over to Denis so he can walk you through the SGC for OTel in action.

Thank you, Heather. So ServiceNow has several methods for populating the CMDB but cloud native is still a challenge due to the dynamic and ephemeral nature of these types of workloads, plus the stringent security requirements as well. In this demo, I'll focus on the new Kubernetes native method via the Service Graph Connector for OpenTelemetry. As Heather mentioned, it's a purpose-built solution that provides complete visibility into cloud-native applications and Kubernetes environments. It leverages telemetry data and the ServiceNow Cloud Observability back end. So as a now CMDB platform owner, what I care about are the CIs and the data quality. For this demo, I'm going to go into this Kubernetes cluster tab, which is an out-of-box dashboard. And I'll go into the Kubernetes' Cluster 1. And when I select it, you will see all of the related items and what we're capturing here is all the Kubernetes objects as well as the workloads. And I can see everything is populated here. What's a little different here now is Kubernetes services in this view is called calculated application services and all they are is dynamic services. I'll next go into the dashboard view. And in the dashboard view, what I'm really going to look at is just the CMDB CI data quality. And because this is a Service Graph Connector, all the data that you're capturing is going to apply to the completeness, compliance, correctness methodologies. The next thing I want to look at is, is take a look at the dependency view. So I'm looking at my eBank web application. And what we see here is it's a calculated application service, again, a dynamic application service. And beneath it, we get to see all the Kubernetes objects. So what we get to see is the deployment, the nodes, the cluster and name spaces. And again, already related CI information is associated in the bottom, as well as incidents and related services, right? So as I mentioned, everything falls under the calculated application service table and I'm going to select the e-banking app, right? And here is the service map. So what's different and unique here is that the service maps are dynamic and are going to be imported from the Cloud Observability back end. So there is no heavy lift that has to be done from an operator standpoint, if you're assumed to be platform owner. And to do a compare and contrast, this is how the data or the service map looks like in the Cloud Observability back end. And all we're doing is an import job and importing the service map into ServiceNow. Thank you. I'll hand it back over to Heather.

Thank you, Denis. So you've been hearing the term observability throughout this discussion. You may be wondering what is Observability. Well, Observability helps developers understand multilayered architectures, what's slow, what's broken and what needs to be done to improve performance. More formally, observability is defined as the ability to measure the internal state of a system only by its outputs for distributed systems such as microservices, serverless, service meshes and so on. These outputs are telemetry data, logs, metrics and traces. Cloud Observability addresses the most important question. What caused that change? For example, a new server, a new service deployment, configuration push, maybe a workload change or broken cloud dependency. And what was impacted by that? Was it the customer experience, the service health and performance of those services. And observability is not just knowing a problem is happening, it's about knowing it's -- but knowing why it is happening and knowing how I can go and fix it. With this in mind, let's see how a single service operations and observability platform can help you create the next generation of enterprise layer. But first, let's do another -- our last quick poll. How many monitoring tools do you use to view and manage IT operations and services, 1, 2 to 3, 4 to 5 or 6 or more?

A little surprised we didn't have them enumerated in the dozens or hundreds, I've seen. So...

I wanted to go easy on everyone. If it's taking you a while to figure out how many, just do 6 or more.

Right, if you don't have enough hands to count. Let's go ahead and wrap up that poll. Well, let's see. Let's give it just another second. I've only got...

I actually got customers that -- I've actually got customers that has over 60 monitoring tools.

Might as well.

[indiscernible]

I guess I really went easy on the choices.

All right. Let's go to see the results of those that have submitted so far. Well, I think you did well, Heather. It looks like only 30% have 6 or more and others fall kind of into that middle range, 2 to 3 or 4 to 5. That's a lot of tools, getting the data from just a few of those solutions is no easy lift. But it does mean it's not quite as bad as we were making it out to be and that they really do need the assistance in simplifying the situation.

Yes, excellent. This just as we all have a lot of point solutions that we're using to monitor, which brings me to the next. We have noted throughout today that one of the biggest challenges you may be experiencing during your transformational journey is dealing with complexity. The complexity is caused from the increase in volume and variety of data you're getting within your tech stack, with so many monitoring point solutions, it becomes difficult to pinpoint the root cause of issues when they arise. And when they do, you spend countless hours stitching together dashboards from all monitoring tools, trying to resolve the issue. As you can see, the image on the left is illustrating the chaos from having data stored in multiple tools. What you need is to have a single system of record, it's critically important. In an ideal world, there would be data, a data leak of data from all monitoring systems. In reality, data is stored separately by each monitoring tool in its own database. ServiceNow provides a holistic approach with AIOps finding key capabilities from ITSM, ITOM, DevOps, Observability, SecOps and IntegrationHub, all delivered on the Now platform. By consolidating automating service and operations tools on ServiceNow single cloud platform with a single data model in embedded AI also keeps your digital services running around the clock. Our customers automatically consolidate critical data across their IT environment to gain enterprise-wide visibility to their services, infrastructure, applications, cloud and modern stacks in near real time, saving hours from mainly tracking down these assets, digital services and more are using legacy disparate systems of record. Let's quickly dig into how AIOps fused with Observability works.

All right. So this slide may look a little complicated but it's really simple at its core. And it's at the heart of how we do AIOps differently. At the left of the slide, you will see the telemetry data, the metrics, logs, traces, events, configuration, database you might get from the CMDB. This is all raw intelligence. We're going to move that raw intelligence through AI routines that we've spent years perfecting. This isn't a new game for us. It's not something that we just started doing yesterday. And we're going to put it into the context of, is this normal behavior or not? Our routines will reduce 98% of the noise that comes in through duplicate alerts, taking you directly to the cause of a particular problem. And then our AI, like I said, compares that behavior with what's normal. When it's not normal, we'll flag it and we'll be able to reduce a lot of the noise. Observability gives you far more raw intelligence. And that's a good thing as long as you're able to manage it. AI helps us do that and that gets us to the outcomes on the right. Automated remediation with workflows and playbooks, more collaboration between services operations, that's going to result in improved service availability and improved agility and response from your business. That means that critical systems are kept up more often and when they go down or when there is any kind of an issue at all that can be addressed quickly. You want to take us into events, Heather?

Yes. Thank you, Michael. So there's operational issues that happen inside of an organization when an application or service is down. It's the responsibility of core operations to figure it out. Where is this? What's actually going wrong in the entire stack? The cloud-native teams that are building apps on the front end, there might be back-end systems that they're interfacing with that either exist on-prem or exist in the cloud. That becomes a massive problem. We've talked through things like MTTR. Sometimes the ability to be able to do this relies on how quickly you can figure out issues when they occur. Previously, we discussed how using the SGC for OTel, extends your visibility over cloud-native apps in Kubernetes objects into the CMDB in service maps. By combining AI-powered service operations and Cloud Observability, this accelerates you to the next level, Level 3 to be exact, from my previous slide. You can quickly diagnose and fix underlying cloud infrastructure issues with integrated full stack visibility into your platform. This creates a unifying element. Now we have this unifying construct that brings us all together. Not only do we get business context from your cloud-native environment but we also marry that monitoring operational data to help you understand the health of all your services. You'll be able to reduce the risk of vulnerabilities, continue to reduce that MTTR by predicting and preventing service outages and multiply the value of your ITOM investment by drastically improving AIOps and ITSM incident problem and change workflows. Another great thing with the combination of service operations in observability, the ServiceNow operations workspace allows both agents and operators to do their daily work in a shared workspace. Daily work is configured in a way that makes it enjoyable for both groups to visualize current operations and then address the issues affecting their own areas. But the benefits of collaboration between these teams is the most compelling outcome of adopting to the service operations workspace. Together, each group can move to more quickly to address alerts, deflect incidents or resolve problems. And with all the data that you've been challenged with, with multiple screens from your cloud native and all the monitoring tools you're using, you're now able to take all that data and easily, seamlessly bring it on into their service operations workspace by unifying Cloud Observability and service operations. You get all that context and it's easily visible and it dramatically improves the CMDB health. You will be able to achieve full stack visibility of resources, applications and assets by connecting your apps to underlying cloud resources. It will increase your uptime by ensuring customer-facing applications and services are always responsive and available. And it will break down those organizational silos that you may be frequent with by using a shared workspace. Now central operations now has a single pane of glass connecting all their apps to their services. Now let me quickly hand it over to Denis. You can see how the service operations workspace works.

Thank you, Heather. Let me share my screen. All right. All right. So I'm going to take on the role or the persona of an IT operator and look at the health of my services in the service operations workspace. So as you can see, when I log into this dashboard, all of my services are grouped by criticality. I have 4 categories. For this demo, I'm going to look at the most critical services because those are the ones that are affecting my business and the ones I need to focus on. I will select the eBank iOS service, right? And I'll go to the alerts. When I go to the eBank iOS service, I can see I have several alerts that has been provided to the system. For the most part, I'm typically aggregating alerts across domains and silos. For this demo, all I have is 2 alerts, this SG Lightstep and the Azure monitor that's feeding alerts. And what this system is going to do, is generate a primary alert. So we are reducing that alert noise or alert fatigue and it's going to generate 1 incident off of that as well. So I'll select the one that's been grouped by CMDB, which I already had open here. So when it's slow, as you see, it's a group of alerts for iOS errors, this is not really an outage. This is more of -- I'm seeing -- I'm starting to see latency and latency in the world of cloud native and customer-facing applications, you can also consider at an outage, right? Because if they're taking a long time to access their service, they're typically going to -- maybe want to go to another service. So I see for this group of alert, it's aggregating 2 alerts in the service, iOS errors about 5%. I also have some services that are -- which are receiving alerts that are over 100 milliseconds latency. I see some configuration items as well but what I also see is the impacted services. There is also a weighted algorithm in this solution to stack rank the alerts based on all the impacted services, CI type and things like that. So I see I have 3 impacted services but I also see I have 1 probable cause. Somebody did an upgrade to the [indiscernible] service. I also see that there was an incident also generated here as well. What this incident is going to do, it's going to be automatically generated from the event management solution that's part of ITOM health or AIOps. You can see the callers right here. What it's going to do, it's going to route this incident to the team responsible for servicing this iOS error for this e-banking service. We're going to see the affected CIs as well as the impacted services and it's going to provide some more details as well. So it's going to scrape the payload and bring it into this incident to ship it to the team, so they don't need to swivel chair to another monitoring tool. When I go back to the alert, what I also get to see is, there are some recommendations. As Michael mentioned, we're leveraging AI here as well. So it's using machine learning to look at similar alerts that happened in the past, incidents on the CI that also -- that occurred, as well as related CIs that we have incidents to as well. What else we'll see is, I have some knowledge articles right on how to actually troubleshoot this, how to find the root cause of latency. And what I also have here is, I have a notebook in the Cloud Observability platform. If I could select this, really, what this is going to be is really a set of dashboards that I create to collaborate amongst my SRE and DevOps teams. We're not going to focus here. But again, this helps them investigate this and get the root cause very quickly. And the last piece is, when I go back here, I can launch into the Cloud Observability solution. So this is going to take me to the dashboard, so I can go out and start to do my troubleshooting as well. All right. I'll end there and I'll hand it back to Heather and Michael.

Thank you, Denis. That was great.

Just want to remind folks that if we didn't get to a question that you asked, we are going to [indiscernible] some [ squeeze ] in the ITOM and service management space. And so if you didn't get a satisfactory answer to your question or you didn't get one, we'll get those questions from you and reply to you at the e-mail that you left when you registered for the account or for the webinar today, sorry.

Thank you, Michael. And to wrap up, so ServiceNow is perfectly positioned to help you drive organizational-wide technology transformation as organizations embrace cloud native to grow and scale their businesses. Ultimately, breaking down those organizational silos that delay transformation will empower service operations and developer teams. By bridging cloud native and traditional apps, you can harmonize modern SRE and DevOps and ITSM practices with AI-powered service operations and Cloud Observability, improve reliability of not just your cloud-native environment but your entire hybrid estate with better governance, complete visibility and monitoring. And proactively improve employee and customer experience, increasing productivity and massively reducing MTTR. When it comes to keeping your digital services running 24/7 in today's hybrid world, only ServiceNow ensures, you have what you need to make sure you can deliver extraordinary employee and customer experiences and centrally manage, enforce and automate consistent guardrails regardless of what type of app it is, who built it or who runs it. With that, I will turn it over to do a couple of questions within the Q&A.

One of the questions that we've got here is does the Service Graph Connector for OTel support any Kubernetes distribution? I think that might be a good question for Denis to answer.

Yes, that's a great question. So we support any Kubernetes distribution because the OTel agent runs inside a Kubernetes cluster. So what that means is, you don't need a mid server or you don't need an ACC agent to execute patterns. We're actually going to do everything within the cluster itself and ship the telemetry data over to the Cloud Observability back end. And then all the transformation, ETL jobs is going to happen between the -- those 2 solutions.

Thanks, Denis. That there's another question here, why is OTel data being integrated through a service connector or a Service Graph Connector instead of its own integration? There are several things about a Service Graph Connector that make it a great choice. First of all, everything is processed through the IRE engine, that's going to significantly cut back on the -- any possibility of duplicates that may arise from the ephemeral nature of OTel data. It's also going to be certified, meaning that people are going to be looking at it from ServiceNow as part of ongoing support. So if there are issues that arise due to changes in the architecture of AWS, Azure or GCP, for example, those will be addressed. And then finally, I encourage you to take a look at other Service Graph Connectors on the ServiceNow store. By making it a Service Graph Connector, we'll be able to ensure interoperability with other data sources, those might include AWS, Azure or GCP Service Graph Connectors.

Great. Any other questions? I know we're almost out of time.

As I said, any questions that we weren't able answer in this session, we will get back to you and reply separately.

All right. All right. Well, just to wrap up here. We have a knowledge -- digital experience with over a 150 recorded on-demand sessions, a lot of these topics we discussed today, we have lots of sessions for -- from between Sony, AT&T, and countless other customers are representing service operations, observability and AIOps. We also have some on-demand webinars. We specifically have an SGC for OTel deeper dive, if you would like to learn more about that Service Graph Connector and how it can help you. And lastly, you can register now for the Utah release broadcast. Please scan that QR code at the bottom and you can grab your spot. And with that, I just want to thank all of you for attending today's webinar. We're so excited to present this topic to you. Please be on the lookout for resources that will speak to a lot of the topics we discussed. Also there will be future webinars covering the additional layers that we did not get to specifically around AIOps and observability for self-healing. Thank you.

Thanks, Denis. Thanks, Heather.

Thanks, everyone.