ServiceNow President & CPO on Why AI Kills the Companies That Don't Transform | Amit Zavery | E305

Any company who wakes up and says I am the end to end orchestrator I think they're dead. There's no clear ROI in many cases. So you land up failing more often because you're doing the spare part approach versus doing a very thoughtful platform first mindset. Our AI business is going to be 1.5 billion this year. That plan was 1 billion. All of our products have become AI native. So if you don't transform, you will get killed. A lot of leaders will say, "Oh yeah, we have to put all this to together. it'll take us 3 months or 6 months or 9 months until then nobody does any AI or nobody does cloud or nobody does this and that is where the companies are going to die but going and saying please onboard an employee and give them access to 15 different application based on their role and then ship them a new laptop based on where they live plus uh make sure the benefits are up to date that is taking action now the systems behind the scenes have to do that heavy lift that's what we solving for >> hey this is Carlos CEO at Paris and your host on the product podcast. Today's guest is Amit Zavari, president and chief product officer at Service Now. Service Now is the platform enterprises run their work on with more than 75 billion workflows flowing across it every year and around 14 billion in annual revenue growing over 20%. Even with a market cap above 100 billion, the stock is down more than 30% over the past year. While its AI business is on track for $1.5 billion dollars this year, ahead of a $1 billion plan, a mitr run product and platform at Oracle for over two decades and was a VP and general manager at Google Cloud before joining Service Now. In our conversation, we cover why the market can't yet tell the AI winners from the losers and why he believes companies that don't transform will get killed. Why the idea of one company [music] becoming the single endtoend orchestrator for the enterprise is a fallacy. the spare part approach that makes most enterprise AI projects fail and what the pay setters do instead. Why access is moving away from the user interface to our agents and what taking action not just retrieving information actually requires. How to hold long-term conviction on platform bets while the market judges you on short-term sentiment you can't control. Let's get into it. >> Welcome to the product podcast, Amit. >> Thank you. Thanks for having me, Carlos. Very excited for the conversation. The first question I have for you is about your title because it's quite unusual. You are president and chief product officer and chief operating officer at Service Now. So what does that really mean in practice? >> Yeah, it's pretty straightforward if you think about it, right? So the chief product officer basically is responsible for defining the product strategy, building the products, so product engineering as well as uh making sure that we delivering the best core capability to our customers. And the co role is how do you operationalize all these things. So how do you really deliver value to a customers? How do you make sure companies operating at a very good scale and an efficiently? Uh as well as delivering this capability in a much faster pace. So combining uh strategy product delivery to execution gives us the ability to now look at it end to end all the way from engineering to customers at one place. So it gets less fragmented, less uh handoffs and you have one person kind of overlooking it so that we can make decisions faster. So that's the goal with this uh role is to really move fast and do things perfectly for customers. >> And when I hear you explain it, it makes total sense. And I've seen a lot of organizations unify product and technology, the chief product and technology officer, but I had never seen uh product and ops under the same roof. >> Yeah. No, I think it's a unique thing here and I appreciate how Bill who is a CEO thinks about it and it gives us the ability to and we've seen a lot of the innovation we've done over the last 18 uh 24 months has been that way because we've been able to now bring efficiency but also alignment across the organiz organization right so have product engineering experience and then you have operations allows us to really uh do a great job I think >> you give me a a sense for the scale of your your team and your business today. >> So, Service Now, uh we are on 15 billion in revenue. Uh growing at 20 plus%. Uh our free cash flow margin is around 35 37%. Uh OP margins around 32%. So, it's a pretty scaled business growing very fast. One of the fastest growing uh software companies in the world today. Uh and we were the fastest to 1 billion, 5 billion, 10 billion and now 15 billion. So, we're very proud of that fact. uh in terms of the size of the company uh we are around 29 8 29,000 employees uh and if in the product and engineering the R&D team is around 15,000 people so most of our investment in the companies to make sure we build the best products for our customers so half more than half of our investment goes there and the rest of the things of course go to market is important for us to really work with the customers to make them successful and adopt our products and then we all the supporting functions around it uh but our goal always has been it's a very productled very engineering driven culture inside company but customer first mindset >> well I want to address one of the big elephants in the room right as you mentioned you're beating expectations growing over 20% year-over-year market cap is over hundred billion dollars yet the company is down uh over 30% year to date from a market cap perspective right so I'm curious to know why do you think the market is still not rewarding that performance and that investments that you are making on on on the AI front. >> Yeah, I think see there is uh with any trans transformation or technology shift, people start to wonder what it means for any company which has been around in that space for some time. So this shift with AI, I think the investor community is still trying to understand what is mean is it a tailwind or a headwind for companies like ours and if they don't understand the change yet, they're putting every software company in the same bucket. But I think we believe we are a very different kind of a platform company than other software provider which are which are much more vertical stacks. So we do have a lot of tailwind our AI business is going to be 1.5 billion this year and growing at a phenomenal rate. Right? Our plan was 1 billion. We already increased our plan to one and a half billion. All of our products have become AI native. So I think it takes time for the market uh the investor community to understand those nuances differences whether we are early company or are we remaining legacy so if you don't transform you will get killed but if you transform and build the right things and do the right things we have a lot of domain a lot of customer base if we give them a lot of value from AI they don't go anywhere else and that's what we starting to see with our growth if you look at our growth has been very very good and accelerating and we seeing a lot of new businesses emerge inside service now as well. So I think it's just timing I would say for investors to understand the differences between different providers. Uh and uh I think it's starting for them to see there's also rotations happening with what's happening with infrastructure what's happening with uh uh the the technologies out there uh with semiconductors and memory. So there's a lot of confusion in the market today with uh AI. So, I think it's a matter of time from my perspective, but we feel good about our long-term strategy. We feel good about our traction and I don't really worry too much about a short-term changes in our stock prices. Of course, we want to continue to be appreciated for our growth and everything, but that's uh for investors to decide. All we can do is control what we can and we're doing the best we can there. >> Exactly. And and I'm curious about that. So from your perspective like how does the market cap affect the way you are planning for the future on a on a quarterly or maybe on an annual basis? >> Yeah, I think the market cap does not directly impact I mean of course we want to listen to our investors. They are a very big stakeholder uh for us when we make decisions. So we have a fiduciary duty to deliver returns for our investors. So we do always keep on looking at it how can where can we be more efficient where can how can we get more growth how do we convey and communicate our messages about what we doing and how we differentiate it how do we show uh our traction in the market some of the investments I'm doing around new opportunities which are going to be big roadmap opportunities for us accelerate our time right in the security space data space what we're doing in the CRM space are all driven to create more value to our customers but also at the end for our investors. So uh we always think about that on a regular basis to make sure that our investors remain happy they get great returns but we also do the right thing for long term as well. The short term you can have this ups and downs but long-term the trajectory has to be great and a trajectory has always been good both from a financial metric uh innovation metric as well as uh return to investor from the financial metric as well right for them >> and so as I try to unpack that for for product leaders specifically there's always this tension between optimizing for maybe short-term revenue long-term growth right so as as you try to resolve some of those frict friction points when you when you plan your strategy when you expand into different products when you try to um maybe increase the performance of your core products like I'm curious to know like if there's any specific framework or way for you to to think about it >> yeah I think see as a product leader you always have to first have a perspective of where the long-term opportunity is build things which are going to be sustainable so my goal with any com anything we decide is a sustainable growth don't do short-term things and then you have to rep redo everything every time and our customers will be unhappy. Uh engineers and product leaders will be unhappy and eventually you're not going to get the results. So I always want to balance no doubt investor sentiment and uh feedback from the from the shareholders and others. But we we also have to have conviction about what we do. You have to feel confident about where the market is going and whether what your differentiation is going to be and how you're going to build a lot of value uh in a products for customers to be really excited about buying using and continuing to expand the usage. Uh so shortterm is good to kind of pay attention to but you cannot lose sight of long term. You have to product development. Even though people say that you know it's easy and fast to build products now because of software has become easier to build. It's not about the speed of engineering. It's always this the real quality comes from understanding what to build and delivering that in a much more valuable and usable way. And that's really what I focus on from my team perspective. uh and uh ensure that we're getting constant feedback from our customers, our users, our partners, our ecosystem and then have a gut feel. You have to know what's happening and where you will make the biggest uh investments and how are you going to monetize those things long term. >> Yeah. I mean, you've been around the block. I see you spent ton of time at Oracle, then as a VP at and GM at at Google, right? So, you've seen kind of some patterns around this type of internal transformation. So I'm curious to know what are some of those elements that you think stay true now in this new AI transformation and and what do you think it's fundamentally different from the way you've been transforming companies before? Yeah, I think see that the basics are still the same, right? The best building the best products and customers loving your products, you're going to win. You have to get that right. You cannot build uh a sustainable business on substandard products or features which are not going to get a value. So those are basic capabilities and basic things you you just feel transformation going on. Any new technology comes in still have to do the basics right. So you have to have a very good team uh people who understand how to build very scalable, secured, performant but very nicely usable products. You have to really ensure that you have customer relationships so that you understand what they're doing, how they're doing things. You have to get input from various different things, different environments so that you can make the right decision. So those basics remain the same. I think every transformation has introduced new new challenges, right? When we went from say uh client server to uh more of a distributed architecture, web- based architecture. The technology transformation was huge. You had to rewrite software. You rebuild a lot of things. How people use your uh products changed quite a lot. Uh browser based interfaces were very different than what you had with the thick clients. uh and the data being residing in a distributed environment. It was very different. So it was a big technology shift. Commercially it didn't change much when we moved from say uh web based to cloud based client brow based architectures where things are hosted in a cloud uh they're running uh and you operating those products yourself as as engineer. So your technology changed, the role changed, but also commercial model started changing. People were doing more subscription based pricing versus a perpetual license pricing. So suddenly you're doing technology transformation and a commercial transformation that you had to kind of figure out how to make that change for your customers, how do you change your sales teams, how you change the incentives and then how do you really monitor and meter all those things because you're also charging for uh you number of users and things like that. When we now moving to the world of AI, it's going to a much more consumption-driven architecture. Architecture is very different with AI of course, right? You're using a different large language models. You're using uh technologies for reasoning. You're bringing that into deterministic workflow. So the tech stack is changing quite a lot. But commercially also people are now saying, you know what, I want to do usage based pricing. I want to do consumption. When I use it, I want to pay you. So commercially instead of subscription now you're going to a more consumption-driven uh pricing. So again so so there are a lot of every technology shift brings different different nuances that requires the kind of people the way you work where you operate where you sell where you build all changes around you. So basics might be the same in terms of you want to build the best products, you want to get customer satisfaction and you want to do long-term thinking. But how you do it is changing every time and the speed is very different now. Right? What is happening with AI? The compression in time is very high. You're doing changes on a regular basis versus maybe six month plans. you're delivering things much more in uh much more regularly and you have a less time to really uh do uh long-term features. You're building things much faster now. So I think it's that's I think the change and I think you have to be cognizant of it and deliver to those kind of thinking now. >> So you recently released a report on AI maturity in the enterprise and I want to dig into that because I think it's a lot of companies that a lot of leaders will resonate with this. Like one of the highlights you mentioned is the AI spend is is going up as as you mentioned teams buying tools or now using more more tokens but there is a constant theme there which is uh a lot of those teams struggling to show real ROI and then you mentioned that there is like a subset of companies you call it the pace setters >> yes >> small percentage of companies that are somehow reorganizing for AI and not just around it so we love to learn more about what you mean by that >> yeah I think that what we've seen and when we talked to a lot of our customers and this report was based on a lot of interviews and analysis uh you find variations in terms of how customers approaching the AI journey a lot of them are like you know what everybody should go and do something with AI without providing some kind of as a prescription or some kind of guidance so the projects are all over the map for companies right some of them are getting some success some some projects are very failing there's no clear ROI why in many cases but also there's no coherent way of thinking and providing expertise across the company organization and then there becomes a very scattered scattered approach but also very much like uh I would say spare part approach you bringing technologies pieces of technologies and trying to cobble it together and make it work and then given the underlying technologies changing so fast it's very hard to keep up for most of the organizations so you land up failing more often because you're doing the spare part approach versus doing a very thoughtful platform first kind of mess mindset. So the pace setters people who have been very successful companies which we've seen is that they're taking a little more holistic approach. They're providing uh a little more structure to the company. They're providing help and expertise as needed and they're picking the projects which are going to be much more value add as well as has the right kind of investment and right kind of people because AI is not just a technology shift it's also cultural shift inside the companies and you have to approach this thing together as an organization and have everything behind it. You can't just say go and do partial things. So not do spare parts, don't do pieces of technology, buy a platform, work with people who understand this, identify the right projects and make sure you put full focus on it to regularly introspect what is working, what is not working and fixing those things on a regular basis. So having that mindset are the ones who seeing the pace setters. they have really differentiated themsel by putting that energy behind this AI transformation enterprisewide uh and then putting all the energy with the right kind of to partners to make it all successful and fixing things which are not working very quickly so people don't just linger around and those are the pace setters we have seen really work out very well >> you identified four main gaps for companies that are trying to go from curious to a pace setter Right. And number one, you mentioned it's the data gap. Apparently like 71% of companies struggle with the data accuracy, access and and management. So how can companies unlock the power of their data? >> Yes. I think data has been always the fragmented hard to manage and I think data will always remain fragmented. I think this idea that everything will come together in one place. It's uh not going to happen in any enterprise. It's very difficult and because systems are built, the things are running everywhere, data is distributed. The best way to fix the data gap is to really have a very good thoughtful understanding first what your landscape is, where is what, who owns it, what the systems are. So getting that kind of understanding of that is a starting point. And second thing which is most valuable is really providing a very good integration layer. A data data stack which gives you the ability to do uh without moving everything around a federated sense to make decisions. Uh so either through zero copy adapters or through different data integrators integration technologies providing that layer and creating a semantic layer on top of it. Once you have a semantic layer with data distributed across multiple different domains and uh silos, you can have now a virtual view of everything without having to rer rationalize everything, move everything around which is very painful, time consuming, it never works because things get fragmented very fast again. So this semantic layer with a knowledge graph and then a context graph gives you a lot more insight and ability to do insight to action with while the data being distributed. So data gap has to be fixed that way instead of re-engineering everything every time. >> Well, on that on that note, we've had uh other product executives on the podcast from companies such as Snowflake or Fiverr, right? And everybody talks about the system of record. Some people try to be the system of record, some try some people try not to be the system of record, but what's clear is that data is living across different systems >> 100%. >> And the the the effort that it takes to clean it up in a certain way so it's accessible for non-data teams seems to be high. So I'm curious to know from your perspective what are some ways for teams to get the data to a point that is good enough so they can continue on their transformation. Yeah, as I said this idea of having uh a text technology which allows you to connect the systems and the data and coming from system of record coming from data warehouses uh coming from file systems all need to be available for you to make decisions and when we what we have built now we have something we call workflow data fabric. The workflow data fabric is basically a connectivity layer with some uh semantic capability to understand where your data is, how you integrate it together, but also create a semantic layer for doing insight to action has been the stack we provide to our customers. And that has worked very well for our customers because they are able to now uh move very fast to get insights from all these different systems out there and without having to do a lot of heavy lifting. If you ask now every data data team to get go rewrite everything move everything around it fails and that's why we created this workflow data fabric as a technology offering with the connectivity layer the semantic layer and on top of that we building the context graph and that has unlocked a lot of value because you are now understanding a lot of the insights from all these systems then >> how do you think about the teams accessing that data I mean we're seeing a lot of companies going headless right and exposing their data via other interfaces they do not own such as cloud, copilot, MCP, CLI. So curious to know your perspective because ultimately I think in order for this to work non-data teams should be able to use data to to make decisions. >> Yeah, I I completely agree. So see the thing is uh access has to be now provided in multiple ways. The idea that everybody will go through user interface is fallacy. It is not going to work in the future. It's not already starting to break apart. So you have to provide assuming the humans are going to use your systems, agents are going to use your systems, co-pilots of the world are going to use your systems and you need to make sure you provide access in multiple ways access through a user experience layer directly but also through MCP server for that access A2A for agent to agent integration cloud code or co-work or cloud co-work or uh Gemini enterprise or co-pilot mic Microsoft copilot all of these things will have to have ability to take information you require to do your day job. If if a if a human is interacting with it to do the job or agents are interacting with it to do a workflow or anything like that that has to be available. So what the way we architecting our platform today and we have this layer called action fabric is to abstract out interfaces to make it easy for every way for anyone to interface into our products into our technology into our data. Of course beyond just accessing the biggest change which we bringing in also be able to instruct the the systems to do action on your behalf not just give you information. That's where the unlock happens. Getting your information say hey tell me what my latest forecast is. Very simple. But going and saying please onboard an employee and give them access to 15 different application based on the role and then ship them a new laptop based on where they live plus uh make sure the benefits are up to date because they're new employee. That is taking action. That is a complicated part. So even through MCP server or through any agent they can give you the instruction. Now the systems behind the scenes have to do that heavy lift and that's what we solving for other than just giving you access can I also now take action that leads me to the the second bottleneck you identified which is the scalability gap right agents need a platform to perform and one of the the things that I've seen with with product teams specifically is that there's always a group of people who are more techsavvy than others and they're able to set up their own agents they they they find ways to increase the productivity ity, but this multiplayer system that enables anyone basically to use the agents to do something with the information without having to be the ones setting up the system. It seems very powerful. So, I'm curious to know how how you're seeing companies be able to get to that stage multiplayer. >> Yeah. See, as I said, if you had to build all this from scratch as individual companies, it will take a lot of effort and lot of expertise. And uh just because you can wipe code an app doesn't solve the problem of scalability doesn't solve the problem of uh really having agents spawning new agents doing multiple things right because you're giving them access they can go and run and operate for 24x7 and keep on interacting and interfacing into those systems today. So the scalability gap which we see is that people are not rethinking how the usage will go away from human interaction to AI agent interaction and if that is happening the volume is changing and the scalability of the platform is going to be very very critical. So that's why what we're doing from the architecture perspective is making sure we can handle the volume but also the security associated with that right. So bringing the mindset of like what is who's allowed to do what they're doing how much volume you allowed to do what are the SLAs's and can you deliver against that the customers have to solve that by looking at platform and their ability to scale to the volume you expect and then you have to have a distributed architecture running in many environments and many zones so that failovers are easier any kind of performance issue a bottleneck is taken care of and architecturally it keeps on evolving to be much faster and better performant uh uh port uh platform as well. >> Yeah, we're seeing early stages of this. I mean, Slack released their own bot cloud integration with the Slack and it seems like once the teams are able to work together across the same the same system then there's also this confusion around okay who can contribute to the back back to the system in terms of building new skills or maybe automating certain workflows and then who is governing all of this. I think that's what you call out in the next in the last two gaps right workflow gap and governance gap like how do you close that loop >> yeah see the thing is governance is the biggest uh I would say barrier to AI adoption governance with security if you want to think about that together but governance itself because every large enterprise have to have a view about what is happening where is it happening can I do I have visibility Do I have control and what risks am I taking and how do I manage the risk? So the way we close the gap, we've been building governed and secured as well as risk management capability in our product and platform for a long time. Uh we have a lot of key data and metrics been delivered to our users in terms of compliance in terms of uh visibility in terms of what is going on what applications were used when they were used how many licenses you have what are what is the pattern look like who has who has uh changes what changes you want to make the life cycle around it for human non-human physical AI all of that stuff right so that is just a core feature in the platform we deliver. And as as as people are starting to build a lot of these applications, they should think about where do you run those things? What is the runtime look like? Is the runtime has the right harness? Does it have the right kind of way of structuring information and does it give you the right kind of reporting and mechanism to ensure that you're safe? You're doing this without risk. And if it's risk, what are the risk? because your audit committee or your leadership team everybody's going to ask you for that. How do you bring that kind of visibility into the picture? And that's how customers need to solve it by having a platform which has that capability delivered as a core feature not something on the side. You know sometimes I've seen especially large enterprises using the word governance as an excuse right because of course there's so many things you have to regulate that by the time you are done with that you'll never get started with the actual implementation right so cious to know from your perspective um how can even large enterprises get started you know like make sure that people can still feel comfortable using the AI in certain ways while of course there there is a certain tower control if you will that is also going to create the right the right guard race without slowing down innovation. >> Yeah. No, I think uh see governance I'm not never a big fan of putting policy for the sake of policy and you don't want to have policies to restrict innovation and slow down adoption of new technologies but you also want to have some safeguarding because some of these new technologies like AI and AI agents can really disrupt your business in a bad way by removing things or deleting things or doing things which not allow exposing data. Uh so every company needs to have some kind of policies and they need to have some kind of uh governance structure to have that mindset and visibility and I would not advise any customers to say put huge amount of barriers before you do anything because that will be silly but put some kind of structure that there are people and they are reports and they are tracking of things which are happening so when things go wrong you can stop it fast without visibility. you would be too late to react. It' be too late to fix and your reputation as well as uh revenue and profit all of thing can be compromised. So you do want to have uh and nothing wrong with some governance but don't use it as an excuse to do less because I think we you're 100% right. I've seen companies a lot of lot of leaders will say oh yeah we have to put all this to together it'll take us three months or six months or nine months until then nobody does any AI or nobody does cloud or nobody does this and that is where the companies are going to die but if you don't if if you do governance with right kind of tooling while you accelerate your innovation it's perfect right that's why we build those tools to remove that barrier say hey you have visibility let's go don't waste time uh and we are not going to slow you down. >> I've seen two approaches to governance in in in enterprises. I've seen the the centralized approach where there's like a colleague chief AI officer that is trying to kind of create the overarching policies and designing on the systems for all the functions. But I've also seen a more federated approach where each function has their own system and so that allows them obviously to go faster but like the downside is maybe less control over the entire AI implementation. So I'm curious to know what you are seeing on your end. >> Yeah, I've seen mix to be honest. I think people are learning. So there's not like one size fits all. Every company has a different culture. Some companies do very well with central team because then they have one place to go and some companies hate that centralization. That means I have somebody else telling me what to do. I don't like it. So it depends on the company culture. So I don't think so there's anything wrong with either model as long as you're clear what your model is. You cannot have everything in different different ways. You cannot have a chief AI officer with a central team, everybody federated, nobody listening to each other and it'll be a chaos. So I think it's okay to have structure and I think it's neither of the structures are wrong if you have the right people with the right mindset and willingness to change. If you find something not working, you have to be able to change it. I what we have done at service now every comp every group has to be able to do AI. So we don't have this idea that it has to be all centrally monitored and managed but we provide we are created in every department a COE which is loosely kind of defining what a department like finance department or uh HR group should be doing. They have the COE's which are AI COE's very experts very senior leader running that and they work with other CO in a federated model while if there's some issues because we are a product and engineering company if there anything we need to do centrally we can bring that expertise as needed to allow our IT team is kind of acting like a central clearing house hey this project let's do it let's kind of implement it let's get it going but the ideas and ability to do it all happens in every department so there's a lot good ways to move fast but there's also ways to kind of get help if you need to and not go down the wrong path by not understanding it right so I think people have to just put some structure I think it's a good thing because this things are moving fast is changing fast there's a lot of risk associated with that so putting a right kind of structure is important I think but don't over go overboard trying to find the best structure >> ultimately all of this comes down to ROI >> right and then so You mentioned even in your report that these pace setters are able to get ROI over 100%. I think you said 160% something like that. So I'm curious to know okay regardless of the approach you take to increase AI adoption and ultimately ROI what are some of the good metrics that help you prove your point and and show the team that we are on the right path. >> Yeah. See I think again this similar to the structure ROIs are in the eyes of the company in terms of what they value. So if if if it's all about uh bottom line or improving efficiency or speed uh you will use those metrics. We see a lot of metrics we provide to our customers both in terms of uh time to value time to uh resolve an issue uh time to fix something. Uh metrics like the number of hours saved in automation uh amount of processes cleaned up. So that you used to take do thousands of uh different variations how are you simplifying? So we provide a lot of those metrics uh for our customers and they pick which helps them see the ROI on it. Of course the amount of money they're spending and the number of tokens maybe using all those kind all good metrics good input to the outcome nobody should just worry about one minor item. you should be looking at holistically and what we talking about metrics are the more corporate level metrics and that's the one I would suggest for anybody to calculate ROI is that are they getting savings are they getting automation are they get becoming more efficient are they resolving things faster and that and eventually all shows up in the top line and bottom line if you have revenues growing and are you are you becoming more efficient and that are the ones which I think people I talk to worry about more if they don't see that then they don't believe they say well you're doing this thing for the sake of doing it and I'm not getting any uh topline and bottom line improvements >> want to talk about go to market because I think there's a lot of companies who are already on the right path right they bought the tools they're encouraging the teams to use AI they're seeing some productivity gains but still they might need some external help and I and I notic in in your model you have forward deployed engineers but you also have it seems like a marketplace called autonomous workforce so I'm curious to know about those different approaches you are taking to help more organizations adopt AI. >> Yeah, I think see when we started doing a gentic last year, one of the biggest barriers for gentic well two barriers. One was worry about governance and security. It was the number one issue for everybody. They didn't know what will happen, what datas will be accessed, what system will be touched, can can it be prevented from doing something wrong, all that kind of stuff, right? So visibility control became number one and that's why we launched a product called AI control tower to give you full visibility about all your AI systems the cost the life cycle the versioning access security all that stuff in one place the observability and everything else. The second uh thing for uh a lot of a lot of the customers we're talking to is that where do I start and and that's where the go to market team started getting more engaged to show them all the different things you can do with our agentic processes take your incident management process or triage process or uh case resolution process uh security uh vulnerability process all these processes and business business flows and workflows uh you needed to figure out where to start and where you going to get the best ROI. This is where we put in investment in our solution consultant as well as FDE to show customers real value of our products by implementing one of those workflows and identifying what they might have used before into an AI based uh outcome and out of the box easy to do and you don't have to rewrite everything every day. So that's really where we saw a lot of good traction because we took the issue out of security and compliance out of the table by giving AI control tower and then we're making a lot of automation happened through a playbooks with agentic full-blown AI capabilities available out of the box the 100 plus like that. So those two things got us huge adoption because customers started liking it they know how to do it. We were able to help them with the first few use cases and they will more and more after that and then we realized see building AI agents is not the end goal of every company why do they care they care about a solution they care about doing the job better and faster if I'm AI agents is doing it or human agents are doing it for the end user doesn't matter really typically so that's why we introduced this idea of autonomous workers where we can take a fullblown t uh uh uh work be L1 support engineer, secop analyst, HR business partner their full job and provide that as an autonomous worker doing it end to end and taking actions for you so that it offloads from human reduces the time to process. So we can go from like in general it should be 2 days to 20 minutes as well as you can now close a lot of the issues autonomously without human interaction. So it saves you money and time. But this is running 24 by7 with multi- language. You don't have to build huge systems out there, people doing all this work. Now I can operate that with autonomous. So that's the that's the marketplace we're creating with this autonomous worker. We have 20 of these who are now taking care of all these end to-end tasks in using AI underneath, but customers don't have to worry about it. So we're moving away from the idea you don't have to do every AI agent every day. We are giving you the full work capability. So, so to be clear, these are humans. Obviously, they might be using agents, but that the companies are are hiding these humans and this is a marketplace. This is different than your own in-house forward deployed engineers, right? >> Yeah. Well, so the two parts that deployed engineers are helping customers with generic AI related stuff like they go help them from week or two weeks and take a product. It could be autonomous workers. It could be AI control tower. It could be the gentic workflows depending on the customer need. We get them started. Autonomous AI specialist are full digital equivalent of human workers. So L1 support engineer can a human agent can be replaced with this autonomous AL1 support engineer AI specialist and do the same task with the same skills but do it faster do it in a in in a much more efficient way and that FDs can also help you with that take this autonomous worker and make it work in your environment. So the fds are really making AI work for customers and the technologies like AI control tower, autonomous worker, agentic platform are the tools or the full skills and the solutions for customers to really be successful. >> It makes sense because in this agentic world a lot of SAS companies are repositioning themselves as the agentic layer. Everybody wants to be the layer on top of all your tools, workflows, data, LLM, you name it, right? orchestrate across the company and that's a bold bet because uh if you you win you win big but everybody wants to do it right so I'm curious to know from your perspective what makes you believe what's your angle to eventually become that that that agentic layer on top of everything else >> so service now has always been the orchestration orchestrator for business processes end to end right so inside an enterprise when we connect value system together service now is used as an agent as uh I would say orchestration layer before even agents came along right so we've been in that business for a long long time and we have experience connecting and automating business processes for years 20 plus years we definitely have ability to do orchestration but I don't think and I think the industry thinks they're going to be one orchestrator which is a fallacy they're going to be multiple businesses business processes orchestrated by different different providers uh and uh in some cases different orchestrators have to work with each some cases they might be siloed because they're only doing things in a particular department or particular application. Uh so my guess and my expectation is over time is that every company will do some orchestration. We might do a lot more than everybody but we might not do all of it. It has to like in some places if I'm using a vertical stack say I'm doing an industry application in say health sciences and that application will do the orchestration for those agents in that environment and then we would come along and we'll do orchestration of our agents or third party agents when we building a business process which touches that application and connect them together right uh because that application is very verticalized in their own world they don't have any visibility across outside we would connect and orchestrate that agent system with a third party other agent where we might need a data flow from one place to another. So if you have source to pay or order to cash all of these things are multiple systems out there and we were doing orchestration for a lot of those core running processes and we will do the same thing in the AI world while some of the other parts of those processes will be run by somebody else. So I don't think I'm expecting or I'm saying to any customer that we are going to be the Uber of Uber orchestrator of everything and there's nobody else who can ever be as well because I think people are thinking that they will own the whole enterprise that is never going to happen. Enterprise is very fragmented. They have a lot of legacy, a lot of new systems, a lot of bpoke applications, lot of vertical stack, a lot of uh new AI systems out there. you will be part of it and you have to always think about that how do I make it work across instead of trying to say I am in this small world and I everybody has to be with me that will be a failure mode for every company if every any company who wakes up and says I am the end to end orchestrator I think they're dead >> I I've seen that movie before and I agree when everybody was trying to replace Excel and they would put themselves in the center of the picture and say and we integrate with everybody else and then the reality is that Excel is still alive there's a bunch SAS tools that are still necessary that integrate with Excel and there are multiple winners. >> So I think that's what I'm saying that we cannot you have to realize and if you are in enterprise space enterprise software you have to know how enterprises work. These businesses are never going to completely do everything end to end with one thing because it's not possible. You have to have multiple things working together and that's why we always have believed in building an open ecosystem. We never been in a provider says everything is service now. Our job is to make it work across. If you come along and say I will I will be the top of the spear and I will be the only one. Everybody else work with me. Your business will be dead. Yet I I notice you are also expanding your own applications, right? So you recently acquired a cyber security business for over 7 billion dollars. I I notice you also launched a new uh autonomous CRM. So I'm going to try to touch on on those starting with cyber right like what is your thesis or rational behind this investment >> see a lot of people don't know our security business is billion dollar plus before I even did the acquisition right so our security be business for doing any kind of CISO related postbach activity now CISO is our second largest buyer for service now after CIOS and the reason they are is because they use us with any security tool out there, crowd strike, uh Google, whiz, whatever it is back end in terms of integrating with a workflow for doing any kind of triaging or incident management, it's service now. So we already have been working with CI CESOS for a long time as as we saw AI we saw an opportunity accelerate our security roadmap a in the areas of non-human identities and identity governance because we were governing humans identity inside service now today we are the ones we onboard offboard employees typically for every company what access they have what what systems they're running what VMs they're using we used to know all that stuff in our system today in CMDB So expanding that to non-human identity was very natural for us. So that was the where the VZA it's a small company but a very innovative axis graph company for identity governance and it's really making a difference especially when the volume of AI agents are growing you need some kind of governance around it. So that's the product we provide now part of AI control tower and our security portfolio. Second thing which is happening is of course we work very very very well with IT. We also starting to work very well with OT the operational technology the shop floor manufacturing devices they also had issues in incidents and they use service now to resolve them. So it is starting to become one platform for management. So we were providing them that technology already and the company Armis which is the number one provider in OT cyber security and exposure management. We bought them so that we can close the gap and speed up our road map and create a bigger time for service now on this exposure management. If some exposure you have in your devices, how do you manage it? What are the how you going to resolve it? How are you going to take it offline? because anything in your shop floor in your factory if it goes wrong can bring down the factory. You will be out of business for a few weeks or few months which is not great for any company. So that's where ARMS fits in is to really provide this IT cyber security exposure management or vulnerability management uh using AI on top of what we were doing already. And now that is great because now customers like you know what I want to be able to have my IoT devices, OT devices, medical devices, physical AI all monitored and managed it through through service now. So that's where it fits in for us. So we've been very thoughtful about where we get in. We're not doing everything. Cyber security is use space but for us this area where we were and expanding it into new TAM which is very natural for us makes sense. >> Yeah. Now, now that you you you explain it, I think it makes sense. It's a it's a natural adjacency to IT, especially for OT departments. Now, the other expansion that I noticed you made is is autonomous CRM. That to me sounds less obvious, at least from the outside, right? You're getting into the sales teams. You are competing directly with giants like Salesforce. So, what is the rationale behind it? >> Yeah, I think see CRM is multiple businesses definition wise. I think people confuse uh CRM as only sales. There are three major parts or four major parts of CRM. Uh sales, service, marketing and commerce. The parts which we think we have natural opportunity for us is be is definitely for sure uh customer service. Customer service is similar to any incident management we do for employees. When employees have wish issue with it, they file a ticket or they call someone to get help. Now they go through chatbot or whatever it is they're asking for help and we are the ones service now was the one which is resolving that issue and helping the fix it. So taking that whole case management and incident management when a customer now end customer calls you saying I my order is missing or or I want to change something in my account or uh this is broken how can you fix it? is same thing as case management we did for employees. Sure it goes through telefony network and all kind of stuff which we integrate into. So for us to move from uh what we were doing in internal employee to c and customer customer especially for customer support was very natural. It's the same understanding. We know how to make it work. We understand how to orchestrate between different users and com people who need to fix the issue and resolve it for the end customer and our sec our our CRM business in customer service has become huge now because we've been able to build very solid product on the same platform very modern very AIdriven as you said autonomous and also now omni channel understanding voice chat documents text uh web all stuff through one through one product underneath the cover so you don't fragmentation. So we can differentiate ourself against all the incumbents in that space and the business has crossed over two billion because it's been on a very fast trajectory. So we are able to do very well in that area because we have a great product and related to that anything which requires complex orchestration uh like CPQ comp uh configure price code it requires orchestration between a configuration from a sales team to a pricing team a legal team and getting a quote out based on product. So CPQ is a very natural interaction for CRM for us. So the things we are doing are areas where we have a lot of expertise, a lot of capability and ability to really differentiate and help our customers move fast. So we are open to competing. I mean there's no harm. I mean if you build a great product uh we think we can win. We keep on winning. Uh and uh we don't do things like I mean we allow we of course provide products in Salesforce automation with sales order management but the things are just about finding information like give me the forecast. It's not complex and that is not something which we really are doing a lot of work in. The part which we do is order management, uh sales efficiency, uh doing customer support, field service management, CPQ which are complex orchestration and giving you a outcome like when you do CPQ, you need to get a quote out. When you do order management, you need to get order finished. Uh when you are doing customer support, you want to resolve an issue. Those are things outcomedriven things. We are very very excited about participating because we have the platform and the ability already to do it but we're not going into random space like marketing or things like that. We partner in those areas. >> So it's more like the the focus on the the the use case around CRM for customer service that has a stronger connection to what you do already with IT and OT. Got it. >> Yes. >> And last thing on this I mean one of the modes for for CRM has been high switching cost. still with AI I think the cost of switching is still pretty high right so in in your case even though you're already a customer you might be owning IT HR or other other functions like what is the emotion there to eventually replace and and gain more market share on the CRM side >> so I said we are doing very well in the CRM side so that means things we're doing is working we help customers migrate we have tooling we guarantee them the time we have a lot of partners we work with as well to help with the migration if they need to sometimes there's integration work not just migration because people might say I want to modernize some parts of it and then modernize everything over time uh we also provide them support from licensing perspective so they don't have to worry about uh uh things running two different systems together for too long we can migrate it faster uh we have uh done a lot of good work to be able to migrate from all the legacy CRM systems customer support systems to us uh today have a lot of successful customers now uh hund thousands of customers who are on service now CSM and uh we working aggressively to keep on getting them faster and faster. >> Thank you for this uh full rundown on AI transformation and and product expansion. Uh it's been a pleasure to learn and spend this time with you. But thank you for taking time with me as well about all the things we are doing so that your listeners can learn about service now and how we can help them as well.