- Awesome. Hey, everyone, welcome, and very good afternoon to all of you. So welcome to our session today, where we are talking about how at ServiceNow we are accelerating the delivery of services that we provide internally to our customers and to you all externally by optimizing and automating the processes we use to deliver those services. And we are part of the Now on Now team. What does that mean? We are actually the customers of ServiceNow as well within ServiceNow. So we are customer zero. We are exactly like you. We are trying to deliver our services to run our operations internally and we adopt every single product that ServiceNow releases right away and we provide feedback to our product and also share experiences with you all for the smoother adoption. So with that said, let's dive into it. A quick safe harbor notice. I know you all have seen this multiple times today, but I still have to do this. So this presentation may include some forward looking statements. Please do not make your purchasing decisions based on this. With that said, a quick introductions for us. Myself, Ishant Goyal, and I am part of the Digital Technologies group within ServiceNow, formerly known as IT. And I'm the director for platform automation, and my team is responsible for optimizing the processes and automating them for faster service delivery within ServiceNow IT. And with me, I have my colleague, Nidhi. - Hey there, I am Nidhi Vohra. I'm a product manager in automation products within DT, and I'm helping out Ishant and his team automating different business processes. - Amazing. With that said, let's take a quick look into today's agenda for this session. We only got 20 minutes, so we'll go through this quickly and we'll make it sure it's more meaningful for you all. So we're gonna look into why we need to fast track the need for process automation. And we'll also look into how at ServiceNow, we are approaching this problem and automating our processes internally. And we'll also introduce you all to something we are doing something internally called self-driving process automation. And then we'll share some key success stories with you, some gains we have seen internally. And we'll end the session with some key lessons learned. With that said, a cup of coffee, right? This is one drink we all love to have. I'm pretty sure this is something we cannot even imagine to not have to start our day with, right? And I know some people may like tea, some may not even need any sort of caffeine, and I wish I could be that person, but not really. I need to get some caffeine in my body to keep going. But you know what? like the interesting thing is getting this beverage was not easy as it is today. In early 1900s, the process to make coffee was pretty manual and rugged. People used to get coffee beans from market, they used to roast them, they used it to grind them using the pestle and mortar. They used to boil them into the water to extract juices to then finally get a cup of coffee, right? But now where we are. We have a single coffee machine, right? Now you can go to the market, purchase your favorite roasted coffee beans, pour into this machine, press a few buttons, and you get your favorite beverage in a few minutes. We have the coffee brewing machine at the Deloitte Center. You must have all seen it, right? So this didn't happen overnight. It took a lot of time, and there was a lot of advancement in technology that happened over the period of years. And companies like Starbucks revolutionized this whole process because all of their customers wants to get this coffee pretty quickly, right? Similarly in our enterprises, we all have to deliver services to run our operations, right? And these services could be IT services, where we are provisioning software, access for someone, or it could be our security services, could be HR services. And all our customers, whether they're internally or externally, they're all looking to get these services at scale and pace, right? And that demands the need to automate the processes and the recipes we use to deliver these services. Not only that, per a recent survey conducted by KPMG, 65% of our enterprise technology leaders are asking us to do more with less. I'm pretty sure you all must have heard the statement, right? Do more with less. So all of this is telling me that not only I have to accelerate the delivery of service by automating the process, I have to do that with lesser amount of resources, right? What does that tell me? We need to fast-track the process automation process itself, right? Not only I have to automate the processes, but I have to do that at scale and at speed, right? So how are we solving this at ServiceNow? Well at ServiceNow, we have a four-step process, right? It's pretty straightforward when you look at the theory, right? You take a process, the first thing you do is you start analyzing that process to understand, okay, where are my inefficiencies in the process? What can I improve on, right? What can I automate, right? And then once you know those inefficiencies, you try to understand, okay, why that's happening? Why my incidents are taking longer time to close? Why my incidents are taking so many hops to get to the right group, right? And once you know those reasons, then you go and start identifying and recommending solutions to implement those. And it could be either a change to your process or it could be a change to your people or it could be a change to your system, right? And once you have those solutions, then you implement those solutions and you get them into your enterprise systems and then you go through the cycle and over again, right? And every time you do this for your process, you accelerate your process by certain percentage. So with that, let's look at it with an example how we are doing this internally at ServiceNow. And for that, I would like to invite my colleague, Nidhi, to share her experience in how she automated her process and made it faster. Nidhi? - Of course. So the example that I'm going to take, it's actually going to resonate with many of you. The software provisioning request, and almost all the organizations have it. And at our end, it was taking too much time to fulfill. And we started analyzing, we met lot of challenges, it was time-consuming. We used to run a lot of reports and even figuring out what all inefficiencies were there, it was lot of time consuming and very cumbersome process. So then we started using some of the products that were at hand, like Process Mining or Analytics Hub. So by using these products, we were able to cut down our effort by 60%. Isn't that amazing? So that was our analysis step. The second step was identifying. Identification of actual bottlenecks behind these inefficiencies. So during our analysis process, what we were able to do, we were able to find the inefficiencies, but why are those inefficiencies happening? What are the bottlenecks? What are the reasons behind this? So again, when we tried to do the all those pros, when we tried to identify all these things, those were pretty cumbersome and time-consuming. Like, for an example, whatever I mentioned, like software provisioning requests, what we analyzed and what we identified that 98% of the requests were already getting approved and only 2% were getting rejected. If you see the example that I'm taking, around 56%, or the 56 records were getting rejected. Now that I knew what the issue was, I still wanted to know that what can I do without it? So maybe I can remove the approval step that I have in that process. But for that, I had to open up, I had to go to the list of records, those 56 records, I had to open each one of the record and find out what the reason was. That was again a cumbersome process. And on top of it, I had to detect the patterns. So then, we started using some of the products, like Process Mining and even GenAI. So with one click of a button, we were able to find the root cause, and also, we were able to see what the patterns were. The GenAI and the products we use, it was able to find the issues and detect the patterns. Now that we had inefficiencies identified, we knew the bottlenecks, now we needed the recommended solution. So we knew that at one point that approval step that we needed to remove. But when we started using some of the products like Now Intelligence, RPA, or some of the automation products, the system was able to recommend that, it was able to pinpoint what exact flow to touch and what exact approval step to remove. And also, there was another issue that we had detected in last few steps that one of the catalog item that was open to all, but still some of the requests were getting stalled. They were not getting approved. So then we figured out it's open to all, but the software that we were requesting was only meant for full-time employees. So the recommended solution was that restrict that catalog item only to full-time employees. So implementation. Implementing manually all the recommended solutions was very slow. Of course, all of us have big backlog to cater to and there were potential delays. So what we did, we started using Now Assist text-to-code and, of course, DevOps. So what it did was it expedited our implementation process. Whatever recommended solution used to take us one sprint with DevOps pipeline, we were able to implement in couple of days. And one of the example I can give is one of the catalog item, where the approval was taking more than five days to get to the next step, we wanted to add escalation process that if it is sitting for five days, it needs to get escalated. So with text to flow, we were able to create where a simple flow so that the request can get escalated if they're stalled. - Isn't that fascinating, guys? Like this is amazing. And you must have already seen what Joe Davis did this morning in our keynote, where they were showing some of these solutions. So this is what helped us accelerate the automation of our processes and make our services deliver more faster, right? But you know what, Nidhi, I have heard some rumors that we are brewing something special back in a lab in Santa Clara. Do you wanna shed some light on that? - Oh my god, Ishant, I don't know who spilled those coffee beans. So now that the cat is already out of the bag, so let me share it with you and with our attendees. So let me introduce you to self-driving automation. So what is self-driving process automation? The steps that we discussed recently, right? Identification, analysis, identification, recommendation, and implementation, those who are being done in silos earlier. So what we are doing right now is we are experimenting how we can weave all these four automated steps into one single automated thread. So what it means is that we will feed the data, it'll detect the patterns, it'll recommend the solutions. And once the process owner accepts the solution, it'll get implemented. So we are not only designing, but we are also taking it step further, implementing automatically. So this is what we had been experimenting. - That's amazing. Isn't like this is fascinating, guys? Like this is like automating the process of automation itself, right? Once you have, like just like a coffee machine, this is gonna be your process optimizing machine, right? You put your process and a much more optimized process gonna come out. So, hopefully, by next Knowledge, we're gonna bring something live for this. With that said, automation equals to savings, right? Every time we do automation, every time we accelerate our services, we save dollars, right? At ServiceNow, we have been doing this for the last couple of years. We have mined around 33 processes across most of our workflows, whether they're IT workflows, customer, HR, security ops, and whatnot. And then on an average, we have seen 20% efficiency gain across each workflow. Some of the workflows, we have seen even more efficiency gain, right? And all of this, we were able to do, we were 68% faster in the process of automating this thing, right? Before we had our hands on all these different tools that Nidhi just spoke about, it used to take us weeks and months to analyze the process and come up with opportunities and then get them automated. And now, with all these tools, we are able to do this much more faster. So this would help us scale the automation of the processes and the delivery of our services. So with that, some of the key takeaways for you all. The very first one is automating the process is a journey and not a destination. So what I mean by that is like once you take a process, you optimize, you go through this whole cycle, don't stop there. Come back to that after a couple of months and reanalyze that process. And I'm pretty sure every time you do it, you will find an opportunity to automate or to improve a process, right? - Next takeaway, connecting multiple technologies, like RP or integration hub, yields end-to-end process automation. So as we saw in last four steps of process automation, we had used multiple automation technologies, which led us to more efficiency gains and quicker automation. - And last but not the least, disruptive technologies like GenAI can unlock keys to success. Well, who thought any year ago that we're gonna have an LLM or a GenAI that's gonna just summarize things for me much more quickly? But now with the tool like we have, as Nidhi showed in one of the step of identifying the reasons, we were able to use generative AI to analyze all our work nodes and detect patterns out of it much more quickly, right? So I know over the period of time, new things are gonna come in and they're gonna make things more faster. They're gonna unlock things, which we are not able to do it today. So gonna just keep eye on it, keep adopting that. As Jensen said today, "We need to ride on the train, not on the side of it," right? So with that said, we will be open for questions here. So if there are any questions, we can take them here, or we are at our hyperautomation both in Now and Now zone. So we are happy to dig more deep into this and share more insights into it. So any questions? Yes? Is that self-driving automation the future or here now? - It is now. Self-driving process automation, it is now. So whatever we explained, we are already experimented, and it is working. (participant faintly speaking) - Right. So I- - Yeah, so the question for the audience, is a self-driving process automation, is it available for you all? Well, this is something we are experimenting internally, and we are pretty close to that. And once we have it, we would definitely share some of that on our Innovation Lab on the app store. So keep an eye on that. And, hopefully, in product we'll also see something soon. - In fact, it is already available till one point, right? - Yeah. - Like improvement opportunities are already getting recommended. We are a Process Mining tool. It's the experiment part, which is next few steps, like automation or accepting the recommendation and then deployment, that is what we are experimenting and that is what is mentioning that it'll be available soon. - So if there are no more questions, as I said, we are at Now on Now zone at hyperautomation booth. Please come and see us there. And we will be happy to take more questions and dig deeper into this. And we can also share what we have done with self-driving process automation with you all. Thank you. - Thanks a lot. - Thanks a lot for attending this session. - Thank you.