GenAI in the Enterprise: A Conversation with ServiceNow | The Six Five Summit

Welcome back to six five Summit. Dave Nicholson here, got a very special guest, Mr. Jeremy Barnes.

He's Vice president of AI platforms at ServiceNow. Welcome to the program, Jeremy. How are you?

I'm great, thanks, Dave. It's, uh, really great to be here. So, ai, uh, something that we've heard a fair amount about over the last year or so.

If you, if you accept that we're at least a year into this Gen AI phenomenon, how would you characterize where we are in this journey? Well, we, we can say that we're a year in from Gen ai, but really AI has been going on for, uh, since a long, long, long time before that, you know, sometimes people will say, what's new about Gen ai? 0 in that it's ai, but it actually does what you expected it to in, in the first place.

So, uh, yeah, I think we are in a very exciting part now because, uh, before AI was something which people kind of put in the dark corner and wanted to behave itself. And, you know, not spending too much effort on it. Now it's in the mainstream and people are thinking AI is able to do things that, you know, I'd only dreamed of a few years ago.

So, overall, we're in a pretty good place. It's, it's pretty fun. It's exciting to be part of this.

So, specifically generative, you know, gen gen AI as a subset, of course, machine learning has been going on for a long time now. You've been at, you've been at ServiceNow for five years, is that right? Somewhere in there.

Is that right? I've been, I've been here three years. I was the CTO at L ai, which was acquired about three years ago.

So, uh, yeah, three years been Okay, three, three years. But, but in the recent past, you've talked to a fair number of, uh, CEOs in this space. Is that fair to fair to say that you make the rounds?

Yeah, a absolutely, uh, you know, CEOs, but also there's a lot of, uh, subject matter experts where a lot of CEOs know that they gotta do something but don't necessarily wanna take the time to learn. And so I often get involved here with, uh, CTOs, CIOs and their teams, uh, in order to go one level of detail down beyond, you know, what is this AI thing to exactly what can we do with it in our business. So what are you hearing as the, as the kind of hopes, dreams, aspirations, and, and concerns out there as you, as you engage with folks on the front lines?

Well, certainly, yeah. Let's start with concerns. Um, you know, absolutely the mandating this year, you know, 2024 is a year of, uh, you know, board mandates to go figure out generally AI and what it means for the, uh, for the company.

And so a big part of the concern is, you know, this is, uh, there are results becoming expected, right? This is not just to do a proof of concept and make a nice graph. You know, people are thinking, okay, what is this gonna do to my business?

I have some challenges with my business that this might help me address. I want to actually move the needle on those things. I don't wanna just sit down and talk about it.

So, definitely concerns about, you know, are we gonna be too late? Are we gonna lose competitiveness if we don't move fast enough? Are we gonna move too fast and break things?

Uh, you know, there's a lot of that, uh, you know, hopes and dreams. I mean, that's the part which I find the, the most exciting. And that's, that's really, uh, there's a lot of, yeah, there, there are kind of like, I call 'em by snow plower roadmap.

So it's like you imagine a snow plower driving and the snow's just getting pushed in front of it. There are a lot of things in companies which were, this is part of our vision. I think somewhere someday we'll be able to do it, but we keep just pushing it off a year in front, and it just stays in front because it kind of never, you know, it never gets a, uh, away from the snowplow.

Now, gen AI allows us to clear that, right? It allows us to clear that that jam, it allows us to do things which were potentially years and years out before. So we're also seeing a lot of reconfiguring, which was, uh, we had structured our organization.

Yes, what I'm hearing, we structured our organization based on some things being very hard, and we think that now they're gonna become really easy, and suddenly there's these gigantic new opportunities, which we've gotta take, but we didn't set things up for them in the first place. And so all this change management, in order to allow these hopes and dreams to be realized, we're seeing a lot of that as well. So this whole, uh, you know, the concept of doing a digital transformation in order to drive something, you know, a lot of people started off on a digital transformation route with a sense of, you know, it's gonna help with someplace on the bottom line.

It's gonna make our workforce more effective, better serve customers. Gen AI is clearly the killer app for a digitally transform enterprise. And so now it's like, okay, we see that we gotta just put this in overdrive and get everything ready, uh, from the technology perspective, but also the organizational perspective as well, uh, to make this a reality.

Yeah. Now, it's interesting because ServiceNow has been a, you know, a trusted partner for businesses for many, many years. And in a sense, uh, ServiceNow is on the very same journey that its customers are on in terms of coming to grips with and leveraging generative AI moving forward.

What's the, what's the current state of the art in terms of how ServiceNow is, is, uh, infusing, um, what it's been doing for its customers for a long time? Um, you know, how are you infusing it with generative AI moving forward? Yes, that's a, uh, that, that's a really, uh, great, uh, lead in, uh, 'cause this is something which we've, we've put a lot of effort into, uh, building out the, our, you know, our CGIO department.

Uh, so we do two things. Number one, that department there is empowered to do the same things that our customers would do. So not necessarily using the, uh, the, the tools that we build, but looking across the broad industry ecosystem and saying, what is the best tool for the job?

How can we, how can we make the organization as effective as possible? But at the same time, in parallel, we also deploy all of our products, uh, internally, and we are customer zero for. So a lot of the early feedback, validation testing, uh, yeah, a lot of our numbers or in terms of what does this mean for a business, what can I expect in terms of outcomes are calibrated based on what we see internally as well.

So, drink Your own, you drink your own champagne, as they say. We, we absolutely, uh, drink our own champagne. And, uh, you know, the, there's, you know, there, there's, uh, drinking champagne too early doesn't necessarily taste as good as, you know, when it's been, uh, aged for a while.

But that gives us the opportunity to know, you know, we absolutely don't want our customers to drink it until we know that it's a really good vintage. And so that's the approach that we take. So we've put a large amount of effort into building out this CDIO department, which can be completely empathetic to customers because it's got access to the same tools as well, but also understands exactly what is the, uh, what is the gap between what's needed and our products, or what's there in the industry as well has a really bigger advantage that we have in the, uh, in, in the ServiceNow, uh, product development, uh, methodology.

So when, in, in general terms, uh, when people think about, uh, information being generated in the, in the, in the generative AI context, which is different than machine learning coming up with a response or business guidance, often, uh, often people are concerned about this idea of, of, uh, bias entering a system. Um, not, not necessarily political bias, uh, but any kind of bias that can direct guidance in a way that we don't necessarily want it directed. Do you see bias as a challenge?

Um, what about governance and com and, and, and things like that? What are, what are some of the challenges around generative AI from a ServiceNow perspective? Yeah, we, we absolutely care about those things for a simple reason that our customers do.

Uh, so the, you know, one of the, uh, ways that we think about product is it's not just a question of what does a product do, it's how effectively can it be, can it be used? So for that, uh, you know, for that reason, we think about those things. Our customers, uh, have good answers to the questions when, when they're asked them for bias.

The, I think the, the thing which a lot of people don't understand about it is, if you don't, e everything has a bias. So if you don't measure it and a design, so that that bias is, you know, minimized or so that bias is, it's active wherever you want it to be, there's a bias there. You just dunno where it's right.

And so a big part of it is how do you do that testing? How do you proactively look to see, let's assume that this is biased, what could it do? And then test to see, you know, if any of those outcomes are happening, and to the point where you can say, well, we are now pretty sure that we know exactly what that level of bias is, and that's at the acceptable level.

We have hundreds and hundreds of people who do testing of all different kinds, uh, to ensure that we we're able to make that promise to our customers. And that means our customers don't need to have those hundreds and hundreds of people, you know, in every single one of our customers there. So bias is, uh, you know, it is a really, uh, interesting and subtle subject.

We don't expect our customers to be experts in that, you know, we, we, uh, take that work out and we, uh, you know, that, that's part of what we provide governance, uh, and everything around responsibility and, and trustworthiness of ai. We, there's a few things that we do there. Uh, the first is that we have our human centric AI guidelines.

And so, you know, things like transparency, human centricity, um, uh, you know, accountability, uh, things like that. They ensure that when we develop our products, that we look at them from all the lenses, which allow us to understand, you know, how people about to see the products and how we, the trust is not something that is automatic. So trust is something that you build up over time, making sure that we allow that trust to build.

And we, we, you think about exactly how that's going to happen. Uh, and governance, you know, you've heard, I'm sure, uh, about the, all of the, uh, your EU AI act, uh, for, for European, uh, customers. There's the presidential order which came, uh, was beginning to come into effect.

Some of those rules have been published. And then there's just the, you know, all the normal enterprise stuff you do around risk and governance and, and like that, uh, our customers, you, we, we have very large, sophisticated customers. They absolutely care about those things.

And so it's also building into the product experience, the ability to understand what's going on, to surface the information, and to govern those in the existing enterprise processes, which allow it to be deployed in a way which is compatible with your whatever, uh, procedures and, and policies that our customers have in place. And those are, that, that's all the building around. That's not the core technology.

That's everything you need to build around it in order to enable effective and successful customer deployments. Yeah. Yeah.

That makes sense. Well, let's, let's talk about more kind of, I don't know if it's necessarily the core, but just, um, can you gimme some examples of, uh, may maybe look at it this way, um, something from the ServiceNow portfolio or suite of capabilities, uh, what that scenario traditionally looked like before ai, now we have the advent of maybe more advanced machine learning. Now we're talking generative ai.

How do these things change? Uh, you know, if you've got, if you have specific customer examples, great. If you've got anonymized examples, great.

But give us a more concrete example of what people are doing with generative AI in this context we're all familiar with, or most of us are now playing around with the various tools that, that keep coming out every week. Um, but what about if I'm in an IT service management environment, what's, what's generative AI gonna do for me? Can you make it more, more, more palpable?

Yeah, a absolutely, and this is, I really love that question. 'cause we are fortunate enough that there are millions and millions of people who, you know, spend all their day doing work and getting work done inside the ServiceNow platform. So when we talk about, you know, how do we make this concrete, we can actually talk about specific user personas that spend their whole day using the software that we build, and we understand what they spend their time doing, where they get stuck, uh, when they meet their objectives and when they don't.

And, uh, a lot of our, uh, you know, prioritization that we do in our roadmaps and things like that is related to not what we imagine that people want to do, but really the work that they're actually doing and how they get that done. So an example here, uh, I'm not gonna go into the obvious, um, you know, virtual assistant type example because I, I think that's been done to death a little bit, although we, we have a really good, uh, uh, story there. Let's talk about, Yeah.

Give us something else. Yeah. So let's talk about a, a service agent, right?

Uh, so you know, your responsibility is to you, you'll have a set of incoming, uh, your incidents or cases that you need to handle. And this could be, this could be an IT service agent. Uh, this could be, you know, an IT operator who's looking after a, a data center.

You know, this could be customer service, and there's a whole bunch of, you know, field services. There's a lot of personas who fit into that. Now, if you look at how people are, are working, you, one of the biggest challenges that they have is when they, you know, if they just finished their day and they've handed over to the night shift or something like that, they come back, all the things they're working and could have changed throughout the night, right?

Or there could be new ones that have been handed over. There's all these reasons that suddenly their idea of what's going on, you know, might not be up to date with reality. And so in the interface we, uh, allow them to, uh, you know, various productivity tools there coming out, things that allow them to, for example, figure out what they should work on next once they get it, it's summary of what happens in our last di so that instead of needing to go back, and sometimes there's hundreds and hundreds of interactions, they get a sense of, okay, these are the important things that, that have happened.

Once it comes time to resolve it, uh, there's probably information elsewhere in other, uh, incidental cases, which are pertinent, but they're not gonna read through 120 of 'em to understand you exactly what's going on. The generative AI can do that and create a summary saying, this is what's common. And in your situation, this is what seems to be the, the disaster.

And present that information to them. Once they get to the point where they want to interact with the user, again, instead of needing to type out, uh, all of the details, they can be taken from that summary and put into place. Uh, of course they check the message before to, to make sure that it makes sense, but it's a lot less mental effort to do that.

And they're sure that they didn't miss anything as well. They send that and, uh, let's say that the incident is resolved after that their incident notes can be created with generative AI in order to make sure that the, the resolution notes are accurate, but that that information is available for the next agent as well. And then finally, if they realize actually this is something which is happening frequently, and they wanna create a knowledge base article, they can create that using generative ai and that can feed directly into auto resolving incidents.

Uh, when a user will come in through the virtual assistant, it will be able to use that information to allow them to self surveys afterwards. So they can also create this extra efficiency there with, with the automation. Now, all of those things you could have potentially done as a, as a service agent, but the time it would've taken to do them all means you never would, you know, you cut corners.

There's, uh, productivity is important. So we enable a faster outcome, less mental load on the, on the people as well. And each time that, uh, an agent interacts with the system, they create a basis for further efficiency in the system itself.

And it's really generative AI in lots applied, you know, not as just one use case, but in lots and lots of places to, uh, you, you know, that enables all of that to happen. And we find that really, really exciting because we can look after, you know, really the entire workflow of, of people in our platform. Yeah, that, that, that's a great example of having the, the, the, the digital assistant that's there to basically say, Hey, welcome in.

Let me tell you what's been going on since you were, let me, let me get you caught up so you can hit the ground running as as, as you get going. I know personally, I've started using very, very informal language and tone of voice when I'm interacting with, uh, with, with these natural language assistants. It's, it's amazing.

Well, tell us about what is, what do you see coming in the future? Um, we've got about a minute left if you can, uh, if you can kind of give us a summary of what, what you think the future holds. The, the future is, uh, is really interesting because it's not gonna be a future of just generative ai.

It's not gonna be a future of, uh, you know, people doing one thing and generative ai another. It's really gonna be a future where generative ai, you as agents or as assistants, works together with people. And, uh, that's gonna push that the horizon of, of what people expect to be able to get done, the kinds of problems they can solve, the things they can build, the level of service they can provide, uh, you know, it's gonna increase the, the horizons of people's ambition.

So, you know, that's gonna be really exciting. Uh, all all of that is gonna happen. We also see it from a enterprise perspective of, like I said, this is the killer app for a digitally transformed enterprise.

So enterprises are going to put the digital transformations into overdrive. They're going to use that to do things with generate AI that make the biggest step change in their business that they've ever seen. And we're gonna see that driven, uh, you know, by this technology, but by also this maturity as people understand that generative AI is not something you bolt on.

That AI is something that you build into your business. And you know, we're gonna see this. The businesses that do that well, we're gonna see them just achieve things which are absolutely amazing, even by today's standards.

Sounds good. I say we schedule a meeting a year from now, let's have our digital assistants, uh, meet one another and discuss what's happened in the prior year, and then they can brief us individually. It's a brave new world we're entering my friend.

Yeah, your advertise been chat for sure about that. Thanks so much. Jeremy Barnes, vice president of AI platforms at ServiceNow.

Uh, for the rest of you stay tuned for more interesting content from six five Summit.