In this talk, Dave Wright, Chief Innovation Officer of ServiceNow, talks about how low-code is accelerating organizations’ AI journeys and is expanding into new, scalable use cases as we move into a post-COVID-19 world. The video and a transcript of the conversation are below.
Recorded Voice: This is Digital Anarchist.
Charlene O’Hanlon: Hey, everybody. Welcome back to TechStrong TV. I am Charlene O’Hanlon and I’m here now with Dave Wright, who is the chief innovation officer over at ServiceNow. Dave, thanks so much for taking a couple minutes and talking with me today. Always appreciate it.
Dave Wright: No, thanks for the opportunity.
Charlene O’Hanlon: Okay, great. So first, I’m really interested in your title, chief innovation officer. What’s that all about? Tell me.
Dave Wright: So it has multiple roles I suppose. Part of it is looking at how we understand what customers are doing with the products, so looking at customer stories, building out reference cases. Some of it is looking at what we do around the world of evangelism and looking at how we talk about what the technology does.
And a lot of it is around looking at new technology and trying to understand, is that new technology gonna impact us in any way, or do we need to potentially look at some of these new areas of areas that we can move into to as well. So, it’s pretty cool. I get to spend a lot of time researching things. I get to speak to people, everyone from psychiatrists and sociologists all the way through to technologists. So it’s a pretty cool job.
Charlene O’Hanlon: Yeah, sounds pretty nifty. All right. Well, I wanna talk to you about low-code and it’s something that – We’ve been talking a lot about low-code over the last 18 months or so. It seems to have – I almost kind of feel like we’ve jumped the shark on the topic, but I wanna talk to you about artificial intelligence and the use of AI in low-code as organizations are swiftly adopting it and recognizing the real value that low-code has for the business.
Obviously, AI is kind of icing on the cake, if you will, if it’s done correctly. So, interested in getting your thoughts on the role of AI in low-code and maybe kind of see what’s going on in the industry, what you’re seeing there.
Dave Wright: So I think a lot of times when people produce low-code applications, one of the things they’re really trying to drive is adoption of those applications. So when you build something, it’s gotta be better than whatever people were using before. And I think that when people start to integrate artificial intelligence into those low-code apps, what actually does is it starts to elevate the experience a little bit.
So, it makes sure that the application you create actually delivers smarter outcomes. And this can be anything from ease of use of the application. So how do you start to process data and collect data in a different way? Or it can be the actions that the application then takes. So how does it learn from more and more people using the system to gradually improve and modify the experience to make it easier to use?
So I think those are elements that you see people building in conversationally, or you see people building in with ML models. I think there’s probably a second part of the conversation, and maybe we’re not quite there yet, but I think in the future, you’ll actually start to see artificial intelligence influence the coding side of low-code. So actually helping people develop those applications as well, but we can go into that a bit more later.
Charlene O’Hanlon: Yeah. Well, that’s actually kind of one of the things that I was thinking about, because if we’re talking about low-code as a business enabler, so obviously we’ve got a lot of folks out there creating code or creating applications using low-code, and who don’t really have that experience in coding.
So I imagine AI is kind of like a tutor to – That’s how I view it as AI within the low code application being used as kind of maybe the conscience of the voice of reasons. Like, “Are you really sure you wanna do that in that application?” Or just kind of helping them build a better application and user experience as part of that.
So I guess we’re kind of talking about two different things. We’re talking about low-code and AI, because we could be building AI application using low-code technologies, or we could be using AI within the low-code application.
So, are both of them kind of happening right now? And then, do we really need to distinguish exactly what we’re talking about in our conversation? And we could be talking about both of them, which is fine. I’m totally okay with that, but –
Dave Wright: So I think the both happening, the one that’s happening the most is people using AI within low-code applications. So that’s gonna happen on multiple levels. So it could be I’m building an application where part of it is collecting data or it’s interfacing to a person. So that might be the inclusion of things like virtual agents.
And this is where you start to get into the real benefit of low-code developments. When you’ve got pro-code developments, you go out there and you build something, you’ve got a program of entirely skill that can build out exactly the interface you want, can build out the data structure.
When it comes to something like that, building a national language understanding conversation, , it’s not the programming that’s hard, it’s the subject matter expertise, it’s knowing what to ask and what to ask next.
So if you can put that capability to build those conversations into the hand of the SMA without them having to be some programming god, then it means that the conversations flow more naturally and you get what you want. So I think being able to enable people to build those types of interfaces when they haven’t got the programming skills, really speeds up what happens in the world of low-code development.
And then I think you have the second part of it, where perhaps it’s using machine learning algorithms to then simplify processes. So, a great example of that is, let’s say you build an application and in that application, you’re gonna describe a certain type of request that you’ve got or something that you need.
And what you do in a traditional non-AI powered system is, then you walk the user through some system where they had to categorize what type of issue it was. And then based on the categorization, you’d pass it to someone to execute. But if you can put, let’s say a machine learning step in that, where it looks at the description, and it goes, “Okay, based on this, I know what type of request this is, so I’m gonna categorize it.”
Then you start to, A, remove the potential for human failure, where someone miscategorizes, but you also start to think about how you design your interfaces, because now you don’t need drop-down lists.
So you merge and you take that app, you mobilize it, you put it on a mobile device. You can have a much more cleaner, simpler interface, because a lot of the work around data collection and data determinations being done by ML algorithms sitting in the background.
Charlene O’Hanlon: That’s interesting. I hear so much about low-code actually being used within an organization to create kind of those secondary internal business applications or internal business processes, and then kind of leaving the big stuff, the customer-facing stuff out to the developers themselves. But it sounds like, with this infusion of AI into the low-code applications, that may not necessarily be true forever?
Dave Wright: Probably not. I mean, I think the use case that you talked about them, where people are doing it for business applications, or maybe not business applications, the way I see people building them are applications for core operations. And it depends which area of business you go to.
So you might go to a financial Institute and they’re looking at how they do loan processing, or you might go to a hospital and they might be looking at how they do clinician onboarding or how they set own emergency medical centers. So it tends to be something that’s very close to your core business, what you’re all about, that things don’t necessarily come off the shelf and that you need that specialist knowledge to build. So that’s where you see it now.
I think moving forward, perhaps we start to see this concept of people using it for much wider use cases and starting to build bigger and bigger applications, or perhaps even more interesting, starting to chain the applications together. So there is also wrong application is actually driving something in another.
And then we have concepts like the bigger AI models that we start to see emerging now. So some of these large language learning models, the GCP three-type concepts where you are starting to look at the capability to be able to describe an application and then have AI build that application based on the description. That’s an interesting development, and it’ll be interesting to see where that goes in the next two or three years.
Charlene O’Hanlon: Yeah, that is definitely something that I think is fascinating, but it’s also slightly frightening if you just tell the robot to go build something or the machine to go build it. So, with that in mind though, are we starting to see a shift in the way developers, their roles moving forward, because we are in effect kind of taking away at least some of the remote or the rote tasks that they do these days, kind of the lower through low-code and AI. So how do you see that changing, how developers view their jobs and what they actually do?
Dave Wright: So I think a lot of the things with artificial intelligence in the role of developments will be the same that we’ve seen the way that artificial intelligence works in the regular world. So what most people use artificial intelligence for is to either improve the accuracy or remove the mundaneness to try and take some of the stuff that people just don’t wanna focus on away.
And I think that’s where you’ll start to see the first AI use cases in the world of developments where people are looking at standard routines that they use, or they’re looking at tasks that they just – They wanna focus on the complex side of it and have the simple side of the stuff just built from –
So that coordination and that orchestration of being able to take what you need to be able to build the application. I think it’ll be it more AI as an assistant to programmers. I did a presentation a few weeks ago and someone said, “Does this mean AI is gonna replace all the programmers in the world?” And that’s not true. It’s no more than it’s gonna replace all the artists in the world, so that [crosstalk] in the thought process still sits in a program’s mind.
Charlene O’Hanlon: Yeah. And there’s also a level of experience and expertise that developers bring that low-code never could. If you’ve got somebody working with a low-code technology who is not a developer, they might not see the things that might trip up the program the way a developer might see.
So I firmly believe that obviously there will always be a place for developers in the development environment, but I do certainly see the value of low-code. Even though I was joking at the top of our conversation that I kind of feel like low-code may have jumped the shark, but I think that there are so many different and practical use cases for low-code technology in organizations that are technology organizations, or they just happen to build sneakers or something like that.
There’s always gonna be an application there where a part of what they do, whether it’s customer-facing or internal, they won’t necessarily have to even bother the developers or the IT part of the organization to get those things done. So yeah, I think low-code is actually going to kind of permeate the entire organization moving forward. It’s a matter of time. And I think AI is gonna be a huge enabler for that.
Dave Wright: I think so. I mean, if I look at our world, if I look at ITS ServiceNow’s world and how people build up on the platform there, we’ve done things like introduced the concept of app engine studio to make it simple for people to build apps if they can’t code. But then even on top of that, more and more people have said, “Hey, can we have standard templates?”
Because a lot of people are gonna say, “Well, I’m gonna build an application.” And all the application really is, is an approval process, the way to track something being approved, or it’s a way to track leave, or it’s a way to track something that’s really simple that people do every day. “Can you just give me a template that allows me to go in and be able to do that?”
So we’ve seen that concept already start to emerge, but the thing that always surprises me is, I’ll speak to businesses and some people say, “Well, even though you call it low-code people still need a bit of skill to be able to build these things. But I always think the irony of that is I’ve seen people do things in Excel that I couldn’t do in a million years.
The complexity of the stuff they’ve got in the background, it’s like, “Wow, I don’t know if I can build a low-code app.” We’ve moved on quite a bit now, the concept of low code is available to everyone.
Charlene O’Hanlon: I think it’s amazing and I think it’s great technology. And we’ve been covering low-code on devops.com for, going on five years now. So, it’s obviously been a technology that’s kind of been waiting in the wings for its moment. And the pandemic really highlighted its value to organizations.
And I firmly believe it is here to stay and I’m excited about seeing how it can be extended in an organization and really bring a lot more business value than it is currently. So Dave, thank you so much for having the conversation with me, always enjoyable. And hopefully, maybe this time next year, we can have another conversation on the same topic and see where we are compared to where we are today.
Dave Wright: I would love to do that. All right. Thanks so much.
Charlene O’Hanlon: It sounds great. All right. All right. Well, thanks again, Dave, for your time. I really do appreciate it. All right, everybody, please stick around. We’ve got lots more TechStrong TV coming out. So stay tuned.
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