Real ROI from AI | The Six Five Summit

Hello, and thank you for joining us at the six five Summit AI Unleashed. Welcome to the session, delivering Practical Applications with ai. I'm Paul Nash, a practice lead for the application development practice at the Futurum Group, and I'm excited to introduce you to Andy from Twilio.

Andy, would you like to introduce yourself? Hi, Paul. Thank you for having me.

Uh, yes, my name's Andy o' Doer. Uh, I'm the VP of Product for Voice and Video here at Twilio. Andy, welcome and it's exciting to have you here.

So, Twilio, let's tell the, uh, audience what's Twilio and why, why should they care? Sure. Uh, so, uh, Twilio is a customer engagement platform, um, founded about 15 years ago from, uh, really the invention of what we call cpass communications platform as a service.

That's really how the company really started is, is abstracting way all of the complexity and fragmentation of telecom so customers can communicate via voice messaging, OTT channels, email, video, every channel that might arise. We're a really, a business needs to communicate with consumers. So, um, now we're a global company, over 300,000 customers building on all of these channels.

Um, created really this CPAs category, um, through acquisition, uh, acquired a company called Segment, which is key to our data strategy to power. Really where we're going is the next level of customer communications, where we see this blend of communications best in class, world class, global scale communications channels, along with data, um, to really power more personalized, more effective communications channels. And then you mix in all the advancements with ai, both AI in, in-house at Twilio, and also with partners as that landscape changes to really bring what we call a trifecta of data communications and AI together to really move even beyond cpass, where we then, um, into new levels of, um, of customer engagement, uh, for, to be more effective for customers and consumers alike.

So, uh, yeah, it's been, uh, a really adventure. I was a longtime customer of Twilio for many years before joining, uh, Twilio on the, uh, on the product development side. Eddie, it's, Hey, it's really great to have you here because, you know, I last, uh, was it last week or so, we, I was, uh, in person with you at the Twilio executive event.

Um, lots of activity going on there, lots going on with how, what developers are working on, uh, the market efficiencies of what we're seeing in the market. So there's a lot happening there, but, you know, let's jump right into that first kind of question and think about how we wanna answer this for the audience when we're looking at investments, right? And we look at AI at every layer of the stack.

Um, you know, we're seeing that we're, we're talking about AI to production and customer facing, particularly gen ai and, and, and when things get real, right? Yeah. How do you see, uh, a lot of this hesitation due to security and compliance?

Uh, yeah, sure. I think, you know, there's, there's no shortage of amazing new updates of language models and developments in ai as well as, like you said, investment up and down the stack from, you know, the, the, uh, hardware layer all the way up to, uh, to SaaS apps and things that are even wrappers on top of LLMs that are getting massive amounts of funding. Um, but there is the difference between that and real customers solving real problems out in the real world.

And yeah, those, those areas around security compliance, um, those starting to get into new territory, especially with, uh, generative ai. Um, you, you might see a case where we, we have many customers, hundreds of customers live with, uh, with a variety of capabilities that we've offered. And we think that, you know, Twilio's in a unique position where you think about how do I apply AI to improve a customer consumer experience?

Um, I can use generative right outta the gate and maybe I can deflect some calls and, and I can stay out of inter uh, introducing all of my consumer's data into the mix, all of their usage data, all of that type of data that segment, uh, at Twilio has been prime time for, uh, for really activating that data to improve the outcomes. Um, so you, you do get that apprehension of, but then I unleash it into the wild out in front of my customers. Uh, what might go wrong?

What other data do I need to bring into this virtual agent, for example, to make it smarter and better and faster, uh, but not, not introduce, uh, security risks. So we've taken a, uh, a, a couple approaches. One is transparency.

So we've launched, uh, what we call even nutrition facts, like quite literally took from a transparency, uh, uh, construct that exists out in food of labeling. What is in my food? What is in my ai, what models is Twilio using?

At what layers of the stack? What data am I sharing to those models at different layers of the stack to get the outcome that I want? So I think first things first is you have to have that transparency, uh, to be able to then get the trust of your customers to be able to not just engage at these early stages, but then to unleash it out and out into the wild so you can avoid, you know, those things that you see in headlines of hallucinations or leaking of customer data out to a bot that you didn't really want to leak out to a bot.

You know, those types of things. So we think that transparency is key, and we've introduced a whole trust center and trust layer, um, at Twilio as, as well as, uh, because we've had many customers in large variety of industries and healthcare and financial services that even in communications they need, they need to trust Twilio. With that, as we layer on the AI piece, we feel like that transparency of what's under the hood, uh, of what's happening is the thing that builds trust and, and at the same time, controls and visibility and analytics and insights.

So when you do unleash these out to end consumers, you can quickly have a feedback loop to improve, uh, those, those types of interactions and, and capabilities. Yeah, I like the way you're addressing how you're helping the customers solve their problems. You know, one of the things we're seeing in our research is just a, about nine months ago, I ran a study about, um, the per, you know, using, uh, AI and production workloads, and we found that 18% of, of the respondents indicate that they were using, uh, AI in their production workloads.

But then I reran the study, uh, nine months later, right? So now, currently, and we see that 54% of organizations are running AI in their production workloads. So it is happening, it's happening really fast.

Yeah. Um, you know, which, which is interesting to me because when I think about it, um, and, and actually from our briefing, we were talking about the impacts of using these, using tools and acceleration of, of these production workloads. What, how are customers measuring their success?

Sure. It's, it's a variety of ways. I think, um, you know, when you look at, in some of the automation categories, for example, uh, many of our customers have over the years built up bots using predictive ai, um, uh, for handling all sorts of customer support.

You know, how do I handle hundreds of thousands of customer support inquiries? Um, and, and how do I do that without hiring another 200 people, um, into a contact center, for example? So they're looking at measurements of success in terms of, um, resolving customer issues and support type areas and service type areas while maintaining high CSAP, um, that customer satisfaction.

That's absolutely critical. So, um, you know, it's one thing to just throw a generative AI bot out there, um, that's wrapping a chat, GPT, for example, and hoping for the best. It's a whole other measure of success to be able to say outta all of these types of inquiries that my business received, these that were more transactional, um, that could really be handled by a virtual agent were handled successfully, and CSAT was maintained or even increased.

So we've seen many cases where customers are having those exact same results. Um, so that's one way when they look at it. Another way is I have all of this data and, uh, I know they're in all the, the communications that I have back and forth bet oh, across all channels.

Um, how do I turn that into actual structured data so I can learn from it too, um, and then learn how I might even optimize a virtual agent, for example. So, you know, we have products like voice intelligence that, uh, that we launched that will be in general availability here, and actually in a couple weeks, um, where we have hundreds of customers automatically securely recording, transcribing, doing language analysis on all of their calls. So they're turning that into very structured data that they can then use for analysis to improve an automated process, and then know where their products might need improvements and where their competitors might get mentioned.

And so they're really getting that analysis served up on a silver platter, if you will, from previous just mounds of data, um, so that they can improve their processes, uh, in that way. So it really falls into those kind of make money side of the house and the save money side of the house both need to be secure, and we're trying to look at that, uh, communications and unlocking that data, putting enhancements on top of it with ai, using it AI as a tool so they can drive, uh, outcomes like that. Now, now that makes sense.

And, you know, it sounds from the productivity gains and the savings, but also the differentiation of making money really does kind of play into, uh, you know, the, the measurement of success. You know, uh, one of the things that I found very interesting, and you touched on it just a little bit, I want to kind of double click a little bit more on it, is what AI solutions you have to offer and that help with addressing these concerns. I know we had a lot of discussion last week, and I think that that would be great for the audience to hear.

Yeah, Sure. So, you know, starting in things that we've been doing for, for quite some time on, on the segment side of things is really analyzing all of this customer behavior and predicting who's going to churn, who has propensity to do this activity? You know, who are my highest LTB customers in these types of campaigns across all these channels that I sent might, you know, might drive more ROI for my business.

So those are the types of things that are, you know, typically in these type of CDP customer data platform offerings that, you know, we've been confident segment's been, you know, the best in the market at that. Then you look at the other side on the communication side of the business, um, offerings in, in messaging or for things to just simply reducing fraud messaging is one that's, uh, fraught with some of those issues and, and pumping, uh, SMS volume and things like that that our customers don't want to have to pay for and shouldn't have to pay for. So we've got AI in those areas to help you also be compliant in your messaging, uh, but also introduce, uh, cost savings there, like I said, in voice intelligence launching things, um, with, uh, with speech recognition and language understanding so you can really turn all these ephemeral calls and, uh, that you might have, um, with all of your customers, usually at high lifetime value and high customer acquisition cost moments that, uh, then you can turn that into data, um, for insights.

Then the other things that we're doing on, uh, virtual agent side with are both with partnerships with, uh, with open ai, uh, with Google, um, as well in the virtual agent category and language understanding and really conversational understanding that we have as well. Um, out there, the umbrella really is what we call customer ai, and that's all of these insights activated across all of your channels to be able to be much more effective. You're not just sending more messages or making more calls or taking more calls, you're being a lot smarter about each one of them because we look at this future as, as not just more, um, it's more effective, um, and, and those things that can delight end consumers and drive repeat business and things like that.

So those are the offerings that, that we're bringing, um, under that umbrella and to, again, make them much more tangible and real. So you can bring in the three horizons out really cool technology into the here and now. Uh, and we're also launching those types of things.

We've launched an AI assistant builder within our Alpha team as well. Uh, so you can extremely quickly get something up and running and bring in Vector database and bring in all sorts of other assets to make that agent faster and smarter. Um, so we're, we're putting things out in the market that are more, uh, I can drive ROI today as well as things to show what's to come, uh, o over the years.

Well, very, very cool. But, you know, I, one of the things I, we can touch on real quick here is, and you, and you kind of, uh, DoubleClick down a little bit already, but like, there's a lot of data out there, right? And Twilio has data.

Yeah. Customers have a ton of data, right? Um, but, you know, generic AI produces generic results, right?

And with these large Yep. Right. Language models, and whether it's private in instantiations or public, you know.

Yep. How are you able to leverage all the data to basically create effective outcomes and decisions for your customers? Yeah.

That, that's key. I think, you know, as we've seen all the generative ai, uh, pop up and yeah, these, these interesting, these big, big language models are really good. Uh, but when you say, as a business, I need that to work for my customer segments with these problems, with these products or services, that's what I really need to solve here today.

So in many of these cases, they can also be, uh, small language models and, and smaller models that are more discreet and highly, highly trained. The missing piece though, is that personalized data of the customer and the consumer that you're interacting with to actually resolve issues in a call or in a virtual agent chat, um, that can also connect into systems to reschedule, reship, um, you know, an application that can actually produce an end result of a task that an end consumer wants to accomplish. That's why they contact you in the first place.

Um, and the personalized data is key. So, uh, you know, we were fortunate to acquire Segment a few years ago at that, that leading customer data platform, and very publicly we've talked about this merge of data and communications. And so what we see is high volumes of customers that have been using Segment for many years for their customer data platform to activate it and bringing in data across every channel from Facebook acquisition campaigns to website, mobile app behavior, to support requests to, um, to any, any number of, uh, service CRMs and everything else, data warehouses, um, and everything else to be able to activate that data and then really baking that into communications use cases that our 300,000 customers, uh, over the years already do, and make that connection really seamless for them to activate it versus having to do development work.

So that is very, very top of mind for us to bring these things together all the way from, uh, you know, interoperability with, uh, we could call it legacy data warehouses to zero copy, um, for security reasons for large banks and financial institutions and healthcare to activate that and then to make it seamless with the communications. So that's really, uh, the, the unlock that we see is just AI alone and or communications alone don't really hit that mark. Uh, all of us selfishly as consumers want highly personalized experiences.

And so that's what we really see as the, the value add is bringing those key pieces together as the new models will come and go. Um, we want those, uh, those foundational elements to be active for our customers. Yeah, it makes a lot of sense.

Um, where, where to what, how would you advise the audience to get started? Uh, sure. com and looking what we have, we are very, um, you know, like I said, we've got over 300,000 active customers, 10 million developers.

We've, we've really put, um, the emphasis on the ability to get started and see that magic moment, that aha moment, um, with Twilio, with just quickly and easily signing up and connecting these things and getting a proof of concept out in minutes rather than days and weeks. And so, you know, coming into Twilio, you as a, as a customer, um, can automatically quickly try things, um, and then do a paper use model and it's usage based so you can get started and ease into it. Um, and then quickly scale.

So, you know, we look at, you know, we're the partner that you can scale globally with as well, not just easily get started. So that's where I'd encourage you to go and you can learn a lot more about what we're doing, the offerings that we have, uh, both live right now and things that are coming and, uh, you know, you'll see more and more from us, um, over the coming months, uh, in, in this space. Great.

Thank you, Andy. Hey, I wanna thank you for your time today. I wanna thank you for your, your perspective.

And it, I know it's just the tip of the iceberg here. Lots to think about, lots to consider for your organizations. I also wanna thank the audience for attending our session today.

It's been a, a, there's a lot of data here, a lot of information, and a lot to consider. com for additional information. Thank you very much, and have a great day.