Twilio Inc. (TWLO) Earnings Call Transcript & Summary

All right. Hello, everybody, and welcome. I am Kailey Raymond, and I lead Campaigns Marketing and ABM here at Twilio Segment. I'm really excited to be joined by Jacqueline Woods, the CMO of Teradata; and David Chan, Managing Director at Deloitte Digital, for a little fireside chat today. Jacqueline is an executive with over 30 years of experience leading marketing efforts at Fortune 100 companies, including IBM, GE, Oracle and Verizon. And David has spent his career partnering with clients to digitally transform their organizations by enabling key CX capabilities to creatively solve complex business problems. Welcome, Jacqueline and David. Thank you so much for being here.

Thank you for having us.

Thank you for having us.

I would love for you all just to start off by introducing yourselves, your role and how it impacts the customer experience and journey. Jacqueline, why don't we start with you?

Sure. I am the Chief Marketing Officer at Teradata, which means that I lead really all aspects of marketing, which includes even our martech stack and our digital -- everything that we do digitally as well as comms. For me, personally, I started my career in finance. And so when I think about the journey to becoming a CMO, it really is kind of rooted foundationally with the numbers and analytics. And so today, I'm more focused on not just storytelling, but really focused on how do we bring what people are looking to do to life through stories. And as you can imagine, everyone is talking about AI and analytics. It is today's zeitgeist or what is the lexicon in messages of today. It is what our technology does, so I'm really having a great time of really sharing those stories about what we do in that area.

We've got a storyteller amongst us. David, tell us about yourself.

I was going to say Jacqueline's background probably gives a lot of people in finance like a great opportunity to take on a CMO role at some point. That's pretty awesome if you think about the background. Yes. So like my background or actually my current role is I am a Practice Lead within Deloitte Digital, focused on customer data platform, CDPs, and it's really about how to create more hyper-personalized experiences, engagement, leveraging that intersection of the robust amount of data signals that now exist. How do you use insights and analytics to then drive and deliver experiences using that connected martech ecosystem? But what's interesting is when I first started out my career, I actually started in industry and then moved into a consulting role. And through that, it was really about just creating experiences. Before omnichannels, just cross channel and cross channel just meant you had a brand experience sitting somewhere in the channel. It wasn't even consistent, right? An omnichannel was actually combining and making it consistent. And back then it was just a website and a mobile -- like a web -- mobile-optimized version of the site before even a native app. And during that time, I don't think I really thought about the measurement side. It was just building the experience. But now, to sort of Jacqueline's point around the data, the data in itself is good but then how do you measure it and understand what the data is telling you in order to drive your businesses forward and make good decisions? I think that's really the next step.

That's great. We have 2 folks that are data experts here, so very excited to learn from you and really excited for everybody in the chat right now. Feel free to introduce yourselves, all the people from around the world, go and tell us where you're from.

Okay. So to the topic at hand, we're talking about the realm of customer engagement. We know it's perpetually evolving and it's really hard to stay abreast of all these constant developments. So this is why we have these 2 experts here in the room. They're going to help us decode today's top CX trends and share the predictions that they have for the year ahead. So Jacqueline and David have already mentioned the 900-pound gorilla in every single room right now, which is AI. And for those keeping tabs, yes, we'll be counting the number of times we say AI today, and one of you is going to walk away with a little gift for guessing that volume. So should be fun engaging in conversation today. To kick us off, I'm going to take some trends from our recent growth report that we released, which was saying that half the businesses that we surveyed were expecting to spend more time and money, I would argue it's probably more than half at this point, on AI-driven campaigns in the next 12 months. Not so surprised. But David, I'm wondering, from your perspective, I know Deloitte has been deepening your investments in AI. And I'm wondering what that interest and demand has been like from your clients. And in particular, are there any use cases that folks are looking to solve for?

Yes. I think AI is a very sort of broad umbrella. And, in general, if you saw what happened in the last year, there's huge interest, right? There's a huge demand and interest around gen AI as well as AI. And I think the biggest challenge with most companies is trying to understand where to even start. Do I buy something off the shelf? Do I build my own? You look at a lot of companies today. They're all -- especially the agencies, they're all building and releasing their own AI initiatives with some sort of custom-built core AI platform that they're running all their marketing plays off of. And then so you have these sort of now potentially black-box solutions, but they're supposed to be their secret sauce of what they're going to provide to you to differentiate. At the same time, you have companies who also understand that they want to own that capability as well. And so there's this balance of what do I buy that's differentiated, I cannot do myself, but also things that I need to get support from and build like custom, to give me this like custom share of wallet with my customers. Now based off of all the different options and possibilities a client can go towards, I think they struggle with knowing where to start, like I mentioned. And some -- what I've seen is a lot of our clients either go with, hey, we're going to do some -- run a few pilots, right? Low risk, low investment, or they say they take more of a gated waterfall approach of saying, "No, I'm going to invest in a very, very comprehensive holistic AI strategy before I take the next step because it's so critical, but -- and I accept the fact that I might not do anything for a year." I don't think there's a right or wrong way. I mean, I'd love to get Jacqueline's thoughts on what she thinks. But I think, to each their own, right? Some just have higher appetite and desire to go faster than others. And when I think -- and it's not so black and white that there's going to be laggards and leaders. I think there's also these people who -- this is where I'd play. I play in the fast follower copycat, see what's working and adopt that versus being on either end of the spectrum.

I like that. That's really smart, which is going to lead into the question that I want to ask you, Jacqueline. And as we all know, I mean, what Dave was talking about is relying on this really solid foundation of data. And so it makes sense to me that you're saying that some people want to wait a year because maybe they need to make sure that their data is in a standardized position to be able to actually get the right AI outcomes, I don't know. In our reporting, we found that 71% of people were saying that they felt like AI would be more useful if they had access to higher-quality data. And, Jacqueline, I know you had something similar to come out with Forbes recently saying that only 24% of companies feel like they have the right data to get started with AI and make those informed decisions. So how and where should enterprises invest in AI for that maximum impact?

Well, to David's point, and I want to pick up on what he said, is there the right way to do this? I think we're in unchartered territory and I think everyone knows that. But I do think that there certainly are things that we can learn from the past with other technologies that were launched. Everyone knows that the iPod certainly was not the first MP3 player, right? And so there was an appetite to kind of have 1,000 songs in your pocket, so to speak. But the ability to do that and the platform to do it on actually didn't come out when the initial MP3 players came out. And we probably can't even remember the names of those companies that have those initial MP3 players, but everyone knows what an iPod is. And so to David's point of fast followership, often not a bad idea for things that you don't have a solid idea and a plan on how to do it. What I would say with data and the way that we think about data is data is everything. It is the holy grail. When you think about how much data there is in the world and how much data is being produced every minute, every second, and the number of zettabytes that everyone talks about like in the next year or 2, the replication of all that, like most of it is replicated, so you just keep having things kind of replicate. It doesn't mean that everything is original data. And all of that means that you actually don't have clean and pristine data. And I often talk about data is not like oil. To me, it's more like water because when you think about the Earth, the Earth is really about 70% water, but only about less than 5/10 of I think 1% of it is actually like usable. So you have a lot of water that's not usable. You have a lot of things in data today that aren't usable. Now in order for AI to be really impactful in your organization, it has to start with data and do you have clean data. Is that data pristine? Do you know the lineage of the data? Because AI is nothing if it doesn't have clean data to essentially build intelligence off of, particularly when you talk about generative AI. When you talk about machine learning, it is learning from patterns that are already in the data. So if you start with something not good and unclean, what's going to come out on the other end is also going to not be clean. And so when you think about how can an organization really use that data, you have to start with is my data clean, do I know the lineage? Do I have all of the things that I need in order to make sure that, that happens? Obviously, those are things that we do at Teradata, but it's less about Teradata and kind of what we do. These are things that everyone who is thinking about artificial intelligence, that is where you must start. And if you don't start there, you -- I think you're setting yourself up to not be successful. And I do think that there is a fair amount of uncertainty as we kind of enter this inflection point that clearly we are in, but I think there's also a tremendous amount of opportunity as well. So for me, it's something that excites me. But we all have to be responsible with how we're going to be using this technology for the good, I think, of everyone.

And we'll, I'm sure, talk about that as well. That tethers on kind of like trust as a narrative. And by the way, I think it was a Zune, I'm pretty sure I had one of those first MP3 players that wasn't an iPod, if I'm recalling that correctly?

I have a friend who's an early adopter and he like always has all the gadgets and he had the thing before the thing. And even when he had it, I was like, what are you doing? Like where are you getting your music from? And it was -- it really wasn't until Apple came out with their platform to download music and you all remember when it was like $0.99, which feels like honestly, it was more than -- it was 20 years ago. Like it feels like it was yesterday, but it was a while ago. But when that happened, we were all excited to carry music in our pockets.

I remember burning songs. Absolutely.

I do, too.

I still remember the CD players with the anti-skip.

Yes. I carried that around. It didn't work out well, though. It still skipped.

Yes. Well, yes, the disk is going to be [indiscernible] no matter what. Going back to AI. Jacqueline, I love this idea of data being like water. I haven't heard that one. I think it's a really fresh take.

I want to borrow that, Jacqueline.

It is my original thought, so just give me credit. It really is like water when you think about it. There is a lot of it. And honestly, it's not usable. So I have thought about this a lot just conceptually. And also, as you know, I always say I do love telling stories, and I like to think of things that kind of take different concepts and just make them really very visual in people's minds. I have found that, that one has resonated really well.

It stuck with me. I like it. [indiscernible] say it so don't worry. Your stamp is going to be on there. I want to get us to the kind of predictions for AI. I feel like you 2 are really kind of in the know and have your pulse on this. And one of the things that I was reading recently and I'd love your take on it as well is that Gartner came out with a new prediction that was saying that we're thinking that organic search traffic will decrease by 50% by 2028 as consumers start to adopt gen AI search, which I think is interesting. I'm not quite sure if I'm on the believer side of that or not. But this one is for both of you. Any predictions that you have for AI and how it's going to enhance customer engagement in the future?

David, do you want to go first or do you want me to take it? Either way.

Yes. Yes, I can go first. So put me in the camp of selling and not buying the fact that gen AI-driven organic searches -- sorry, gen AI-driven search is going to sort of take over the world. This was actually a very early one. I think when gen AI first came out, someone proposed this, and you see a lot of people writing articles talking about how well gen AI can spit out the information from web scraping, then there's no reason for them to actually click through, right? Now as someone who's at a very young age been told they're really good at searching the Internet and had people very young in my career say, "David, how did you find this out? You're so good at it." And I get -- got thinking that I'm a good Internet search person. You know that when you do search, if you're searching for something very, very noncritical, nonmission-critical like what time does the store close? Yes, you might accept like the first search result that you even see today, there's some Gen AI-based search results that are coming up. But anyone who knows when they're trying to do real proper research, they probably click through at least like 10 different links. Some of them are just copy and paste and paraphrased version of it probably just to drive some revenue to just land on it by leveraging some of the person's comments. So that content isn't always accurate. And so you kind of have the due diligence. You have to flip through like, I don't think anyone wants to like on the second page of search results link. But my point is, when you do that, you get a sense of, okay, this authoritative place said this about this thing. You're now trying to triangulate on it. With the gen AI search results, it just summarizes. It doesn't cite references. It doesn't do anything. And even if it did, the amalgamation of it, you would still put into question. So unless search fundamentally changes, right, in the next 4 years, then -- and search -- gen AI-based search results reflect that transformation, that gen AI search on its own is not -- in its current state, is not going to drive 50% to gen AI search results. So that's sort of my statement.

I think the content is the thing you have to worry about more with gen AI is like how many aggregated gen AI articles are going to be created by all these firms that are then going to be served to the top of your search. That's something more realistic [indiscernible].

I tend to agree with David. First of all, even when you think about AI and machine learning and anything in that realm, it's all going to boil down to critical thinking. And I think that skills of critical thinking are going to be even more important than they were 2 years ago, 4 years ago or 10 years ago because you will be required to really think through in a really methodical way where not only is where the information is coming from, but how is it relevant and contextual to what you're trying to do. I was talking to a friend the other day because you can't see it now, but like to this side of me, I have these like encyclopedias and we were making a joke about it because, obviously, no one has encyclopedias anymore. But we've had them in this office for like, obviously, more than 20 years. And I started thinking about when I was a kid, how I looked at things in an encyclopedia. So to David's point, having to do research, having to kind of go in and kind of look at all the things that you wanted on a particular topic. And what we were talking about was with Google and search that imagine -- I can't imagine kind of looking up turtles, something that I was doing when I was in fourth or fifth grade when we were talking about reptiles, and all of the things that are at my fingertips today versus what was just in that one encyclopedia when you looked up reptiles. That said, the one thing that the encyclopedia has is the source and citation for all of that data and where it's coming from. It's just not copied from something to something else where you actually can't trace back to what happened.

We're going to touch on this a little bit later, I think when we're talking about kind of trust, which -- who is the arbiter of truth here? Right now, I think it's probably newly appointed tech god, Sam Altman, but we'll see where that ends up at the end of the day. I wanted to see before we end our discussion on AI for now, any other predictions related to CX and how AI is going to play a part of that in the future?

I think that personalization is the -- it is the thing that marketers want to do best. It's the thing that we wake up every day and want to do better. It's the thing that we know drives stickiness and engagement. And to the extent that we can leverage these learnings to do that, I think it is -- we need to be all in. I think it makes our companies better companies. I think the -- what we deliver to end users, whether that be a company or whether that be a person is going to be really important. And those platforms and technologies that enable us to do that, I am all in 100%.

How about David?

So I think for going into 2024, though, obviously, Deloitte actually produces some research studies. Everyone is doing it around AI and gen AI. And one of the things that we're seeing is there is adoption of some, like, simpler use cases to pull off right now with like copywriting, image generation, content production, right, all rounded up into content production. But the other wrinkle is what channel are these content pieces being produced? And what's the risk -- brand risk associated with getting it the fidelity level, perfect versus not? And so obviously, own channels like e-mail, direct mail, SMS, what have you, higher level of quality of content required. But I think you're going to see that going into this year where you're thinking about paid media channels, where maybe there's already an acceptance of like a lot of DCO solutions, the dynamic construction of some of the final output doesn't even -- isn't that great to begin with. Gen AI is going to see -- going to add more quality to what's there already, in my opinion, and that's where the adoption is going to grow in for this coming year.

I am on the same page with you, and to Jacqueline's point around personalization, I think that's a perfect segue kind of into our next trend, which I wanted to touch on, which is omnichannel real-time personalization, which is something that I think has been a driving force for a really long time. But with technology, we're getting even closer to being able to actually achieve this, being able to kind of create all of those personalized images and content pieces for every account or individual gen AI is actually making that possible. We're also seeing, to your point, a change in kind of behavior as it relates to this proliferation of digital channels. The number of touch points has just only been increasing. Our reporting is saying that it's tripled in the last 15 years from 2 to 6. Frankly, that seems low to me. I feel like it's probably even higher than 6, especially for different industries. We also know everything kind of relates back to timing when we're talking about customer engagement. So the importance of real-time, especially in certain industries may important than others. So David, do you have any insights or examples you might want to share for how your clients are approaching that need for omnichannel, real-time personalized communications?

Yes. I would say, a couple of years ago, coming up with an omnichannel strategy was pretty much, I think, everyone asked for. Now that we've sort of accelerated our adoption of digital, I feel like I see less of hey, can you help me out with an omnichannel strategy? Because I think most companies have already either spent the time to create one or -- and they're actively executing it or they know enough or have the maturity to actually not need a partner like Deloitte to go and help them with their omnichannel strategy, right, which like I said, has changed to before. Just having a brand like flag in the channels versus now creating that omnichannel experience that's consistent, that's connected across the journey. So I treat that as table stakes. Now the real-time one is very interesting because, in my world, everyone wants real-time personalization. And what that means is the data has to be real-time collected. Data has to be real-time processed. Data has to be real-time curated to be made of some sort of business sense to then activated on in real time. But what I've also seen is that most companies just don't need it. When we actually walk through their use cases, none of that or only parts of that have to be real-time. And if only parts are real-time, it's not real-time. It's sort of a batch-based delayed response, which is acceptable. And to me, what matters more is less about whether it's real-time because just faster is not always better. It's about how contextually relevant the message is being returned to the customer from the brand. That is more meaningful, right? And so for me, my advice to a lot of companies out there that are so focused on real-time, you have to understand there's a penalty to pay for not only implementing the systems and technologies to do it, but also the support and the licenses and the operational costs, when in reality, you probably don't need it if you really ask yourselves those hard questions and talk through it with your teams. And so really think about whether you need real-time or not and invest in those areas where it's required and don't in those others.

That's great. And it kind of touches a little bit on personalization when you're talking about the context is key. It's kind of like riding the line between personalized and creepy if you're just like immediately sending an e-mail to somebody based off an interaction that they had, depending on, I think, the industry is really dependent on this as well like B2B probably doesn't need to be as real-time as maybe retail. If somebody buys your shoes, maybe you want to suppress ads for that shoe from that person so you can save that dollar immediately. So I think there's some interesting -- David, I'm wondering, do you have any thoughts on what might be next in this realm of real-time personalization?

So, I mean, I think the logical extension, Kailey, of what Jacqueline was talking about and how do you trade that personalized contextually relevant experience is that most people think, "All right, I've got something that caught the signal. We've now had the information to build that audience, they're part of it. Okay, I'm going to now just deliver the experience." But the missing piece is the content and not to harp back on the content, I always joke to people who go, "Yes, we want to do one-to-one people-based marketing," right? You heard that. And I said, okay, that's cool. But do you have a piece of content one-to-one, do you have that content supply chain set up to scale and deliver a piece of content to every person? And I'm talking about companies who have millions of people. Now multiply that by your 2 to 6 expansion of channels, which has a slightly different rendition and format, right? So how are you going to achieve that, which is why I think from a content perspective, I know Jacqueline earlier said, a, is data the new fuel now, I think about it as water? Well, if you roll that back and still think about data as a fuel, content is almost like an alternative fuel or another fuel that doesn't allow you to deliver great CX unless you have that as well, right, another element, another dimension. And that's where I think that some of the studies that Deloitte has had is that there's actually a 54% increase in the amount of content that's required by most marketers today compared to the previous year. And what's that driven? I think it's all the stuff that we talked about. It's the explosion of channels. It's the desire to be more personable, more relevant. And so because of that, I think that gen AI will probably play a pretty big role in that space because it can do -- it can pull off that use case. It's much closer to pulling off that use case versus all the other interesting and exciting and emerging use cases that are out there, which they will, at some point, but these are very ready and baked in my opinion.

Yes, I do think if you -- just kind of expanding on your example, I do think if you go to Kailey's, there's 6 channels minimum, maybe there's 8, but at minimally 6, and you do your "one piece" of content that's e-mail, right? And then you say, how does this get served up in LinkedIn, TikTok, Instagram? How does it get served up in text or blah, blah, blah, like all the places that it could show up? I think that gen AI can -- would be the a great place to start to be able to do that because it knows what the character requirements and -- or limitations thereof for Twitter. It knows how the content differs between Instagram and TikTok. It knows when it -- Twitter has -- sorry, LinkedIn has to look like this in order for people to engage and to capture attention. And I think that is an opportune place, honestly, for AI to help because those are things that kind of whether it's format or trends or other things, you could take one thing and then say, "This needs to be in all these places. Give me an example of what that could look like." And I think that, that would be awesome. And what that does, it doesn't take anything away from the person that's developing or creating original content. It just helps them quickly propagate that content across all the channels.

That's really smart, and it's a really nice transition to the last topic trend that I want to speak to you all about, which is trust and privacy. So we've been talking about some of these one-to-one, people-based. You need all the content, 54% increase in content, by the way, David, is just astounding and feels like it will resonate with my team as well. So I'll bring that back and I'm sure I'll make them feel very hard. So thank you for that. But consumers are less comfortable with using their personal data for personalization purposes. We're seeing that in our State of Personalization Report. They're not quite sure how businesses are going to leverage that. So Jacqueline, I'm wondering if you can share your thoughts on how to best address some of these privacy concerns coming from consumers, while still reaping those benefits of personalization that we can talk about.

Thank you. Two things. I think people opt-in to the personalization, I don't want to call it platforms, but things that they actually care about, that they feel that they get value from. So in other words, what we see is that, from a personalization perspective, if people believe that the company product or service that they are opting-in to provide personal information is creating value for them, they are willing to give pretty much all the information that is required in order for them to optimize that experience. And so what I think companies really need to do is understand what do they need the data for and how they're going to use it and how they're going to use it primarily to create value not for themselves, not monetizing the thing themselves, but how does that information help them deliver a better experience and create value for that end user. When that happens, people actually do want to opt-in. So imagine if it was Weight Watchers as an example. I mean, you're kind of giving everything, your age, your height, your weight, even your ethnicity because those can be factors for other things. Do you have hypertension or not? Do you have -- are you diabetic or not? You're kind of giving a lot of information to kind of an entity that doesn't necessarily know you. However, the belief that you are going to be on a journey to better health is wealth is worth it to you. And therefore, you know that if you do not give all the information, you're not going to get the optimal output from whatever the platform is. And those are the times that I think personalization can be really helpful. So even if you translate what I just said to a company and someone said, "I need to have these kinds of insights for this reason to help me create more value for my own company," then you are going to be willing to kind of figure out what data that you need across your enterprise to make that happen. And if everyone understands the value that they're going to get out of that on the other side, then they're going to be more willing to share that data. If there's no value creation, then it's not worth sharing the data. You don't need to know everything about me. You don't need to know I was born in December, just in case, Kailey, you wanted to send me a present or a Christmas card. You don't need to know that, right? But if there's something valuable to it where we're exchanging value, then I am more willing to share that information. That's across the board on B2B or B2C.

The Weight Watchers example is so on the money. I love that example of value exchange. It makes perfect sense to me. And I wanted to talk about the other side of this a little bit, which is -- well, it kind of touches both. But like acquisition, I think related to trust and privacy. Right now, we know the world is going cookie-less. It's on the top of every marketer's minds. It's related to how consumers are calling for more control of their customer data and they're placing top priority on identity data. So, David, I'm wondering, how are you advising companies as they're managing this cookie transition this year and even more broadly, how they're managing consensus data? Do you have the biggest piece of advice for folks?

Yes. I would say that a lot of people within the market or what I see is they immediately gravitate toward first-party data solutions, right, as alternatives. Well, I need a more durable identifier. I need to get right with my first-party data. And so they go and try to build data lakes, data warehouses, customer data platforms and such. And so that's sort of like where they immediately gravitate towards. What I tell people, though, you have to understand that when third-party cookies first was initiated, like within sort of made in existence, it's a little bit of the Wild, Wild West. I mean, people were using third-party cookies almost in a way in which it wasn't intended. And what they did was they did things that consumers didn't realize that, hey, you could track a user cross-site using this third-party cookie that followed you around, right? So just so it's clear, third-party cookies are being deprecated. First-party cookies like the one you go on your banking website and it prefills your sort of user name and whatnot or a token, that's still sticking around, right? And I think that when you want to first start understanding how you want to go about third-party cookies, there's sort of like 3 types of people in this world. There are people within organizations who think, "You know what, I am ready. Like I've been planning for this," because they're a planner, they're getting ready and we'll see what happens, right? And I think because Google has started to make the changes to Chrome in sort of like a slow rollout fashion, we'll see -- they'll realize whether what they had -- all this planning had done was right or wrong. Then you have the group who says, "I'm not ready but I don't care because I'm not really convinced that this is going to have the impact that I think it will on my business." They're not getting asked by their CFO or their CEO on this topic. And they're like, "We'll see what happens." And then the last bucket are just people who have no clue, right? They've been living under a shell or a rock. They have no idea that third-party cookies or what their impact is. And so my recommendation is there are actually a lot of companies who have come up with some sort of way to help you measure the impact of cookie laws and signal laws on your business as a whole. And they vary in degree. So like Deloitte, we offer this cookie calculator, which is almost like one of those simple surveys, which you can just kind of fill out and say, okay, if I spend this much media, if I run these number of campaigns, this is the potential impact to my business if I do not address, right? Other companies will have -- will offer services where they will actually do a deep assessment, right, or a combination of both. Either way, I think it's first measuring how much does your business actually rely on third-party cookies and then figure out from there what's the right level of investment into some set or suite of first-party data services that can combat it. And it won't be just that. DMPs are going away but you're going to have to leverage first-party data platforms. You're going to have to leverage data clean rooms. You will also have to leverage maybe third-party services to support the gap that will exist once third-party cookies are fully duplicated.

It's a challenging world to navigate in [ 2024 ]. Honestly, Google's been telling us about this for how long now, David? I feel like [indiscernible] spent plenty of time to prepare. And now people are maybe finally waking up and they're in that category of, oh, I need to take action.

Yes. That's exactly what that feels.

I have 2 more questions. Yes. I have 2 more questions for us and we're around about today. Are there any predictions or trends we haven't discussed today? So, so far, the 3 that we've talked about, AI, omnichannel in real-time and trust and privacy as some of the biggest things we're talking about in CX. Any other ones that you both wanted to throw into the room but you're thinking about?

I do want to pile on with we all know that AI, it has the potential and likelihood to change everything about how we think, about how we do work and get work done and how we interact. What I want to throw in is that the importance of trust in the people, so when people say responsible AI, in some ways, I cringe because I do not think technology can be responsible. Responsibility or something being responsible is generally an attribute that you put on people and humans. And humans can be responsible because we have a consciousness that makes us believe that we should do something and that we have ownership to be accountable and have accountability. And so I think that the trend will be really doubling down on making people responsible as we move forward in this world of AI and that what you will be able to build trust in is either the data that you are -- that you've cleaned and that you can serve up and certainly, those are things that Teradata does. Not to put it in a plug for Teradata but I noticed that David was putting in plugs for Deloitte, so I feel guilty. I feel guilty that I haven't plugged as much as maybe I should. But I feel very strongly that what we'll need to really do is be able to trust data and trust the output and that we need to have transparency and that the responsibility squarely falls on us as humans to each other. And I think that will be a trend that comes out and the approach around the ethics around AI will become extraordinarily important over the next year and in the years to come.

Beautifully said. I will plus one all of that and I'm sure you'll get a lot of hearts in the chat for that one, too. David?

Jacqueline being my gen AI prompter [indiscernible] creativity. I think what you said and going all the way back up to our initial conversation where we started talking about good data and from search like the Oracle, of like governance around data. I think what's going to happen in this coming year is this idea that people are going to realize that the gen AI solutions that are based on public data sets, right, those LLM-based public data sets will probably realize that there's not a good solve. I mean, there's this concept of AI cannibalism. One of my coworkers calls it -- told me that it's called AI eating itself where the LLM is starting to consume data that was generated from an AI, thinking that it's truth and it's creating this like circular loop where it's almost collapsing on itself, thinking black holes or something, I don't know, or like a dwarf star, I don't know, it collapses on itself. But point is because of that, there might be a pivot to trying to understand, okay, the public LLM-based solutions might be too hard to pull off. Maybe we got to focus on the private ones, where there's governance around the data and maybe there will be third-party data providers who are going to say, "Well, I have the clean data water for you. And I will supply you my clean data sets that I've refined, not oil but water, purified, for your LLMs to drink in a private setting." So that's one that Jacqueline, you kind of made me think about. The second one is I really think that the last couple of years, everyone has been focused on data platforms to try to figure out how do I turn data swamp, data lakes -- from data swamps to data lakes and create some order to the chaos of data? Great. But now the next step is, okay, I know how to move data from A to B, but that in itself does not make it usable, right? The data is still just like taking your analogy, Jacqueline, of the 70%, we just moved it from one area to the other but it's not -- still not usable. And I think to make it usable, besides ETL and transformations, all that, I think it's actually identity resolution. It's actually some people -- for customers, it will be identity resolutions. For non-customer domain, it's going to be entity resolution. But it's this idea that the data now has to make sense and be tied to make it usable. And so if you don't do that, then your LLMs, your personalization campaigns, your experimentation, your measurement frameworks that you're now trying to unify, those reports will mean nothing, right? And I think this coming year will be the year when a lot of people have invested in these data consolidation projects, now they're going to realize that unless they figure out this identity resolution, they might not be able to take it forward.

Beautiful. I love that one. And to your first one, I feel like the media industry, the publishers just got a whole new business model, like imagine New York Times sells OpenAI because they can actually trust the information that's coming from the room. A lot of [indiscernible] for the next year.

Yes. I'm pretty sure that wasn't an original idea for me, but thank you, or you're welcome, rather.

And I actually think that is coming up on an interview that I think I had read about or seen with Satya Nadella from Microsoft because I do think, to your point, they're already trying to -- some of the LLMs were kind of bringing in data from The New York Times, but I think they have not paid for it. And I do think that, that's wrong because I do think that is IP that someone owns, and the owner is The New York Times and the people that wrote those articles. So it's not like this is just in the public square and it's free, the same -- and so that, I don't agree with that, that should be free and expect to have to pay for that.

I think it's just a sure fun thought where we originally might have thought that gen AI would kind of eat journalism as an industry, but in fact, maybe it's actually paying their bills.

It is a flip in the story. You got to get all that information from somewhere and you can't make it up. And David already talked about the hallucinations that occur when AI is getting information from AI and then pretending that's AI and generating more generative AI.

Totally. Totally. Okay. You all have been extremely generous with your time. I have one last question for you, which is what steps or recommendations would you both have for somebody to stay ahead of the curve as it relates to customer engagement? Jacqueline, we're going to start with you.

I think you have to start small and then scale. Like I think you need to start with something that you can develop a use case for, pilot that. If it's successful, scale it out. If it's not successful, make sure that you obviously tweak and course-correct until you get it to where you want it to be and then scale. I think that's going to be important. To David's point at the top of this call, it's -- you just can't just go all in without a plan in a prescriptive way to actually go down this path. This is a marathon. This will not be a sprint. And I think those people who train well and do all the things to be well-trained, I think they will be the winners of this race.

Train well, everybody. David?

Yes. No, I like what Jacqueline said. I think it's a combination of when you do your pilots and your POCs, it's to prove a point, but once the point is proven, then you have to come up with a plan, right? That's actually holistic it may be waterfall. I like that very much. So thank you, Jacqueline, again for being my gen AI for this conversation.

This is a match made in heaven. There's a lot of ideation that's happening today. This is great. Well, Jacqueline and David, thank you so much for being here. We so appreciate you sharing all of your insights with us. I hope everybody on the call found it valuable, and thanks again.

Thank you.

Thank you.