LaunchPod AI - Zuora === Shakir: ~product owners and managers are spending a lot of time researching. So extracting. Community ideas, our partner sentiment, our analyst reports our competitive intel, and extracting all that data. We used to manually identify themes which would drive priorities.~ ~And now tools like Gemini and OpenAI and CLO are allowing us. To use them to extract the themes and that research is now taking minutes instead of days. So that's just not efficiency. That's literally velocity.~ Jeff: ~Welcome to Launch Pod ai, the show from Log Rocket, where we sit down with top product and digital leaders to talk real practical ways. They're using AI on their teams to move faster and be smarter. Today we're talking with Shaki Kareem, senior VP of Product and Kartik Chak, Ani, CIO, and Senior VP of Corporate Operations from Zuora.~ ~In this episode, Kartik and Shaki share why they believe AI is a bigger inflection point than web, mobile, or even cloud. Their internal AI agent workflow that automatically resolves thousands of service requests instantly freeing teams to focus on strategy, how they cut a major feature deployment step from 14 days to 10 minutes using cloud code and why prompt aons and hackathons have been vital for driving not just AI adoption, but AI literacy.~ ~So here's our episode with Kartik Chak Ani and Shaki Kareem from Zuora.~[00:00:00] Shakir: Product owners and managers are spending a lot of time researching, so extracting community ideas. Our partner sentiment, our analyst reports our competitive intel and extracting all that data. We used to manually identify themes which would drive priorities, and now tools like Gemini and Open AI and CLO are allowing us. To use them to extract the themes and if that research is now taking minutes instead of days. So that's just not efficiency. That's literally velocity. Welcome to Launch Pod ai, the show from Log Rocket where we sit down with top product and digital leaders to talk real practical ways. They're using AI on their teams to move faster and be. Today we're talking with Shaki Kareem, senior VP of Product and Kartik, Chuck Ani, CIO, and Senior VP of Corporate Operations from Zuora. In this episode, Kartik and Shaki share why they believe AI is a bigger inflection point than web, mobile, or even cloud. Their internal AI agent [00:01:00] workflow that automatically resolves thousands of service requests instantly. Freeing teams to focus on strategy, how they cut a major feature deployment step from 14 days to 10 minutes using cloud code and why prompt aons and hackathons have been vital for driving not just AI adoption, but AI literacy. So here's our episode with Kartik Chak Ani and Shakira Kareem from Zu. Jeff: All right. This is a special one ' , I think this is the first time we've had two people. So, Shaki Karthik, welcome to the show. I'll be honest, we had Ken Houseman on from, , the product team a little while back and afterwards Ken and I ended up talking a little bit more and going through what. You guys are doing over at Zuora with ai and immediately we landed on, I gotta have you both on to talk about this. So I'm so happy this happened and thanks for making the time to come on and chat about how Zuora is looking to 10 x efficacy and 10 x innovation from ai. Welcome to show guys. Karthik: Thank you. Shakir: you. Yeah, Jeff: Yeah. We had Ken Houseman from, your product team on a couple weeks back, Shaki and. Afterwards, we ended up talking for longer and longer. And Zuora, we've all seen, you [00:02:00] know, the best deck ever presented is the Wara deck, right? It's like the. template of what a pitch deck should be. And you guys have been such a part of SaaS as it has grown into what it is. I was really curious to hear how you guys are using AI to kind of go into this new era and reinvent again. And this idea of 10 x in innovation kept coming up. So maybe Kartik we can start with you on, what did that mean? When you say 10 x like wheels on the ground, how are you guys looking at that? Karthik: we have couple of key objectives that we are all driving towards, and technology is a key enabler to these objectives. Number one is we want our customers to have the right expertise with the right reliable service. Number two is we are the provider of choice for the order to cash platform by CFOs in the SaaS market. Number three is we are replacing the legacy billing and recurring payment systems. At the B2C companies. And last but not the least, which directly kind of, connects to the AI is be the most efficient SaaS company in our market to enable future investment in growth in customer success. Our 10 x initiative, which is focused more on AI, is directly [00:03:00] linked to this fourth one, to be the most efficient company. And we have seen several opportunities. And in an AI native world. Technology leaders are no longer just technology enablers, ? We have to be architects that are adaptive and drive towards more intelligent enterprises, and also lead the shift from the static systems, existing SaaS landscapes. more contextual and agentic experiences that learns, personalizes and also evolves in real time. And that is a whole principle behind our 10 x innovation and transformation. Jeff: . And to be clear here, , there's two elements to it. One is, you know, a little bit of how does AI operate within the product? That is Zoar and for, you know, customer facing stuff. But also with a company that sizes zu, there's a lot of people, how do you make them 10 x more impactful? Karthik: Absolutely. We are witnessing the next wave of disruption which is accelerating super fast. Large language models like from open AI or anthropic and others are evolving rapidly and enabling more and more natural and powerful interactions. And even [00:04:00] now we are seeing the both of AI native browsers as well, right? Like which are transforming how we search. We summarize and act online, not with clicks, but more with intent and interactions. So how do we do this? How do we make the, our enterprises ready for this new engagement and interaction? In order to do that we need to be more of an AI native company. What I mean by AI native company is deploy AI broadly to enable everyone to experience this new interactions and reach their productivity potential. That is a baseline and foundational thing that we have to do. And we had been doing a lot of reskill and upskill and enablement session. To show them what's possible, what's the art of possible, how can you take an existing process and reimagine and make it more automate and smarter? Those are the things that we are actively working on. And this is a foundation in order to drive towards more of an AI native business. Once we enable the employees with AI native, then we can gradually move towards automating business processes across multiple functions. So that's a road we are taking. Jeff: . Kind of regardless of where you're looking in product or for [00:05:00] your own team, it kind of seems like what we've seen across the board talking to product leaders is it starts with if you're looking at yourself, you're gonna also be building with the tools and using them. And the more you just figure out these things, the more you're gonna. Learn how they can be extrapolated and used across in other places as well. Generally, everyone has trust with just doing it and then you get better and deploy it elsewhere and elsewhere, and it's gonna have compounding gains specifically over at, Zuora. What does that look like, like with within the company? How are you looking at automating. Things , that maybe historically before, Either you know, a weren't done because they're just too time consuming or not, you know, value to a cost ratio weren't there until you could AI it, or b maybe it was, you know, expensive or slow and you can just like turn something that was slow and slow into instantaneous. Karthik: No, definitely. Great point, ? So we don't want to take an existing process, which let's say it's a 10 step process and just make it automated, right? It's not going to move the needle a lot. So we need to really understand what the future of work needs to be, and we look at future of work in four different categories. Number one is enterprise context. Can [00:06:00] AI understand me and my work? ? Number two is enterprise search. Can AI gimme the right answer right away? Number three is agentic ai. Can AI help me with smart automation and completes the actions autonomously? And number four is continuous learning. Can AI grow smarter as we grow? So if you look at these four principles of future of work, this is something new, right? It's not like a typical SaaS transformation or cloud transformation. In order to do this, then we have to look at how do people work today. Whether it is a procurement process or a payment process, or an opportunity management or driving lead managements, how do we do that today? And look at how we can amplify their current processes, either reducing the friction, unlocking new productivity gains, and also amplifying their current potential, what they can do with these additional tools. So we have to look at, from the eyes of an person or end user. And look at how do they do the work today and see how these new tools and technologies in this new intern based architecture can deliver value in a different way. Jeff: One thing I think from my conversation with Ken even that he brought up is this idea that you guys launched an [00:07:00] internal AI agent for your own teams and just the productivity that's had of just smoothing the road for everyone to move faster. How'd that come about, I guess is my first question There. Karthik: Actually we are in year two of our AI journey, and one of the first pain points that as employees had is service management. Whether it's IT service management or HR service management. We had like eight services across the company and it was a very painful process. Anytime people ask a question or a request, it takes anywhere from three to five days, and the common things were still the same, like, forgot password, or I need access to this application. What is the latest vacation policy? What is the latest benefits? Hey, I've closed a deal. I don't see my comm statement yet. So the normal things that a day-to-day employee would use and we were getting for the size of a company. Across all those functions, we were getting almost like 5,000 plus requests, So when we looked at it, we saw that, okay, almost many of them are driven by knowledge. AI is great when we have good knowledge in the systems, and so we want to tap into, okay, let's pull in all the knowledge, train a model, and create a self-service ai. Where employees can [00:08:00] have a natural interaction, ask questions or request for services, and also fulfill some requests automatically as well. So helping them to find information quickly or get help quickly or automatically service fulfillment as well. So we rolled it out almost a year ago and today we have almost close to 60% self-service deflection. We get 10,000 interactions. In our AI agent, which is lives and breathes in Slack. So it's kind of a natural flow of work because we do operate slack day in, day out. So we just embedded this new AI agent, self service as part of the natural flow of work. And that really helped us, and that was our first AI agent that we rolled out Enterprisewide a year ago. With that. We did have a lot of lessons learned. It was not like a smooth ride. We had our ups and downs as well. The problem was, it was not about the technology, it was non-technology aspects like organization readiness, knowledge gaps, the content should be very rich. So those kind of the things that where we worked on today, I think it's a well running machine and we have built more agents on top of it now. The one thing that worked in our favor is as consumers like you and I, we are embracing these technologies in our [00:09:00] personal lives, And when we come to work, the same consumers are employees and they expect the same thing. So that worked in our favor. It was a good tailwind for us and we had lesser of the head headwinds over here. And obviously we have to do more like pilots and region based and we didn't roll it out to all the services on day one. We started with it like eat our own dock food. And then we learned the lessons from there. And obviously LMS were not that great a year ago. 'Cause the models were still coming up. And so we have to fine tune multiple variables, like people, aspects, readiness, aspects, enablement, technology, LLM. So it was almost like an long, simultaneous equation with 10 variables that we had to keep on fine tuning. It almost took us like three to four months. Last year to settle down, and then we started rolling out to other chat services functions like hr, finance, workplace services and everything. Jeff: But you get, people spending less time thinking about this and less switching costs. You get people happier. Because they don't have to go through this inhuman process that actually involves more humans. But you're also learning how to, you know, I'm sure you guys picked up a lot of important skills for building other kinds of, agents and other functions into your product. You guys [00:10:00] mentioned when we talked earlier this tool called True Peer and how it kind of accelerated training and communication. I think that it's just a cool tool that I checked out after too, but maybe go into a little bit more of like, what did it actually look like? Why was this so important? Karthik: We were going through various functions to identify the potential use cases and opportunities. We already had a catalog of his use cases and still we didn't realize about this content creation of videos. It would take so much time. As employees, we don't have visibility. What happens behind the scenes? All we see is awesome videos that gets published on the website, but behind the scenes, what we came to know is for every video by language, it used to take like four to five hours and we have to support like 10 plus languages, and we do multiple product releases and capabilities on a regular basis. And at the same time I'm, I came across this vendor troop here at a CI forum. And I asked, what do you guys do? It started as a simple question. Oh, we create ai generated content from a simple screen recording. So had that in mind. And I just shared about that product in our AI channel where people contribute a lot of things. Suddenly Shay's team came up, [00:11:00] Hey, this seems very interesting. We create like thousands of deals on a regular basis and okay, go ahead and give it a try. Right? So, and they went and give and try. It was a very self-service based no product set up, nothing. We just created trial account and boom, they were able to create like 10, 15 videos and multiple languages with different avatars. And I thought that use case was only specific for product and training, but apparently. Every other part of the organization. Does create videos, like it can be a sales demo or a customer success presentation, or my own team was doing internal enablement videos and it kind of went like wildfire. Okay. Then I thought, okay, anything, content creation is a pain in the neck, right? Whether it is PowerPoint presentations or video content, or audio file or podcast, Gemini made it very easy to create podcasts on existing documentation. But this one, we didn't even know that we have to solve this use case, right? So this is one of those use cases where Shaki Stream wouldn't come and tell that, Hey, I need to solve this video creation lifecycle better. 'cause it never even popped up in our use cases list just because we saw this product in the market. it's almost like [00:12:00] an unmet need and unlocking new productivity is Jeff: Yeah. Karthik: say it, right? So. Shakir: every feature that we build here in the product team requires some form of an educational video to help our Jeff: Yeah. Shakir: understand the value of the feature the capability, how to turn it on. This is typically a four week minimum journey of scripting, recording, editing, publishing. And what we found is just exponential impact. I mean, we dropped that down to four hours because AI automation allows for a rough cut recording to be submitted. We're creating our rough cut videos anyways as we do our internal release management, we're taking that we're loading it up and ar ai avatar. So our Zuora own persona essentially dynamically creates the script, presents the script, and then we get localizations for free. So we've also extended our reach. because now we've got these educational content in our Zoa University in multiple languages. So it's been immensely impactful for us and the team. Karthik: And it's you. Search as well. So now if you [00:13:00] publish it on a website, when I go to chat, GPT or other, it can search through the video and pinpoint the exact video clip, right? So it reduced from four to five hours to less than five minutes, I guess, Jeff: Yeah. As wild , talking about the level of improvement you can see in some of these initiatives. It's not like, oh, hey, we shaved 25% off of the time to make a video. It's, we went from four weeks to four hours. I'm not gonna do the math off the top of my head, but it's a really high percentage cut. Right? It's like 96% or something. But I think it brings another interesting point. is one, just we have all witnessed now there's just absurd, even exponential, as you know, logarithmic increase in tooling that all has these things. A lot of it is just. Trash, to be honest. And a lot of it's really good. But there's no way any one group can keep up. And then, again, at zuora's scale and where you guys sit in a lot of companies stacks, you have to be a little aware of. You can't just bring in everything and connect it and, you know, go crazy. So, Shaki for your team how do you guys think about. Balancing experimentation and velocity on, on trying some of these new functions. And , I assume there's some crossover [00:14:00] with with Kar X's team on that as well, Shakir: yeah, Jeff. So I would say about a year and a half ago we had these AI zealots at Jeff: Yeah. Shakir: These were like bleeding edge people who were experimenting. They were excited about the possibilities of ai. They were either doing , side hustles or fun projects. But two kind of challenges with that. Number one, small cohort of individuals versus greater. Across the p and t organization, we didn't yet have vetted or approved tools that we could use. And fast forward to today, partnering with it with Karthik's team we essentially have the tools available that have been vetted and it. tandem with a culture of and structure to use them responsibly has been the real key unlock for us. I would say , it's not only about adoption, but literacy with these Jeff: Yeah. Shakir: is really important to us. 'cause when you think of Zuora Karthik mentioned right, we are the quote to cache engine. So we Jeff: Yeah. Shakir: customers price package. Bill capture payment, all the way to revenue recognition. We're a financial system of record. So we can't allow [00:15:00] for every employee to plug into any AI tool or online. That's just not Jeff: That'd be an interesting, that'd be an interesting dance right there Shakir: yes. I see it kind of like the symbiotic relationship, right? Our, if customers don't trust that we've got the right processes and the right tools in place, they're not gonna be a customer for long. if employees can't trust the tools, so they're not vetted by our internal folks that are vetting these, the AI champions crew, then they won't use them. And if the company can't trust how employees are using the AI tools, then obviously it can never scale. So all these Jeff: Right, but the. There's also even like a fourth almost, which is if, but if you guys can't help them get access to some of the things they need for innovation, right? You're just gonna start to get like dark it where they're gonna do it themselves anyway. So you have to provide like the velocity and experimentation where they don't feel like they have to go outside the rules, but you also have to keep it safe. Shakir: Agreed. Karthik: Yeah. that's why we do in partnership with Chuck and team, right? So we do have a well established AI governance process. It's a three stage process. . First stage is anybody in the company can go [00:16:00] experiment on a new tool Karthik: within the guardrails of security and compliance. Once they see a good proof of value, not even a proof of concept, like proof of value, yes. Is it moving the needle? Is it a net new or is it a duplicate? So we check all those things. And then when it comes to stage two, is where we really review the business value, the business case more, additional security and compliance to make sure how the data is governed, how are the L LMS doing, what type of privacy we're going to have. And then we go to the stage three where we formally approve it and take it forward. So stage one, it's a big funnel. Anybody can do, but You cannot go connect your production system. Jeff: I love that though. ' It goes back to, you know, people want to be trying these things and you're always gonna have those people who are going to be on your vanguard of pushing forward. But Shaki to your point, it's about not just adoption, but literacy and literacy has to include how do you do this as a safe, steward of the data you have access to. So, I wanna kind of dig in a little bit here into like, now that you have the tools, you have the access Kartik group has been fantastic about. You can try these new things and move fast what does that look like on the product I, we had a dinner actually last night in Boston and had, you [00:17:00] know, 30 product folks from, a couple small startups up to some of the biggest retailers and you know, financial companies in the world. And everyone though had the same question of like. How are each of you actually using this stuff in workflow to make teams faster to 10 x our productivity? So I, I'll cherry picked a couple of examples Shakir: . O one might be pretty obvious, right? Like as a product. Technology company. A big part of what we do is research to identify what are the next big bets that we want to make as a product team? How does that drive our roadmap? And so, product owners and managers are essentially spending a lot of time researching. So extracting. Community ideas, our partner sentiment, our analyst reports our competitive intel, and extracting all that data. We used to manually identify themes which would drive priorities. And now tools like Gemini and OpenAI and CLO are allowing us. To use them to extract the themes and of course that research is now taking minutes instead of [00:18:00] days. So that's just not efficiency. That's literally velocity. Another maybe unique use case that we're just experimenting with is sales enablement. So every release we have to train our field and external stakeholders around what was built and why it was built. We're using Gemini storybooks to essentially load up our release collateral, and our recordings and we're allowing our sales. Reps and go to market folks to jump in and ask questions into the Gemini storybook. And why is that important? Well, sometimes when you're watching it real time you may not have a customer use case that really resonates for that release or that feature that was built. so at any point in time they can come back and they say, oh I remember a product manager had that in this release. Lemme go ahead and search for it. So it's like a. Powerful search tool for any of the releases that we've created. . Shakir: , Maybe the most obvious one is around self-service prototyping. Like one of my favorite anecdotes is a product manager saying like, Hey, what's in my head. And in minutes we've got prototyping tools now that can convert that [00:19:00] idea into a clickable prototype. And now even. great enough to use Zora's own design system. So it's already, in the user experience language that Jeff: Yeah. Shakir: And instead of days or weeks of back and forth that prototype can be handed off to UX and engineering to refine. So that speed of ideation has changed dramatically as well. Jeff: I'm curious if we can double click on that. What is the process like, how are you doing that to, you know, do the prototyping at speed, but still maintaining the Zuora design language and all of that? Like, is there tooling you, kinda brought in Shakir: yeah, so like, funny enough we were getting really scrappy in the beginning. We just for full transparency, we've Jeff: Yeah. Shakir: with VV Zero, we've played around with lovable, with rept three are really great applications, so we haven't really decided which ones better. Jeff: Yeah, they're good for different things. I feel like when I've used them. Shakir: Yeah. You know, you can get really scrappy. You can literally take a Figma output and throw it in there and you can prompt and say I want to do this, and I want it to look like this. And they're actually all really good at that. And A lot of them now have that bridge to Figma direct, Jeff: [00:20:00] Yeah. Shakir: your own design language as well. So those are two of the hacks that, Jeff: Oh, nice. Shakir: allow us to unlock building Zuora specific experiences. Jeff: So you guys are just kind of, porting out of Figma where you are already doing UI design and using that to get the guardrails. 'cause what's interesting is I feel like in the past, like literally a week or two, I have seen three different startups in this space where their entire thing is you can integrate with your GitHub or your you know, libraries around your front end. And be able to help you prototype fast, but in the language of your product. So it looks like it's part of your product. Shakir: Yeah. And we probably would've started that way, just connecting our own repos. We are an existing Figma customer, Jeff: Yeah. Shakir: and so our design language is there, our component library is there, and so that's why we leaned in on just , bridging that Jeff: Yeah. Shakir: director Rebell. Yeah. Jeff: And that really has allowed you guys to accelerate by, the prototyping and hand it off and you know, if a picture's worth a thousand words that's way more. Shakir: . Yeah. And Jeff also on the engineering side, like. Obviously as a financial system of record, everything we do needs to [00:21:00] be auditable. It's just who did what, when what did it look like before? What did it look like after? For every single feature we build at Zuora, there's a sprint dedicated to. Integrating to what we call the notary service. Immutable, auditable database that our customers can report off of. and that used to be a sprint, like a 14 day effort. And with cloud code, we've cut that down to 10 minutes. Like literally Jeff: Yeah. Shakir: what object are you enabling or building? And essentially it'll write the code on how to bridge to use the SDK to write into our notary service. Jeff: Right. Shakir: efficiency gains for anything integration oriented as well. It's very binary, right? Like we do a lot of payment and bank integrations for our customers who need to collect cash. And so we're seeing 30 to 40% efficiency gains with integrations as well Jeff: 14 days to 10 minutes is. . Basically you're saying it's zero like functionally, right? If you go 14 days to 10 minutes, that's functionally zero at that point. that brings up a good point 'cause we had on recently non you who's the head of product over at Linear and he brought up a [00:22:00] really good point of, at some level, , the value is you can kind of reduce the time to do things right? There's 30% here, 4% here, but the big thing is the cost of trying things goes to almost zero, ? Like I said, 14 days to 10 minutes is basically just went to nothing. That time doesn't exist anymore, but that means now you can. Try a lot more things. A few of those things are gonna hit really big and before they weren't worth it, but now they are. It's a whole new world of opportunity that you can try. Shakir: Agreed. And the way we're thinking about it is almost the concept of an AI labs as well, where we give our customers the ability to test and play around in a dedicated sandbox , They can load it with their dummy data, it doesn't have their production data. And we can quickly release innovations that we've built and like get feedback, experiment with our customers in in mind, right? Like allowing Jeff: Yeah. Shakir: first class citizens to our development and giving us feedback. Jeff: Right. One thing I'm curious and kind of for both of you is at heart a lot of the things we've talked about have come down to. Adoption and education [00:23:00] and literacy. You know, we talked about the beginning with the, you know, internal agent for internal support. It's a good learning exercise to then take lessons to other things you've done. But that's hard to scale across, the size of company you guys have and the multiple geos , that you operate in. How are you kind of ensuring that this is something, everyone is literate on? How do you keep everyone moving and building and trying when maybe it's not always their core job is to do something quickly or there's a deliverable now, but you also need them to learn for the long term so they can all get better at this. Karthik: change management is one of the key success factors. it has five times more weightage than enabling a new technology, Because we are all used to doing our work in a different way now with this new interface and intent-based interaction. We need to think differently, right? So we have established a good practice of change management and organization readiness. Number one is we created , in addition to the Zoey, the agent for service management, we also enabled a Gemini notebook, lm, Whenever we have new tools and processes and new functions enabled, we just put [00:24:00] it in the lm And we have an enterprise wide notebook, lm, where people can go and ask questions. You can create a podcast out of it. But what I've noticed is individual functions as well, they've created their own version of Notebook, in Go to Marketing, they have created their own version of it. That is specific to their needs. So we have like common enterprise ones and then we have functional notebook element, which is exactly what we wanted because we cannot, one team cannot do all those things. So that's number one. Number two is we do have a regular enablement session by geos, by row, by personal, on a weekly basis where we walk them through what is possible, how can you do our work better. And so we do have partnerships like with OpenAI and Glean and other vendors. they come and show the best practices, what they have seen with other customers. So rather than hearing from us, we have them connect with our employee base directly. So that's number two. Number three is Shaki team started with the hackathon couple of months inviting even external parties. A month ago we also held an prompter on kind of a challenge where we [00:25:00] enabled as I mentioned earlier, we want to be an ai native employee base first, where we rolled out all the tools like Gemini and open AI and clean and everything else. And we just wanted people to just experiment. Just use it. Right? It's almost like when we get a new Apple phone, we don't get training on, we just use it. Right. Similar to that. Right. And nobody gave us training on chat, GPT as well. We all used it. So going along those same principles, we just enable them with all the tools and we gave them some specific needs. Can you identify one use case or two use cases? With these tools? Can you see how it is making your life better? If you find something cool, why don't you go share it in notebook, lm, or share in office Slack channels. So it's more like word of mouth. Communication as well and enablement. And then , in this prompt us on almost 200 plus submissions came in and we curated and prioritized the top 30. And in that we started the top eight. So this whole exercise made it more like a community driven one where people were exposed to the new tools they were playing with the new tools, they came up with these use cases, they built up prototypes using AI tools, and they also gave a demo as [00:26:00] well. By doing all those things in parallel. I would say that our enablement and AI literacy is in a good state right now. Obviously we have to do more work on it ' cause AI is not constant over here. It keeps changing every day. So, again, at least the mission is running now. Shakir: The only thing I would add is that was the catalyst, that we took and essentially helped to get to the next level for product and technology for our team. Our goal is every employee in product and technology. A hundred percent of all individuals as Karthik put it, are experimenting. They've got their hands on something, and helping us understand how their work will change, helping themselves understand, the impact that the tools can make. And so what we did is we put together, a flagship event called AI Week. That's AI Week is because I'm not creative. I couldn't really come up with a better name. Jeff: It's well communicated and it's straightforward. I love it. Shakir: was, Jeff: There's no question what it's. Shakir: it was inspiration from the chief product officer at Amplitude. Francois was giving me tips on how he was helping to drive adoption across his [00:27:00] product and technology organization. But, What was really exciting about this event is it was , first of all, bookshelf with our CPTO kick things off Pete, really talked about our overall product strategy and how we can leverage AI internally to unlock it. Out the event reminding us how pivotal this moment in time is Jeff: Yeah. Shakir: employee 11 at Salesforce, so he was in the real midst of the internet wave and SaaS. So he really told a lot of stories really playing back to his experiences of another precipice in tech. And then we used this opportunity to unveil our new. AI design language from the UX team, our new AI architecture from our chief architect. Our CSO came in and did secure coding session, and a lot of it was hands on keyboard. and then to lead up to it, we did something called crossing the chasm exercises. And these were all like exercises that individuals were asked to take on. Prompting and things like that. And what was really exciting is during this AI week is those individuals came back and presented their crossing the chasm exercises. We cherry pick the ones that [00:28:00] resonated. And a lot of it was of course, accelerating our H two roadmap items. But there was some real fun ones as well where folks went rogue. I'll give you an example. Individual named Steven Parker, he went and built. lovable clone using Claude. How meta is that? Like using one AI tool to build another AI tool. And it just showed like the art of the possibility and it was just Jeff: Yeah. Shakir: And again, like the intent here is as Karthik pointed as how do you drive a ton of adoption and make sure everybody knows that this is a moment in time to experiment, get their hands on things so. Jeff: , I creeped on both your LinkedIn pages just to look at what you've been up to lately to, in case you know, there was any good questions to ask. And I saw you both, posting about the prompt, Aon and cart you with the hackathon. And , I had to ask what are some of the more. Wild or interesting outputs from that. 'cause everyone has seen, you know, oh we, you know, figured out a way to do a chat bot this way in here or something. Any other fun kind of takeaways Karthik: I was one of the judges from the top 35. And apparently whatever came up to the top was all product relevant ideas, right? , And because we've already [00:29:00] kind of, expanded our enterprise AI things, yes, we did see some, but some of them were so cool, Shaki, I'm not sure like whatever the top finalists did, right? Like an auto close and driving the self-service implementation and end-to-end. Those were the things people were able to do prototypes in less than a week, which would've ideally taken like three months or six months. So we were also seeing AI being used. To fast track their ideation process and to show what is possible and make the product capabilities better. There were other internal use cases, like how we can drive more growth and drive better personalized engagement with our prospects and customers. Those were the typical use cases that we have seen as well, Jeff: Like you said, there's a couple people who did real product things and they were able to get from kind of idea to prototype that functioned in a week or under a week. And that's at kind of some fundamental level. That's the pace we all have to be operating at now it's so cool to see a company like Zuora moving at that pace where at your scale and to have that pace is so powerful. , It's a fun time to be in tech Guys. I'm really glad you can of come on and talk about this. Karthik: thing I want to share is not Jeff: [00:30:00] Yeah, please. Karthik: Not everything is Roy. Jeff: No. Not as much as we like to think it is. Karthik: We had our own ups and downs as well. Hey, this tool is awesome. Why are people not using it? we have seen it in a couple of the tools where the adoption is less than 10%. Where we ideally want it to be like 40, 50% adoption in the first three months. But the key thing is, at the end of the day, CFOs going to be holding us accountable. ' cause we have reinvested some of the SaaS rationalization into the new AI tools and capabilities and not adding motor budget. But we need to see the value. The value is not about, Hey, I have saved, let's say a hundred hours on producing 10 videos. Right? That is a great first step, but the key question that we have to share back with the leadership team is what did we do with those a hundred of, Did we create more videos or did we do other high value oriented work? we are not there yet. We are still at step one. We are saving some time here and there. We are driving growth in some areas, but how are we actually translating that into more new values, what we are Jeff: Yeah. Karthik: working on? Jeff: It's very much not. Can you make a hundred more videos? It's, can you try all [00:31:00] those 10 things now that you couldn't do before? They're now possible. Well, I am bullish from talking to you guys, bullish on seeing where you guys go. We'll have to have you on maybe a year and see how that's progressing. Or I guess at the speed of ai, what a year in the new times is about a week and a half. is LinkedIn the best place to reach out? If people are looking to ask questions or is there a better way? Shakir: LinkedIn is the perfect yeah, Jeff: Awesome. Well guys, thank you so much for coming on. . It's been a blast talking to you. Let's definitely stay in touch and I'd love to follow up and see how it goes, a year or so down the line. Karthik: No, thank you Jeff. It was great. Thanks for having us. Jeff: Awesome. Thank you so much. Have a good day. Bye. Bye. Karthik: Thank you. Bye.