Supercharging productivity with AI in quote-to-cash

… everyone. Uh, thanks for joining us today. Um, you are in the Zora AI webinar, um, covering “AI and Quote-to-Cash: What Finance Needs to Know.” Um, we’re really excited to have everyone here. Um, please feel free to put where you’re joining us from in the chat, and if you have any questions, you can also pop those in the chat as well, uh, throughout the conversation. We will be answering those accordingly. So, um, yeah, we’re really excited for this conversation today. Um, we are gonna have some real practical conversations about how our finance team is experimenting with AI, so I’m really excited to, uh, get these folks, um, in front of you. Uh, so let’s do some intros. Um, Mallory, you wanna do a quick introduction? Sure. My name’s Mallory Foster. I am a revenue operations manager here at Zora. Thanks. Kayla? Hello. Kayla

Gentry. I am the billing operations manager here at Zora. Awesome. Thanks, Kayla. And my name is Shaelyn. I’m on the product marketing team. I’m gonna be kinda playing MC today. So, uh, before we get started, I did just wanna share a quick forward-looking statement. We are gonna be talking a little bit about the Zora product today. Um, we’re gonna focus on what’s available today, um, but we’re also gonna do a little bit of what’s, what’s to come. So, just to keep in mind. Now, before we get started, I wanted to start with the reality that most finance teams are dealing with. Quote-to-cash is getting way more complex. There’s more pricing models, more contract changes, more revenue rules, but the team capacity hasn’t scaled with that complexity. So, what happens is teams are spending more time reconstructing what happened across those invoices, contracts, and revenue before they can act. And

we believe that AI is clearly part of how teams are going to be able to close that gap. In finance, we know you’re struggling, uh, how to figure out, um, how to make the most of AI. Um, experimenting can be scary and there are real financial impacts on the line. So, we’ve partnered with The Harris Poll to survey over 300 finance and accounting decision-makers about how AI is actually being used today. Um, I’m really excited to share some of that information with you. And so it’ll just help to set up our conversation, and then we will move into a chat with Mallory and Kayla, which I know you’re all waiting for. So, first up, less than half, about 44% of finance leaders trust AI to operate within financial controls and audit frameworks, which tells me that the barrier isn’t adoption, it’s your confidence. And

nearly every finance leader has concerns about using AI in core processes. There are real risks with data privacy, lack of human oversight, and data integrity. And as you all know, in finance, almost right isn’t good enough. A- now, 87% of finance leaders say there’s a gap between AI promise and reality, but the number one issue is that AI outputs don’t integrate with finance workflows. So, in other words, teams are getting answers, but they can’t actually use them in the system where the work is happening. So, uh, let’s start with our first question for Kayla and Mallory. Uh, why do finance teams have a more difficult time adopting AI compared to other departments? Yeah. I can take that one first. Um, so finance requires accuracy. Um, a small mistake, for example, on a validation

can have financial consequences. Additionally, finance teams follow a strict framework to ensure compliance with things such as SOX, audits, and AI can make it hard to explain the decision-making to auditors and prove consistent outputs. Adoption is happening, but at a lot more controlled pace. That starts with lower risk use cases such as adding in extra checks and approvals and, uh, proving accuracy and consistency. I think Kayla made a great point. That’s a big part of what makes this harder for finance. The other piece is that AI adoption in finance just looks fundamentally different than it does in other areas. In finance, especially in billing and revenue, everything is connected. It all flows from upstream processes, so you can’t really optimize one piece in isolation.

You have to think about the entire end-to-end process. Where’s the data coming from, how is it being transformed, what your source of truth is. And on top of that, you have requirements like SOX compliance and audibility, which adds just another layer of complexity. So, it’s not that finance can’t adopt I- AI. It absolutely can. It just requires a more thoughtful, structured approach. You have to be really intentional about where you introduce it and how it fits into the broader system. So, it’s not about moving slower, it’s about moving smarter and making sure everything still holds up under scrutiny. Awesome. Thanks. All right. Um, now, Mallory, Kayla, I’m curious if you could answer, what does finance-grade AI actually mean to you both? Yeah. I can go first on this one. Uh, finance-grade AI actually means, to me, um,

having a tool, having an AI tool, that understands what you want and what you need out of that tool. And when it comes to finance, things such as the controls, um, being able to see clearly exactly what the outputs are, and knowing that the outputs are accurate, um, is what’s important to me for a finance-grade AI tool. Yeah. I agree, Kayla. For me, finance-grade AI really comes down to trust. Um, in finance, it’s not enough for AI to just be fast or helpful. It has to be accurate, auditable, and consistent. We need to understand how it got the answer, we need to be able to explain it, and we need to trust that it will hold up under scrutiny, whether that’s from leadership or auditors. It also has to fit into our existing processes.

Um, this has been a theme across, I think, most of my answers. Finance doesn’t operate in silos. So, finance-grade AI needs to work across the full lifecycle, from upstream data all the way down through the reporting, re- without breaking that chain of integrity. And then there’s the controls. Um, we need clear guardrails, strong governance, and confidence that our source of truth is protected. So, to me, finance-grade AI isn’t just about automation. It’s about delivering automation with accuracy, transparency, and control. ‘Cause at the end of the day, if we can’t trust it, we can’t use it.Yeah, that makes a lot of sense to me. And something that you had said in earlier conversations, um, that really stuck with me is that,

like, when you make one change in billing or one change in revenue, there are upstream and downstream impacts that we need to really be tracking. And so if an AI is working in one part of your system and it makes a change and that doesn’t waterfall, good lord, that’s (laughs) gonna cause you a lot of headaches I imagine. Um, so yeah, that makes a lot of sense to me. Thank you guys for sharing that. Uh, let’s move on to the next question. Uh, how do you both think about experimenting with AI in your day-to-day? Yeah. So when I think about experimenting with AI in my day-to-day, I really start with the pain points, especially in finance where there’s a lot, still a lot of manual processes. I usually ask myself, where are we spending the most time and what’s pulling our team away from work they’re actually trained to do? A lot of the time it’s the prep work. Our accountants

are pulling data from different reports, stitching things together, adding formulas, building pivots. It can take hours just to get everything into the right format. That’s a great place to bring in AI. It can take a lot of that manual effort off the table and help us get to the actual analysis much faster, and that’s really the goal. We want our team spending less time assembling data and more time reviewing, thinking critically, and making decisions. So when you start to shift time in that direction, that’s when you see the real impact, things moving faster, and you can start to shorten that close process in a meaningful way. Yeah, I agree with Mallory. The goal of AI is to find ways for our team, um, to use our time more efficiently. In finance, we already have the data. We don’t always get the insights that quickly though,

so I look for places where AI can help accelerate the understanding. I also experiment testing it alongside what I’m already doing, like using it to double-check analysis, draft explanations, or explore different scenarios. I see AI as a way to enhance our judgment but not replace it. If it can help us move faster from data to insight, then we’re in a better position to add value where it matters. I love that. It sounds like you guys are thinking about what’s the low-hanging fruit and then also doing some companion testing to help you build that trust. Um, I think those are both really valuable points. So next, let’s move on to the next question. What are some ways that you are actually adopting AI today? We’re actually adopting AI in some really exciting and very practical ways right now. One of the biggest

wins for us have been contract reviews. This has been honestly a game-changer for our close process. It’s helping us move faster, reduce manual effort, and increase confidence in our outputs. It’s been so impactful that we’re already looking at how to extend that into the billing and deal desk workflows. And we’re also experimenting with Zhora’s, Zhora AI’s capabilities in beta, which is opening up some powerful new possibilities. For example, we can now model revenue impact in real time, like adjusting allocation treatments directly in our tenant without needing to spin up a sandbox. The same goes for contract modifications or changes in carve-outs. What used to take multiple steps and a lot of back and forth, we can now do much more quickly and dynamically. And then there’s the day-to-day layer, which is just as important.

We’re using tools like Glean to instantly search across internal knowledge, NotebookLM to prep for meetings, and ChatGPT to draft and refine communications. But honestly, one of the most interesting shifts has been behavioral. People are starting with AI first. Before reaching out to a teammate, they’ll use AI to get context, pressure test ideas, or draft something. That’s creating a real acceleration across the board. Yeah. Right now, I’m using AI in a few really practical ways as well. Um, one of the biggest areas in research is in research and report creation. Um, AI has been incredibly helpful in building queries, pulling together information, and helping me work through large volumes of data and resources much faster than I could manually. Um, it’s also been very helpful taking on

complex or scattered inputs and turning them into something more structured and more actionable. On the billing side, we’ve also started adopting Zhora AI in beta, and that’s where it gets really exciting for finance and operations. We have already seen faster invoice investigations and reconciliations, more self-serve reporting and data queries that used to take hours and are now just taking minutes, and automation of some of the, uh, repetitive operational work that used to bog our team down. And the part that matters for finance is that this is all wrapped up in controls and audibility. So we’re not just trading speed, we’re also getting SOX comfort and we’re getting both. Awesome. Thank you for sharing. Um, Mallory, I thought what you said about pivoting the way that you think was really interesting. Um, instead of, you know, just tackling a task the way we’ve always done things,

I think in this new era, you really do need to think, how could AI possibly help me with this? And I think that’s a really important place to start when we’re thinking about experimenting with AI, is it’s almost permission to think differently. So, um- Yeah. Yeah. I thought that was really interesting. All right. Uh, next, if AI removes manual investigation work, like you have both mentioned in your, uh, your answers, what does that free your team up to be able to do? So this is a really interesting question that I hear a lot. Um, I know at Zhora, by removing a lot of that manual investigation, our team can focus on making better, more informed accounting decisions and doing it faster. It’s also changed how we operate throughout the month.

Instead of everything piling up at close, we’re able to spread the work out. We’re reviewing contracts continuously, which makes our month-end and quarter-end close much smoother and faster. And importantly, it’s given us the capacity to lean forward. We now have time to test and adopt new features, which just compounds the value by enabling even more automation over time. We’re also getting much more proactive with the business. Instead of waiting for deals to hit revenue and then reacting, we’re partnering earlier with sales and the deal desk teams. That means fewer surprises, fewer fire drills, and much more, much better outcomes overall.And in some cases, it’s even opened the door for us to engage externally, sharing how we approach these processes with customers, which is something we just didn’t have time for before. So it’s just, it’s not just about efficiency,

it’s about shifting our team from processing work to actually driving better business outcomes. Yeah. And again, I agree with Mallory. AI is taking over a lot of, um, billing’s manual investigation work, things like digging through transactions, reconciling discrepancies, and finding root causes. What that really does is give, uh, my team time back. Instead of spending hours just finding problems, we can focus on solving them and preventing them in the first place. It’s also shifting us from being reactive to being more proactive. We’re able to analyze trends, identify issues quicker, and improve the overall billing process instead of chasing one-off problems. That means less manual work and more focus on higher value efforts and improving workflows, building automation, partnering with other teams,

and enhancing the customer billing experience. So overall, the work becomes more strategic, more impactful, and less about reacting and more about driving better outcomes across the business. I love that you’re experimenting with Zora AI and other AI tools is actually allowing you to be those design and build partners for the AI team. Um, Mallory and Kaela and the entire finance team has been really vital to the product team to designing Zora AI to make sure that it makes sense for finance teams. And another thing that really stuck out to me, what you guys said was, it’s allowing you to be more strategic partners, um, getting ahead of issues and trying to plan and prioritize and strategize with, uh, the sales team so that you’re getting ahead of any issues that might come up,

and even trying to point out, create a solution. So, um, yeah, I thought that was really interesting. Thank you guys for sharing. Yeah. All right. Well, we’ve talked a little bit about Zora AI, and I wanted to share about our vision. So it’s pretty simple. AI needs to operate inside of quote-to-cash, not something that operates outside of it. And that means being connected across your entire quote-to-cash ecosystem from CPQ, metering, billing, accounts receivable, payments, and revenue across the whole thing. And it needs to be embedded directly into those workflows where your teams are spending their time to accelerate what would be bottlenecks or surface insights or streamline the work of running quote-to-cash. I know it’s not easy. So next slide. Um, when we talk about AI for

the future of finance, it really comes down to three things for us. First, eliminating manual investigations so your team isn’t spending hours figuring out what happened before they can act. Second, helping you make decisions with confidence because the system understands your billing, revenue, and contract context. And third, protecting the integrity of your ledger. Every action stays governed, auditable, and within your existing controls. And that foundation, finance-grade trust and governance, it wa- is what makes all of this usable in your real finance workflows. Zora has achieved the ISO 42001 certification, which is the international standard for AI management systems, reinforcing our commitment to governed, accountable, and safe AI development and operations. So AI, Zora AI is in a

place where you already operate, you already trust, um, and it’s here to make your life easier. And with that, we have a huge announcement. Um, I’m really excited to be the one to share the news with you all. Um, we are announcing that Zora AI is available for all customers to turn on now. So talked a little bit about Zora AI. Um, as I mentioned a few seconds ago, Zora AI is truly built for the people who run quote-to-cash, with billing operations, revenue managers, accountants, and collections teams in mind. We’ve purpose-built Zora AI to help with the work that you are actually doing every day, not just surfacing insights. Those are helpful, but we wanna help you move faster through those insights. And this is just the beginning of the Zora AI journey. We’re building for more personas as we speak.

And at its core, it comes down to these three things. Helping finance teams scale by reducing manual investigation work that slows everything down today. Second, helping teams move faster with decisions because the AI is operating within your full quote-to-cash context. And third, doing all of that within controls that finance requires. So like I said, big announcement. Zora AI is now available for all customers to turn on and activate, uh, and start getting value today. Now, I know you folks are probably asking, “Okay, this sounds good, but I need to know a little bit more about how you all have built this.” So I’ve said it a couple of times, it’s embedded directly into your quote-to-cash system and can see across your various products. It understands your business and it’s grounded in your data. Plus, it knows how you

work. So it learns from interactions from you, the individual. It knows who you are. It knows the context within, uh, in which you operate. It understands your habits, things that you ask it commonly, your preferences. Do you like a Z or an S? Um, your permissions. And it’s all based on our nearly 20 years of experience within the quote-to-cash space. We’ve built in best practices to help users get their jobs done really, really fast. Um, it’s governed by your existing financial controls. Every action is fully traceable and auditable, and you can always go back into your audit framework and check, check something that the, the AI maybe performed for you. And lastly, you can extend Zora AI to any LLM where your business teams are already working.

So there are… Let’s talk about the ways that you will experience AI within the Zora platform. I think about it as the new operating system inside of your quote-to-cash platform. It works directly within your finance workflows, helping you move faster while protecting the integrity of your ledger. So it shows up in three ways. You can chat with it directly. There’s an always available assistant embedded into the product for investigations, summaries, and quick actions. It’s integrated directly into your finance processes, surfacing insights and recommending actions before the user even has to ask for them.And thirdly, through our MCP servers, orgs, organizations can allow LLMs and developers to securely access ZORA data without needing to actually log into ZORA. This is really cool because it means that business users

can get information out of ZORA without actually needing to log into the system at all. Um, so it’s truly extensible and it’s embedded into your financial intelligence system. So, I know you probably wanna hear a little bit more or see it in action, so I’m really excited to introduce Kevin Souare, our director of product management, to come give us a little demo. Kevin? Awesome. Thank you, Shailynn. Hey, everyone. Kevin here. Really excited to be with y’all today, and really excited to give you a demo of ZORA AI. So let me go ahead and share my screen. Okay, awesome. Now I have ZORA open. And I know I have ZORA AI turned on in my tenant because I can see this little, uh, ZORA AI icon down in the lower right-hand corner. So I’m gonna take you through a few of the items that Shailynn just mentioned.

Uh, the first thing I wanna show you is our integrated AI. So I have an account that I’ve pinned to my left navigation. The account’s called Moore and Co. And so this account has subscriptions, it has invoices, and one of the new things that we have with ZORA AI is this ability to summarize. So you’ll notice throughout the product on key pages, uh, within the product, there will be these summarize buttons. Um, and what this will actually do is I just, I just clicked it, and now we can see that, uh, ZORA AI’s thinking. Uh, it’s retrieving all the summary information about this account behind the scenes, and it’s gonna take all the complex information about this account and generate what we call a narrative summary for you. Uh, so it’s, it’s

done that. We can see a brief description. “We’ve had this account for over 10 years.” Uh, we can see its MRR. The other thing that AI does is it, it identifies prioritized actions. So we can see here there’s a series of suggested actions along the bottom of the screen. So maybe what I wanna do is review one of the subscriptions, um, for the account. I can click this, and it’ll automatically open up what we call the ZORA AI side panel. Now, the ZORA AI side panel is available on every screen in ZORA, um, and it’ll, it has context of where you are. So if you are on, say, uh, an invoice page or a payment page, you could start chatting with ZORA AI and get key details about it.

Okay, so you might be wondering, “What can ZORA AI do?” So one of the things I wanna show you is what we call our full-screen chat experience. I’m gonna go ahead and open this up, um, and now I’m in, in an interface that, where I can directly chat with ZORA. I can go ahead and say, “What can you do?” And we’re constantly updating ZORA AI. So we’re really excited about this launch. Uh, it can do a lot of different things across a lot of different parts of the ZORA product, and we’re constantly, every week, adding to this. Um, if you ever wanna know what, what ZORA AI can do, you can always ask it what it can do, and based on your permissions and the functionalities that we have available, it’ll, it’ll tell you. In this example, I can see that

it can do a whole bunch of different billing operations activities. It can do collections. Uh, it can help you analyze your revenue if you’re a ZORA revenue customer. Uh, it can perform administrative functions. One of the really popular things is it can write data queries and analyze your data queries. Uh, and it can also, um, uh, write code, and it can build workflows for you. So there’s, there’s a ton of things that it can do. Now, uh, I have another tab here. I’m just gonna show you a few examples of some of the different types of prompts and things you can ask ZORA AI and the kinds of results you can get. So one thing it can do is it can actually build visual dashboards for you. So here, I gave it a simple prompt saying, “Can you generate a billing ops

dashboard?” Uh, and it went off. Um, now you’ll notice that it created a plan. For more extensive operations, ZORA AI will actually show you its planning process, how it has to query all the data in ZORA, how it analyzes it, and then how, you know, the final step is building the actual dashboard. And these are all inspectable. So if you wanna see the actual queries that it’s running behind the scenes and, and how it, how it arrived at those things, you can always inspect those. But the end result is that now you have a useful billing operations dashboard. Um, I can see a breakdown of invoices by draft versus posted, um, and it can also give me some key indicators of health a- along with recommendations of what to do next.

Uh, so this is one example of what you can do with ZORA AI. Another example is I just asked it to create an Excel file of my top 50 accounts in my tenant by open balance. So you can see this prompt here. Again, it goes off. This was a two-step plan it executed. Uh, it went off and analyzed this, and it actually created an Excel file. ZORA AI allows you to both import, uh, files into it. You can do screenshots of things, you can upload spreadsheets, um, and you can also download spreadsheets. So in this example, it created a nice Excel for me, and I’ll go ahead and, um, open it. Okay, and now I’ll go ahead and open it, and I can see I have a nice Excel spreadsheet of all my top 50 accounts by open balance. Another thing you can do,

um, is you’ll see this Save to Runbook feature. So as you use ZORA AI, uh, you’ll be able to, for, for the aspects of it that you find useful, for processes you wanna repeat over and over, you can create what we call a runbook, which is an automated process. If I click this button, what I would get is a, it’ll actually generate a prompt, uh, for me to create this export of top accounts by open balance to Excel repeatedly. I could create this, and then, um, over here in the runbook area, I can see all these different runbooks I’ve created, including the one that we just created here. So this is a way where AI can help you start to become more and more efficient with the work you’re doing inside of ZORA.Now one final piece I wanna show you all today

is controls. So built into Zuora is a series of governance around your AI, where you can set very specific controls in terms of how AI works with your, uh, finance and accounting data here. So for instance, uh, I have set a control here where I limit the amount of invoice amount that can be written off by AI itself. Um, and I say do not write off any subscriptions with an open balance, uh, greater than $1,000. And so to show this in action, uh, let’s say I was back on an invoice page. I can open up Zuora AI side panel. I can type, uh, “Please write off this invoice.”

Now again, the Zuora AI side panel has full context awareness of the page that you’re on in Zuora, so it knows that I’m on this, uh, invoice ending in 254. Uh, we show our thinking process. You can see, um, it’s acknowledging that the user wants to write off the invoice. Uh, we’ll give this just a moment here, um, as it’s thinking through how to do that. Um, again, we show when we call functions, uh, so here it’s querying, uh, the invoices in the tenant. And then notice, uh, it comes back with, uh, it cannot be written off due to an active business control violation, and it actually cites that same control that we were just looking at. Um, and, and so you have full control

with the AI, uh, that you can build out as many of these controls as you want to, to limit what the AI will be able to do in your tenant. And so, uh, hopefully that gives you a sense of some of the capabilities. I didn’t show you Zuora MCP, but if you work in things like Claude Desktop, um, you could connect, uh, Zuora to your, to your Claude and be able to do a lot of these same functions, uh, natively with- within Claude Desktop as well. Uh, so this is all available now. Uh, we’re super excited about this, and we hope you find a lot of benefit and value from Zuora AI. Thank you, everyone. Thanks so much, Kevin. Um, this is really cool to see. Um, there’s some new features even for me. The uploading,

um, Excel files, I think that’s really gonna be valuable. Um, Kayla and Mallory, I wanna hear from you. What are you guys most excited about? Hey, Kevin. Can you show us how Zuora AI can connect across multiple different platforms within Zuora? For instance, can it connect between Zuora Billing and Zuora Revenue? Yeah, yeah, great question, Mallory. Yeah, that, that’s one of the, the great things about Zuora AI is that it is connected across all the different products. So, um, yeah, may- maybe one, one question we could ask it is, uh, can you find all the invoices from the last 60 days where billing does not reconcile

with… Oops. Reconcile with, uh, revenue. Okay, so this, uh, yeah, this would be an example where, uh, it needs to pull data across Zuora Billing and Zuora Revenue to, to answer this question. And Mallory, for, for my sake, could you just explain why something like this would be so exciting for you? Like describe the pain a little bit. Yeah, so this is a process that I’m assuming most of our customers do at month end, um, close, and it’s just reconciling the data. A lot of times it takes so long because you’re pulling multiple different reports from two different systems, and you’re trying to marry those reports and figure out whe- where those differences lie.

Um, you’re adding formulas. You’re creating pivots. It can take hours to, just to figure out where those differences are. Wow, holy crap. Big time saver then, huh? Yeah, yeah. Yeah, we can see, uh, the plan here. So Zuora AI is always gonna show, uh, its plan and its thinking. So yeah, this is a, this is a pretty extensive one, um, you know, where it’s going across six, six different steps. You can see it’s querying a lot of different, um, data, uh, to, to, to do this analysis. Okay, so, uh, let’s see. So it’s, it’s come back. Um, it has a proposed query that it plans to execute.

It kind of explains what it’s gonna do. Now the nice thing about Zuora AI is that there are a couple different options. Um, there is a way you can set it up in- where it’s only read only at the tenant level. So if you don’t want AI taking any actions in your tenant and just querying and analyzing your data, there is a setting for that, and that’s the default setting for Zuora AI. Um, you can also have it where it can make updates. Um, there’s also, anytime it’s gonna make updates, uh, it’s always gonna have human in the loop, so it’s always gonna check with the user, um, “Hey, I’m about to do this action. Do you want me to proceed with it?” Um, so it always has that. And then in this example we can see that it, it has some questions for us. So it always tries to check with the user if it, if it doesn’t think it has enough information to perform,

you know, whatever prompt that the user is asking. So here it’s saying, you know, should we limit the results to s- specific statuses? And I’m just gonna go ahead and, and answer this the best I can. I’m gonna say posted only. Uh, I think, uh, invoice level summary. And, um, single currency. So we’ll give it those answers. Okay, so it finished a series of, of queries here. Uh, we can see all the queries if we wanted to, and then it says the reconciliation is successfully completed, and it provides a nice summary.And,

uh, it found that there were no reconciliation gaps, that all posted invoices from the last 60 days have billing amounts that fully reconcile with their corresponding revenue amounts. So, so that’s one example of how from a single interface inside of ZORA AI, you can use the AI to go out to ZORA Billing, ZORA Revenue, tie this data together, and then, uh, do this kind of reconciliation analysis for you. Wow, Kevin, that’s amazing. Thank you so much for taking us through that. Um, there are some really cool things in ZORA AI. One of the things that stuck out to me was being able to upload, uh, Excel files and then create Excel files and, and download those. That’s really cool. Um, Malorie and Kayla, I want to hear from you guys. What are you most

excited about for ZORA AI? Yeah, I can start. Um, Kevin hit the spot on a lot of what we are excited for in billing operations. Um, I will actually tell you my top three ’cause I can’t just pick one. Um, so number one’s gonna be the controls he just shared. That’s gonna be huge, um, especially for auditability, being able to know that you can trust the system to stop, um, when it needs to stop and get a second check. The second one being, um, reporting. Reporting used to take a very long time, um, not necessarily, um, for any specific reason, but just based on different data sources, different types. Um, so when you can do it in AI, you’re saving yourself a lot of time, and again, like I said before, it goes from hours to minutes, and that’s just huge when it

comes to, um, investigational work. And then the last one is the account overview in the summary that Kevin shared. Um, that is actually something we look at every day in billing, um, when it comes from customer emails, if it comes from sales. Anybody that has a question, we are constantly wanting to know what the customer’s account is looking like, whether it’s payments, uh, payment history, invoice history. And having that at one click and at the- at the front of the page is amazing. Yeah, I agree. There’s a f- few things that I appreciate about ZORA AI. Um, I think the top two, I will go with the top two. The top is being able to identify, let’s say I want to identify revenue,

um, on a- on a s- smaller subset of data, and I can just ask ZORA AI about that, be specific about what that smaller subset is, and then it’ll give me that information, um, which is super helpful. Um, the second is things like, let’s say I wanna change the allocation, um, or we’re going back and forth, you know, w- we need to know what’s the revenue impact. Before ZORA AI, we would spin up a sandbox, refresh it to make sure it had today’s data in it, change the allocation, then calculate what the revenue impact is. Now, today, I can just directly in production ask ZORA AI, you know, “What’s the revenue impact if I were to change the allocation on this revenue contract?” And

it would s- come back with the, not only the revenue impact, but how it came to that answer, um, what the calculation they used was. I- i- it’s just very detailed and very, very useful. It saves a ton of time. That’s, uh, amazing. It sounds like it’s gonna be a huge tool for y’all’s productivity. So, um, I’m really excited to use that to my advantage to get more product feedback from y’all (laughs). Uh, okay, great. Uh, that’s really all that we had to share with you today. I do have a couple of plugs. So the first is come check out zora.com/AI. Um, you can see, uh, an overview of how ZORA AI works in video format, and we’ve got lots of details on exciting use cases for AI within ZORA.

Um, also, you can choose to stay up to date with ZORA AI by clicking on this here. We’ve got a lot to share. Kevin mentioned we have updates coming to ZORA AI all the time, so if you wanna stay up to date, please join our mailing list. And lastly, for customers, ZORA AI is available for you to start using right now. Uh, so for more information, you can see our community post, um, that happened on Thursday the 16th, um, which goes into detail, uh, about how to- how to start activating ZORA AI. Uh, we’ve got FAQs. If you have questions about safety, security, we’ve got those answers. We also have sample prompts for you. We’ve got lots of demo videos for you to watch. Um, so there’s a lot of great resources in our community

page for our customers, and I’ll- I’ll just remind folks, that is behind a, uh, a ZORA OneID login. So you must be a customer and must have a OneID login in order to get in there. Uh, so yeah, we are very excited for you guys to get started using ZORA AI. Um, we feel like we have got a lot of great stuff out there for you to start experimenting in, but we are not done yet. So if you have feedback, if you have things that you wanna be able to do in ZORA AI, please don’t be a stranger. Please let us know. Um, your feedback is going to really help us shape the future of ZORA AI, and we are really excited to hear from you. So that’s all I have for today. I hope that you h- guys have a great rest of your day and, uh, we’ll see you soon.