How AI is reshaping revenue operations and accounting

That, uh, my legal team asks that we share on all of these presentations. Uh, you would absolutely hate me if I read this word for word, so I’m definitely not gonna do that. Uh, but please take a moment to read our forward-looking statement before we begin. Some quick housekeeping before we get going. Uh, I hope you interact with the chat. I’m actually seeing some of that going on right now, which is awesome. Uh, it would be really great if you started by introducing yourself, letting us know where you’re from. Really excited to have you all here, so thank you very much for joining. Um, if you quick… Uh, if you click down at the bottom of the Zoom browser where it says More, you’ll find some more resources, uh, that are available to you, uh, throughout this, uh, webinar. If you, uh,

would like to ask any questions, use the, uh, use the chat function, use the Q&A, uh, and that’s gonna be monitored by my team in the background and, uh, I’ve already asked that they just kind of interrupt me and break in if there are any questions that, uh, folks want to talk about, and that would be great. Um, and then finally, we are recording today’s session, uh, and we will be sharing a follow-up afterward, uh, for everybody that registered. So thank you all very much again for registering and for joining. Uh, looks like we’ve got a bunch of attendees already, which is awesome, and we will get going. So welcome, everybody, and thank you for joining today. My name is Laz Pastrikos, and I lead Finance Solutions at Zuora. Uh, our team of solution engineers work really closely with our customers’

finance, accounting, and revenue leaders. Uh, we help them optimize their monetization and revenue operations as their business models evolve. Today, we’re gonna focus on a challenge that is becoming, uh, increasingly important as AI reshapes the technology industry, and that is how organizations can keep revenue accounting ahead of the growing revenue complexity. We’re gonna discuss how AI is changing pricing models, the impact that’s having, uh, across finance organizations, and how leading companies are adapting their processes and systems to keep pace. Before we dive too deep on the complexity that finance teams are facing, uh, it’s really important, I think, to understand where these challenges

actually begin. AI hasn’t just changed the products that technology companies sell. It’s changed how they go to market, how they price and package, and how they actually transact with their customers. For the last twenty years or so, software companies have operated in relatively a simple model. Customers bought seats or licenses or subscriptions. Sales teams could forecast bookings, finance teams could forecast revenue and cash, and everyone had a reasonable understanding of how customers would consume the product. But AI has changed that model. Unlike traditional software, AI has had a direct cost associated with the usage of the product. Every prompt, every workflow, every agent action consumes resources. And as a result,

technology vendors are increasingly shifting from selling access to selling consumption. And that change has transformed how the software is being sold. Customers are less willing to commit to large contracts before they’ve proven value, and instead, they want the flexibility to start small, consume what they need, and expand as adoption grows. The conversation has shifted away from how many users do you need to how much value are you creating. But what’s interesting is that most companies aren’t abandoning subscriptions altogether. Instead, they’re adopting hybrid monetization models that combine the predictability of subscriptions with the growth potential of usage-based pricing.

Revenue accountants are increasingly seeing contracts that include platform subscriptions, minimum spend commits, prepaid credits, usage-based overages, all of that within the same customer relationship, and that’s where the complexity begins to accelerate. Historically, a software company might have had one product and one pricing model. Today, that same product may be sold through multiple different go-to-market models simultaneously. One customer may purchase pure usage. Another may negotiate a minimum commit. Others might have volume tiers or prepaid credits or overage structures. The same product can be monetized through several different commercial models. And the challenge for finance is that every one of those pricing models creates a different

set of operational and accounting requirements. What used to be a relatively simple SaaS contract is now a hybrid model that combines subscriptions, commitments, consumption, credits, variable pricing structures, all of that. And that’s really the key takeaway. AI didn’t just create a new product category. It accelerated the shift from simple subscription businesses to hybrid monetization businesses, and every new model creates downstream complexity for billing, revenue accounting, and forecasting. As I was saying, AI monetization isn’t just creating challenges for one team. It’s creating challenges across the entire finance organization. On the invoicing and AR side, finance teams need to support increasingly complex

hybrid pricing models, capture and validate large volumes of data, provide transparency into customer invoices, and manage disputes across consumption-based charges. On the forecasting side, organizations are trying to predict revenue, cash flow, and customer spend in a world where consumption can fluctuate significantly from period to period. But for today’s session, we’re gonna focus primarily on the revenue accounting challenges in the center column. Specifically, we’re gonna look at how organizations are managing six oh six compliance on increasingly complex contracts. We’re gonna take a measured approach in today’s call, tackling the initial challenges customers are seeing with contracts of this nature. While future webinars in this series are going to

focus on areas such as… Variable consideration associated with usage contracts, reconciling usage, billing, and revenue data, and even some of the broader billing and AR-specific topics as well. When organizations first encounter these monetization challenges, one of the questions that inevitably comes up is whether they should build something themselves or buy a purpose-built solution. And when we think about that decision, I always come back to a couple of simple points. First, it’s your financials. We’re not talking about a marketing application or an internal workflow. We’re talking about the systems that ultimately drive billing, rev rec, and financial reporting that ends up in front of your auditors and investors.

Accuracy, auditability, and controls matter. These processes sit at the core of a finance organization. Second, it touches your customer. Every invoice, usage charge, payment interaction, and dispute ultimately impacts the customer experience. If something goes wrong, customers don’t blame an integration, they don’t blame a data pipeline, they blame you. The monetization process has become a customer-facing process, which raises the stakes considerably. And third, and perhaps most importantly, just look at this slide. This is the reality many organizations face today. Usage, billing, payment, and revenue data are all moving between systems,

between teams, and across different data models. Every line in this diagram represents an integration to build, to maintain, and to eventually troubleshoot. Now imagine introducing AI hybrid models into this environment, new usage metrics, pricing models, and contract structures. The complexity can glow– can grow very quickly. The challenge isn’t simply building one integration. The challenge is maintaining an entire ecosystem that remains accurate, scalable, and compliant as your business model evolves. Now let’s compare that previous slide to what we’re looking at here. On the last slide, we saw fragmented architecture, multiple systems, data models, and integrations. And every time the business introduces a new

pricing model, that complexity increases. What makes Zuora different is that we approach AI monetization as a single business process rather than a collection of disconnected applications. At the center of the platform is a common product catalog and a common data model that spans the entire revenue life cycle from quote to cash to revenue recognition. Instead of moving customer contract, billing, and revenue data across separate systems, these processes operate on a shared foundation. And we believe this creates a number of major advantages. First, finance gains a single source of truth. The pricing structures, usage records, invoices, and revenue schedules all originate from the same framework.

Instead of spending time reconciling systems, teams can focus on analyzing the business. Second, the business gains flexibility. As we talked about earlier, AI is accelerating the move toward hybrid monetization models. New pricing strategies shouldn’t require months of integration work across multiple different systems. Because these capabilities operate on a common platform, organizations can introduce new pricing models much more quickly and with significantly less operational risk. And finally, finance maintains control. Every transaction that ultimately impacts billing, collections, and rev rec remains connected through the same life cycle. That improves traceability, auditability, and confidence in the numbers.

Ultimately, the value isn’t that Zuora’s providing billing and payments and rev rec. The value is that these capabilities were designed to work together as a unified platform. As AI motions continue to evolve, the question isn’t whether your business model will be more complex. It almost certainly will. The question is whether your architecture has to become more complex along with it, and we don’t believe that it does. So we’re going to step out of slides for a bit and get into an actual demo itself. What we’re looking at over here, if I can move my Zoom screen. Thank you. What we’re looking at here is Zuora revenue. And what I’ve got is a revenue contract, uh, that is becoming increasingly standard in the, uh, the age of AI monetization.

Um, an immediate view at the top here shows me a couple of things. Uh, we look to be, if you see here, about three-quarters of the way through the invoicing of this contract, a little bit over halfway through revenue recognition, and I can see in total about a four hundred thousand dollar contract. I’ve got a sixty K deferred balance, and I’ve got a hundred and thirty thousand of ratable items still left to be recognized with about thirty-four, thirty-five thousand left to even be released, and that’s probably unused usage. So let’s look down here and get into the individual performance obligations themselves. I have a platform charge that’s invoiced quarterly. I have an implementation and setup fee. And then below that, I’ve got three usage-based charge

items. One of them is a pay-as-you-go offering, one of them is a quarterly commitment of credits, and this third one here is a separate line for overages off of that committed line. This performance obligation or POB template area is where I can see the timing and nature, the when and the how revenue is going to be recognized. So you’ll see that many of the different items on this revenue contract are all going to be recognized slightly different from the other. That platform charge is going to be recognized ratably. The implementation fee is going to be recognized on a percentage of completion. And each one of the usage-based items are going to be recognized as the customer consumes the goods and services in the contract itself. I’m

gonna scroll over here a little bit. I’ll get back to some of that stuff in a second. And I wanna talk a little bit about SSP allocations. You’ll see only the top three lines are actually included in the allocation. This is seeming to become more and more standard, uh, from the contracts that our customers are talking to us about these days, where only the committed lines that are sold are included in the allocation at the outset of the arrangement. So Zuora Revenue is looking and determining what the standalone selling price is for each one of the individual performance obligations using those standalone selling prices to reallocate those, uh, the, the, the selling price associated with them. Uh, and that allocation, again, as I said, is being contained within those three items.

The two that you’ll see down below, these are the paygo item and the overage of the quarterly credit item. Because those are considered non-committed at the outset of the arrangement, most of our customers are now looking at those as being ineligible for allocations. So you’ll see that those are going to be recognized at their selling price when they become applicable to the contract. Here within this quarterly usage line item, I’m actually gonna go into the consumption history and show you how the customer has been consuming this, uh, service, and what that means from a journal entry and a revenue recognition perspective. Without even leaving my revenue contract, without having to go into a separate tool

or into another area of my platform, I can see the actual usage that is coming across from the customer, uh, for that, uh, prepaid drawdown quarterly credit line item. I can see on a month-by-month basis how much is being consumed and what that really means if that’s coming over daily or hourly or weekly or monthly, whatever that is. I’ve got the insight into the usage history on each individual line item. Here on the overage line, I can see that when the customer at the end of the first quarter went into overages, that’s the only time that revenue ended up being recognized on that item. And we’ve designed Zuora Revenue to set up a separate journal entry that can be directed to hit anywhere, either at the same

place as the quarterly charge or into a different account on your P&L. So that gives you that granularity and view into the revenue that’s being recognized in the manner in which the customer is using the service, be it on that prepaid drawdown committed line, on an overage line, or if that granularity isn’t important to you, fire them off to the same place, and you’re just seeing one place for all of your usage revenue. I wanna click back, uh, here to the left. I had skipped a little bit of this before, but I wanna click over here, and I wanna show you, uh, as I was talking about before, the benefit of Zuora having all of this on a single platform means that I can jump back upstream into Zuora Billing and actually look at some of the more go-to-market, uh, view of this revenue contract.

From a segregation of duties perspective, we tend to see only a few people have access, uh, within an organization, only a few people have access into the revenue recognition portion of the platform. The people who have more of a need to know on, uh, the revenue accounting side of things. But we tend to see people have a lot more access onto the Zuora Billing side, which is more of the go-to-market area. Folks like, uh, the invoicing team, the operations team, some product marketing teams, uh, the people who have more of an interaction on the customer-facing side of a subscription. So here we’ve got a lot of the same information, but we’ve got some, some different and, uh, more granular, uh, information in some other areas. I’ve got some customer, uh, and contract summary information up at the top. But what’s really interesting is I’ve also

got a lot of the same information on the usage down here below. Because this was a quarterly charge with quarterly, uh, credits and thresholds, I’ve got all of that in– that same information of units and drawdown here on the Zuora Billing side as well. Uh, because again, lots of people might need access into this information and not just those who have access to the Zuora Revenue side, as an example. So, uh, some of the other folks I was talking about, operations, uh, customer-facing teams, they can see what’s happening with an individual contract, be able to talk to a customer about commitments and usage and things like that. And you can even pipe this information directly into a customer portal if that’s something that might be interesting to you or your customers.

One other place I’d like to touch on before we hop back into Zuora Revenue is our consumption insights analytics within Zuora Billing. So here I can see a lot of information across the entirety, across the universe of my usage-based contracts. I can see how much is being billed, how much is being collected for those usage-based contracts. I can look at individual customers and see which of my customers are the ones that are growing the most, which are the ones that might be more at risk of down-sell because they haven’t adopted the usage-based, uh, offerings that they’ve been sold. And I can even look into the future and talk about forecasted billing usage. A lot of the information and a lot of the, the,

uh, uh, the analysis that our customers are being asked for is not just backward-looking of what happened historically. It’s also what does the future look like and where can we expect future revenues, future billings. And that’s where we’ve spent a lot of time on the R&D side, is building out a lot of the forecasted side of our usage. In addition, in the accounting insights, I can also dive into an individual customer, and I can look at their usage of the tokens or the credits or the, uh, uh, usage-based services that they’ve purchased and get an understanding of where and how they’re actually using it, because that’s driving not only the invoicing cash collection, but also the revenue recognition. So some really interesting stuff

and a lot of insights and analytics that we’ve put into the platform I’m gonna jump back over into Zuora Revenue, and I wanna talk about some of the areas, uh, that, that are downstream of Zuora Revenue, including what makes its way over into the GL. As we were talking about that, uh, the information that came out of those usage, uh, uh, items and how that turns into a journal entry, we have all of those journal entries right here that are being created by Zuora Revenue and sent over to your GL in whatever frequency at whatever level of granularity is important to you. So I’ve got what looks like a very high-level view of a journal entry, a very summarized view of a journal entry here, but I have all of the very deep granular, uh, level of information

that you can use either for analysis or for if you want a more granular journal entry sent over to the GL. I have all this down to the individual line item component. Uh, think about this as you get into your GL string and how segmented and granular you want those journals to be. We can stay super high level and get to, you know, by product, by geography, by entity, things like that, or we can get super, super deep down into by customer, by line item, by, you know, whatever level that you might need in your GL. So a lot of, uh, options in how you want journal entries that are coming out of Zorbi- uh, billing and Zuora Revenue and into your ledger. I want to jump over into the reporting side real quick too because everything that we’ve been looking

at so far has been all based around a single revenue contract, but I know that lots of our customers are getting questions around the entirety, the universe of their contracts. So we’ve done a lot of work in getting you that reporting around the universe of those contracts. I know many of you have been involved and have listened to a lot of our, uh, demonstrations beforehand and webinars beforehand, uh, so I won’t dive into all of the normal reports that we have historically, things like the normal waterfall and roll forward reports. But I think it’s really interesting to look at some of the consumption, uh, reporting that we’ve put together as well. So if you look at a waterfall report or the roll forward reports, you can generally see those across all of the contracts, all of the, uh, go-to-market motions. But we’ve created a couple of

consumption-specific reports, like the events report and the waterfall report, because lots of our customers are getting asked about the types of revenue and the forecasting of revenue that is coming out of these new go-to-market motions. So I could take everything that we were looking at on that one individual contract, and I can look at that here across all contracts. And again, this is the ability to take all of that information and look at it in whatever level of granularity is important to you. If you want to get down to the individual by customer, by line item, we can do that. If you want to stay super high level and only look at by product family, by geography, by class, all of that, uh, level of granularity or summarization available to you on the platform.

The last place I’d like to go before we close this out is back to our close process dashboard. Uh, when I speak to revenue leaders and accounting leaders of our customers, uh, what they want to know is, help me understand where the business is going and help me understand how to summarize this business for my leadership and for the people who are asking my teams questions. If I was an accounting leader of a Zuora Revenue customer, this close process dashboard is probably where I would spend the vast majority of my time. There’s lots of information in here. I’ll touch on only a couple of the places, but I want to start here in the trial balance. I know we’re only at the eighteenth of the month, right? It’s the middle of the month. But if the period were to end today, I could look at this tab

here and I could see account by account what effectively the trial balance leaving Zuora Revenue and making its way over to the GL would look like. I can see my ending balance of last period, what my ending balance of this period is, the change in balance period over period, and start to get ahead of the questions that are going to come. At any point in time, this is updated in real time. At any point in time throughout the month, I can look in here and see what that balance is, what that change in balance is, and start to understand what those driving factors are, what those bridging factors are, so that I can start to pre-seed that information into the bi- into the business throughout the month. As we know, month-end close is not just the journal entries and closing out the GL. It’s also the story behind what changed

and why, and the ability to get that in real time and drive that information throughout the business so that the business can pivot in real time is really, really important. I’m going to jump back and start to, uh, close us out here a little bit. As we wrap up, I’d like to leave you with, uh, just one thought. The complexity that we’re dealing with today is not temporary. In fact, we’re still in the very early stages of AI monetization. Most companies are only beginning their journey from traditional subscriptions to hybrid subscription and usage models. The next wave will likely include agent-based

commerce, outcome-based pricing, and pricing models that may not even exist yet. And that’s why I think back to that architecture slide we looked at earlier. If today’s modern monitor– uh, if today’s modern, uh, monetiza– Sorry, I’ve used the word monetization like a hundred times. Uh, if today’s monetization models already require a complex set of integrations, reconciliations, and point solutions, what happens when the business introduces the next pricing innovation, the next revenue model, the next AI offering? The challenge isn’t just managing today’s complexity. It’s building an architecture that can adapt to tomorrow’s use cases as well. And that’s really where Zuora brings value. We weren’t built around a single pricing model or a single generation of software companies. We were built around the idea that business models

evolve. By providing a unified platform across monetization, billing, collections, and rev rec. Zuora gives organizations the flexibility to embrace new go-to-market strategies without introducing new operational complexity. Ultimately, AI monetization will continue to change. The companies that win won’t be the ones that resist the change, but they’ll be the ones whose finance and monetization infrastructure is ready for it. We plan on having a number of different, uh, demos in this webinar series, and you’ll see here some of the dates that we’ve got planned for future upcoming sessions in the middle of July and in the middle of August. Uh, so please, uh, please register for those. Uh, they’re

going to be a continuation of these and touch different areas of the AI monetization process. Uh, and finally, just one quick poll question, which is going to pop up. Um, yeah. Are you looking… Is there anything that would be helpful for you for our team to reach out and schedule a call with you to talk about some of these models? That is the end of the content that we had today. Uh, Katherine or Sabrina, do we have any questions in the chat? Hey. Yes. Hi, folks. My name’s Katherine. I’m on our product marketing team. Um, I saw some folks were asking a few things. In the Q&A tab, someone had a question about the UI on this. I’m imagining it might be someone who’s already using Zuora, and this UI looks a little different than what they were…

what they’ve seen in their instance. I don’t know if you know this, Laz, but do you know what version of the UI this is? I know it is a recent version, and we’ve made some updates recently. It, it is the most recent version. Uh, I would be lying if I told you the exact release number, uh, but we can get that out for you. Totally. And Jim in the chat, Jim McCauley had some questions. Um, I’m gonna, I’m gonna take them one by one. One says, “What size businesses is this optimal for?” So maybe it’d be helpful to give a little bit of a flavor into the types of companies that we usually work with on, uh, Zuora projects. Yeah, sure. Uh, really, [chuckles] really interesting question. We have customers that span, uh, frankly everywhere in the, uh, up and down market life

cycle. Uh, I’ve got customers on Zuora, Zuora Billing and Zuora Revenue, that are some of the largest companies in the world, right? Uh, tens of billions of dollars in revenue every year that run 100% of it, uh, for many of them through our platform. And I’ve got, I’ve got a customer that I spoke to yesterday, uh, that’s just going live on us that is ten million in ARR. So we really have, uh, an enormously broad span of customer sizes that use Zuora Revenue. To be clear, not all of the customers use every part of it. Some of them have very targeted use cases. Uh, and like I said, some of those far larger enterprise strategic type customers use an enormous amount of pl- of the platform and use just about all the functionality we’ve created.

The other follow-up to Jim’s question is he’s asking about integration to their existing ERP. Do we usually recreate every sales order in both systems and maintain both or push financial results only? Yeah, good question. Uh, if you’re using Zuora Revenue only, and we have a pretty large, uh, set of our customers who use only parts of our platform, not all of it, obviously. Uh, but for customers as an example who are using Zuora Revenue only, we tend to get very granular data inbound from the ERP, from the sales order. Uh, so we’re not duplicating it, but we’re pulling that information in from the sales order. We’re creating those revenue contracts that I had shown in the demonstration itself. Um, and we’re using that to get all of that granular level information so that we can create journal entries.

But as we send information back over to the ERP, like I was talking about before, that level of granularity can be at whatever, uh, level of detail or summarization that you’re looking for. Most of our customers tend to send very summarized entries into the GL. Uh, they tend to look at Zuora Revenue as a true revenue subledger, keep all of the da- the, the detail, uh, information in Zuora Revenue, and they, to use a very bad joke, keep their general ledger general, uh, sending super summarized entries back into that. And like I said, using, uh, Z Revenue as a true subledger. And then Jim followed up and asked, “Can the sales order be created in Zuora instead of the ERP?” Yeah. So that is how the vast majority of our customers who use billing and revenue together treat

us, right? Uh, they will use either Zuora CPQ or another CPQ and have that data filter directly into the Zuora platform. Zuora will effectively act as the sales order subledger as well, if you will. Uh, you know, on the Zuora billing side, uh, creating the contracts and subscriptions and orders, uh, having that information make its way over into Zuora Revenue for the revenue contract that you saw in the demo. Um, and again, the same answer on the journal entries as well, making their way, their, their way over into the ERP or into the GL, excuse me, at whatever level of granularity. So the short answer is yes, many, many of our customers use Zuora as that true sales order creation, so there is not duplication between multiple systems.

Yeah. Thanks for these questions, you guys. Keep, keep these coming. I’m gonna go through the Q&A tab, um, and hit some of the questions that I’m seeing pop up in there. Harini asked, “When you were showing the integration between billing and collection or between collections and Zuora Revenue, does that mean we can…” Uh, I’m, I’m, I’m reading the question again. [chuckles] “Recognize revenue based on collections without any third-party integrator or for collections data to be brought in as events?” I think they’re asking, like, what is the… how, how are we recognizing revenue based on what’s been collected? Yeah. So that’s actually, uh, uh, a very interesting topic that is coming up more and more, and especially in the AI-based world of, um… You know, back in the SaaS-only world, collection of cash historically

was not generally used as an event to recognize revenue. Uh, we found that a lot back in the six oh five days, and then we’re starting to randomly see that again in this new AI monetization world. Uh, it’s something that we’re actively working on because we do have all of that information on our platform, and tha- this would be a space to keep an eye on as it relates to collection-based rev rec in the future. It’s something that we, uh, we hope to be able to talk a, a lot more about in the next quarter or two. Um, and, and another attendee asks, um, “What are the biggest challenges in obtaining and maintaining audit-ready SSP data in Zuora to be used with revenue allocation models in Zuora, especially when they are a new Zuora user and they may not

have clean historical sales data?” Yeah. That’s a really good question. Frankly, we get that a lot, right? I would say that, uh, there’s a couple of different ways that we see our customers tend to do that. Um, generally speaking, when a brand-new customer of ours comes onto the platform, they will tend to already have some external way that they’ve been doing SSP analysis. Either they have defaulted into, like, a BESP, uh, level of, uh, determination of SSP because they don’t have that history. Um, so they’ll tend to come in and they’ll have a BESP view. They will, upon go live in Zuora revenue, use more of, like, an upload view of for each one of my different goods and services, here’s my best estimate of selling price.

Use that as an upload or maybe it’s a formula because it’s, like, a discount off of list or something like that. But then after a while, after there is history on the platform, we also have what we call our SSP analyzer, and that analyzer is purpose-built to look back over your historical transactions and actually plot them on that bell-shaped curve for you. It gives you the ability to mix and match different stratifications and different, um, uh, strategies, I guess, to look at where you might have that clustering of discounting or clustering of pricing to determine your SSPs. It actually can offer to you what your optimal SSPs might be for certain, uh, product offerings, and again, with different stratifications,

be it by product or by geography or customer type, whatever those might be, and use that history that we’ve already got on the platform to set those SSPs. If it… If you go and run that analysis and it turns out you do have that good clustering of 80, 90%, whatever it might be, a- around a certain discount or around a certain price point, you can review and approve that analysis, and that can become your SSP going forward. Or if you look at that analysis and it’s still kind of more a flat line than a bell curve and you don’t really have that natural grouping of pricing, you can continue to set BESPs in the platform until or as you actually get that, that history, and you can have a mix of those offerings, uh, and those, uh… Not offerings. You can have a mix of those SSP strategies

within individual contracts. You can use two, three, four different SSP strategies within one, uh, revenue contract to determine SSP for individual performance obligations. And then I think this is a follow-up to that same question, but it’s asking specifically around consumption. What’s the typical method to obtain, ingest, and store consumption data in Zuora? Yeah. Uh, I don’t know if it’s typical because I would say, uh, the AI monetization process, uh, what we’re seeing now over these last six to 12 months, uh, really looks a lot to me like what was happening when people were making the six oh five to six oh six move, and that we see a lot of different strategies

to do that. Uh, what we’re tending to see a lot of is leaning into a, like I was saying before, a BESP model where we are… companies are looking at where they want their customers, uh, to land. Um, and frankly what they’re also seeing that landing point B is often sales reps or sales managers, what they have as a discount threshold, right? Where that sales manager doesn’t need to go and ask for approval outside of their organizational structure, go up the chain. We’re tending to see that line, you know, a sales manager has the ability to discount 30%, something like that, without having to get greater approval. That has tended to be where a lot of our customers have defaulted into their initial BESP

because they feel like that is probably going to be where the first set of contracts kind of live from a discounting perspective, using that as that BESP until or as they actually build up that history over a long period of time. Great. I don’t have any other questions that are either in the chat or in the Q&A, but if anyone has anything lingering, feel free to drop it in and we’ll give it another minute or two, um, before we wrap this. And I think the questions, at least from, like, my perspective, the questions we’ve seen here are really, are really common questions, right? Especially as we’re talking about all of the topics swirling around usage monetization, AI monetization, um, how folks are handling best practices within

Zuora. These are all things that we really, really typically go into depth with with a lot of our customers. Yeah. It would be the really rare conversation these days, and I mean truly, truly rare, that my team is talking to a customer and number one, usage and consumption or AI monetization doesn’t come up, and number two, when it does, that these types of conversations are happening. Because, uh, again, we’re, we’re in that same place that we were back in ’17, ’18, ’19, where a lot of people knew they were making the switch into six oh six but really didn’t know what they wanted to do or how they wanted to do it yet. They were kind of looking around the market to see what some of the first movers were going to do. Um, so they were kind of making it up as they go along. We’ve, we’ve seen a lot of that these days as well as it relates to accounting policy,

and that’s where my team full of practitioners, right? We hire a lot of, uh, people out of industry and here into Zuora onto my, onto my team, and we’re seeing a lot of really good conversations and really interesting, uh, uh, science experiments, if you will, on how customers are going to market with this and how they are then, uh, putting together their billing, uh, collections, and revenue policies and, and, and, and, uh, SOPs around it. Great. Well, no other questions have come through. For the folks who did, uh, respond to the survey that they’d be interested in chatting with our team, Laz or a member of his team is absolutely gonna be

reaching out and making sure that we make that connection. Um, thank you all for joining us. Um, Laz, any other final words that you want to leave the crew with today? No, I really appreciate the time, guys. I know you’re all busy, uh, so thanks for taking the time to come talk to us. We would really love to have a conversation with you if any of this was interesting to you or if these were some of the challenges that you and your organization have. These are the conversations that my team and I have with our customers every day. We’d love to have them with you as well.