Unlocking AI for finance teams and processes

… ready to share with all our webinars. Um, I’m not gonna read this word for word. Just gonna leave it here for you guys to have a peek at very quickly, um, and then we’ll get our speakers on the stage. Okay, great. Um, I’m gonna invite Andrew, Rachael, Madhuri to join me on stage. Um, if you guys can come on with me. We’ll give them a second. Okay,

great. Andrew, if you want to kick things off, um, for the team. Sure. So welcome, everyone, to this edition of Finance Leaders Unfiltered. Today’s session is unlocking AI in finance. Less than two months into 2026 and already AI has a massive impact on business and finance. Investors are pouring billions, hundreds of billions, into AI infrastructure and simultaneously reconsidering the valuations of software companies in particular. Uh, boards of directors have high expectations for AI’s potential

to deliver exceptional productivity gains and even potentially accelerate growth. And, uh, without question, AI is proving to be a disruptor unlike anything we’ve seen before. Amidst all this hype and anxiety, for finance teams, uh, many questions abound. Where are companies, where are competitors really in their adoption of AI? What’s working? What’s still a work in progress? Where are the benefits that people are re-, uh, reaping so far? How are companies organizing themselves? And where will the investment come to fund these new AI initiatives?

So to answer these questions and more, I’m delighted to be joined by two seasoned finance professionals whose companies are directly involved in both incorporating AI into their products and bringing AI into their finance organizations. So joining me is, uh, Madhuri, um, Madhuri Koneru who’s the senior vice president of finance and operations at Freshworks, a publicly traded software company, and Rachael Noel who’s senior director of quote-to-cash and revenue accounting at ZORA, which is a, uh, somewhat freshly, uh, uh, uh, named, uh, private company.

So with that, let’s, um, let’s just dig right into it here and start with kind of your current experience. What are the examples? Where are you guys today? Rachael, let’s start with you. Sure. Thank you for having me. Um, we’re using AI tools in a couple different ways here at ZORA, and I’ll just give a few examples. Um, one way that we’re using it is we have an AI tool that helps the revenue team with their contract review. Um, and that use case is really twofold. We’re using it for the technical accounting piece where the tool i- is reading the contract and picking out non-standard terms where the team is able to then understand where they need to focus on the reviews.

Um, and then the other piece is more on the operational side, where the tool is scanning the contract for key terms and then it’s integrated with ZORA Billing and ZORA Revenue, which are systems of record, and making sure that the key data points like start and end dates, products, amounts, all match between the systems. And that’s really sped up the, the reviews for the team and saved us a lot of time on the close. Another place we’re using it is we have a deal approval agent that will, um, bring together all the information that team members might need to decide whether or not it makes sense to approve deals. This could be customer utilization, current ARR on the account, past due invoices,

um, and it can even calculate proration credits if it comes up on an early renewal or something like that, and that’s been very helpful in speeding up that process. And then another example is our deal desk team has an agent where they’ve actually, um, input the policies that the team follows, or desktop procedures, um, really to be a place where they can ask questions about, you know, how they’re supposed to do the job and everything, um, to create a consistent process across the globe. Yeah, so contract, um, contract ingestion and contract analysis is one of those areas where we see a lot of adoption, uh, and kind of contract lifecycle management more broadly

certainly. Um, uh, Madhuri, you’re in a space, Freshworks is in a space where there’s a lot of adoption of AI. It’s really transforming the markets that you compete in. What are you guys doing in terms of bringing AI into your products and offerings? Yeah, thanks, Andrew. So I guess what, uh, you know, Freshworks has two products, just so I give some context. Uh, you know, we have, uh, the customer service suite of products and then we’ve got the IT and employee service management…. uh, which is the, uh, employee suite of products. So and then we’re embedding AI into all of these products, uh, you know,

uh, so essentially have three AI products, if you will. One is the AI Agent, which is sort of like your digital teammate. So they auto-resolve queries on your behalf, whether it’s on email or chat or other messaging, uh, apps. So they actually act on behalf of people. The second is the AI Copilot, which is exactly what the name suggests. Uh, it works alongside your team, so again, with full context from all of your systems. So it gathers all the context and the data and summarizes and suggests the replies. So teams can actually move much faster when they have a co- AI Copilot on their side, without the manual grind. And then the third one is the AI Insights,

which essentially provides the insights for the managers to lead their teams. It’s not just data or dashboards, but it actually spots trends and measures performance so managers can figure out how to improve customer satisfaction or employee satisfaction. And the interesting thing about us is, all of us at Freshworks use one of these products or both of these products, whether it’s to support our customers or to support our employees. In fact, every single department has these, uh, products deployed, and as we’re enhancing our AI features in these products, we are actually seeing better and better AI-assisted resolution. So, people just stop doing the, you know, the grungy work, if you will,

and they can actually elevate to the more strategic work, or actually resolving the more complex issues. So that’s how we are embedding AI and living and breathing AI, as well as selling it. Yeah. So based on that, you know, finance is in a position where precision- Mm-hmm…. really matters and it can’t just be 95% accurate, right? Accuracy is, is of utmost importance, and ultimately, CFOs have to sign off on the books, uh, or face significant penalties, up to and including jail time. So based on what we’ve discussed, w- what are, what are both of you doing in terms of putting in frameworks, governance, and guardrails,

um, to, to manage, uh, how you’re adopting and using both AI and the agents as you described, Madhuri? Yeah. I can go first. So I think we’ve spent a lot of time last year in actually developing sort of a framework, especially for the, the quote-to-cash area, right? And that’s sort of, you know, the theme of, uh, this webinar. Um, so I’ll just focus on that, uh, for a bit. So we actually spent a significant, uh, uh, amount of time putting together an end-to-end strategy on what we’re gonna automate or use AI for, and what is not ready yet, right? Um, so at the fundamental core of all of this is we’re, we’re actually focusing on automating and integrating the systems first, because, so we have,

uh, very high-quality data, because I think that’s really fundamental for your AI to work the way you want it to work. Um, and we’ve taken a bit of a three-pronged approach for the quote-to-cash systems. One is we’re continuously evaluating systems, you know, that are AI-intensive, if you will, and if it makes sense, we’ve actually, um, you know, we’re down the path of replacing, um, uh, some of our systems. Secondly, we’re continuously talking to our vendors, I mean, like, every software company out there, our vendors are implementing new AI feature functionality as well, right? So it’s, I think it’s super important to stay connected so you’re not always in this game of rip and replacing your existing systems, because that’s just gonna, um,

you know, derail you even more. Um, so as our vendors are putting in these new features, we’re figuring out if they meet our needs. Thirdly, which is very exciting as well, we are partnering with our, uh, CTO, who’s actually carved out, uh, you know, a budget and a small team to build AI Agents, uh, to augment our workflows and our systems. So that’s sort of the approach we’re taking. Uh, but we’re also being very methodical about it, we’re not just using AI for the sake of AI. Um, so that’s how we’re going about this right now. So if you had to stack rank kind of in order of your preference, you highlighted one, you’re looking at these new AI, kind of the quote-unquote “AI First” or “AI Native” kind of new products coming to market,

you’re talking to a, your existing set of suppliers, you’re also looking at building or maybe buying some things and building on top of them. So amongst those three options, what’s your preference in terms of how you’re thinking about or prioritizing your direction? I think at this point in time, you know, obviously our preference would be if our existing, uh, systems can, uh, enhance, you know, with the AI functionality, right? I mean, that’s the, sort of the, the path of, uh, least disruption, so that would be our preference, but that can’t always work, and there are also some new amazing tools out there in the market as well. So it’s a combination of the two, uh, but it’s not just LLMs

and AI, it’s, it’s, it’s software, uh, you know, and AI embedded within the software. And it’s not just an entire rip and replace or a- Yeah…. shift, like, when we went from on-prem to the cloud, there was kind of this binary shift from one technology base to another. You’re saying it’s not that, it’s not the case? I mean, that’s what we’re hearing, right? But nobody’s done that yet, and, uh, you know, it’s, it’s definitely still SaaS with embedded AI. Uh, you know, whether it’s the systems we’re using or the systems we’re looking at.Yeah. So in part, that’s the answer to the SaaS-pocalypse. It- it’s not that the traditional software companies are going out of business tomorrow, um… You still need the fundamental

infrastructure, the UI, the integrations, the- the compliance and all of that, right? So it- it… A- again, we don’t know where this is going, but that’s not where we are today. Yeah. Rachel, how about you at Zuora? What are you guys doing? Yeah. So similar to what Madhuri was saying, um, so we start… Um, our team is Quote to Cash, and so we sell a lot of the products that our team uses and so the first place we always start is, um, you know, what’s happening in the Zuora tool and what, um, are we able to use there to improve our processes? Um, right now, we have some agents that we’re looking at in the sandbox that the product team has created, and these are gonna do things to help on the revenue side,

like, um, being able to predict allocations on contracts that you switch, or on the billing side, being able to, um, ask questions about specific customers or past due balances. And so we’re looking at that first. Um, and then the next part that we do is we’ll go through the process and we look at where, um, the process is most painful for the team or we could benefit from having more efficiency, and then we’ll try to think through how AI can help. Is it something where we can utilize, um, some of the tools that we already have and build something, uh, to make it work? Or would it be better if we, um, purchase something that’s on the market? And we’ll go through kind of a cost-benefit analysis

to figure out the best way to solve the problem and make things better for the team. But really, our starting point is, what’s the pain point and what can we do to solve it? One of the exciting things about AI is that it enables us to address completely new use cases, right? And kind of brings new productivity tools to hand. Um, but to do that, you’ve got to kind of step back and look at your processes and be creative about how you want to solve, solve things. Um, finance teams are famously conservative, uh, in their approach (laughs) and kind of personality types, let’s say. Uh, what are you doing as finance leaders

to inspire teams, get them to lean in, and kind of embrace the tools and look at where, what- what the new opportunities are for productivity improvements? Um, so I can go first on that. So I think in 2024, we actually initiated an ideathon, um, you know, where, uh… And ever since, the teams continuously showcase ideas. And it’s not just for AI. Um, you know, of course, the- the initiative was for AI, but what it really unlocked was automation in a ton of areas, right? From the little things to the large ones. Um, and this just unlocked so many ideas. It was very interesting to see.

Uh, but on the AI side… So using those ideas, we built a few prototypes, and then we sort of took a pause and, you know, while we were working on the end-to-end strategy, so they just don’t become one-off pet projects, right? Um, but it was super cool, at least for me, to see, you know, operators becoming builders in this process. Just how easy it was to, you know, to even just build a prototype. Um, and people just got excited with this, and I continuously see, uh, people automating little things, right? Whether it’s with bots or with agents, or even thinking about, uh, you know, what features should exist in our systems. Uh, so we’re just gonna continue these ideathons and hackathons and, you know, continuously

iterate how we’re gonna automate, uh, as well as use AI. Rachel, what are you doing at Zuora? Yeah. So one thing that we’ve done is, um, we have an AI champion, um, in Quote to Cash, and she spends time also working with the overall, um, enterprise initiative with all the other AI champions throughout the company. And so she learns a lot there that she then takes and spends time with the different managers and team members throughout the Quote to Cash process, um, to understand different constraints, um, where teams are feeling like they could, um, benefit, and, um, then she helps them envision how AI can help and help bring the idea to life. And that’s actually how

we got, um, the deal approval agent moving as well as the deal desk agent. Um, and then we also encourage the team to spend time in the tools, um, and we have forums and team meetings where we ask for feedback and try to get a pulse on what people are using it for and what’s working and what’s not. And I think having the open dialogue has been extremely helpful across the org. Yeah. In addition to being, uh, conservative by nature, finance teams are also… Uh, tend to be overloaded in terms of their workload (laughs). Um, what are you doing in terms of trying to free up time for your, for your team members to, to play with the tools?

So we’ll do calendar blocks on… We’re gonna start doing calendar blocks on the calendar where it’s, you know, known that this is what you’re doing. Um, I also think that, you know, just having even one thing in your day that you want to make easier has been helping, um, trying to start small as opposed to trying to solve everything right away. Madhuri, how about you? Are you trying to free up time for your team? I think, you know, I- I gotta say, probably not consciously, the way Rachel alluded to it. Uh, you know, it’s by nature of we’re just…Um, you know, it’s part of our routine, so when people find the time, they come up with these ideas and share the ideas. Uh, and, you know, like I said,

we, we attempted to do this quarterly in terms of how we showcase them. But that’s a great idea, you know, what Rachel just brought up, like why don’t we just dedicate time with calendar blocks? Thanks for the idea, Rachel. But, you know, right now, we’re, we’re just, uh, you know, it’s part of our daily work. Um, as ideas come up, we just share and we, we start putting together presentations. Yeah. The n- the next natural question here, in addition in terms of how are you organizing, is how are you funding this? Where’s the investment coming from? Um, and I wanna ask this in kinda two different ways. One, um, both within, within your finance teams, how are you funding things? And then

in your roles looking across your organizations, as, as companies, how are you thinking about funding? Where’s the money coming from? How are you gonna structure budgets going forward? Madhuri, what are you doing in terms of funding AI? For us, it’s, it’s really not a separate line item at this point. It may be in the future. And it’s interesting, I mean, you know, when we think of pricing our products, we’re also thinking about how we’re buying these, right? Um, but today, it’s, it’s all part of the software spend. And, you know, businesses actually own the software spend budgets as well, so it’s really up to us as leaders to figure out what we want to spend on and what, what is, uh, going to deliver the most value, uh, if you will. So, um, you know, we’re constantly

looking at that and like I said, we’re also working with our CTOs, actually carved out a small budget, uh, you know, for a small engineering team to build agents for us. But on the systems side, it’s just part of the overall software spend. Uh, but again, it does go through a lot of scrutiny if you’re gonna rip and replace systems or if you’re gonna actually spend on AI features as, you know, vendors are also, um, you know, pricing in the AI features, even though it’s on a separate line item in their pricing. They’ve sort of increased the prices of the software, even at renewals, uh, because of the AI features that they are adding. Do, do you expect your overall IT spend to go up as a result of AI? Possibly, but, you know,

uh, not without an offset with, with real value on, you know, on the other side, right? So, it should net out, uh, overall, um, is, is the expectation. But there will be a period of time where, you know, you’re on your current system and you’re experimenting with new systems and, you know, but once you get to that steady state, the idea is that you will see more value and, and spend probably the same or less overall. Rachel? So for us, the way that we approach the budget is, um, AI and SaaS are kind of embedded together and it’s managed more on a company-wide level. Um,

and we have any new purchases going through a SaaS/AI rationalization committee. And really what the purpose of that is, is to ensure consistency on how we’re going about the spend, um, ensuring that leadership is very clear on how the cost since save- cost savings will come about and what efficiencies will be gained from it. Um, and then it also makes it very clear, um, that, uh, to make sure that tools are not overlapping too much so that you’re not spending on two tools that are doing, um, very similar things across the organization. Um, and that’s kinda how we’re handling it right now. The main exception for us is the product team manages their own, which makes sense given

the nature of what they’re doing in that organization. Um, but that’s how we’ve been approaching it to date. Yeah. Um, I was at a finance, uh, uh, executive offsite last week and actually, the, the CFO of Zuora, Todd, was there, and, uh, as part of his presentation, he said that 2026 is the year for AI to really deliver business value and business ven- benefit. How, I’m kinda leading the (laughs) the, the jury here but how are you guys thinking about, uh, how you quantify the value and the, and the benefits that you’re receiving or seeing so far from AI? Madhuri? Oh,

we- Yeah. So- Yeah…. for us, I think, you know, it’s, it’s, it’s gotta be productivity gains, right? And when we’re looking at AI, it’s, it’s really augmenting what people are doing currently. So, it’s gotta be a lot more efficient, um, you know, once we fully implement AI. So that’s sort of table stakes. But we’re also looking to, um, you know, get to a point where it’s, it’s a much more scalable and intelligent revenue engine from start to finish. Um, that’s what we’re looking at. Uh, in terms of the pro- the productivity gains or the value, yeah, we are gonna measure it in terms of efficiency. And so I’ll give you two examp- couple of examples of what we’re implementing, and these are work in progress, so I really hope we get the value

that, uh, you know, we promised from these tools, right? One we’re implementing is sort of a, an AI tool which acts as an operational intelligence layer, and this is super important for us because we have global teams spread out everywhere. Um, and what this tool does is it continuously monitors how people are working, and it also acts as an advisor and a coach. So, what it does is, I mean, you can choose to turn it on or turn it off, so it’s not like somebody’s monitoring you all the time, but if I wanna get more efficient and I wanna see how I’m doing, you know, compared to the best performer on my team or…… uh, what have you. So you turn it on and it continuously coaches you. So what this is supposed to help is it’s supposed to help drive consistency,

right? And with that, automatically your processes are tighter and the best practice sort of gets embedded across the team. And, you know, you also have an insights tool where the managers can see how people are performing and, and get the best, um, sort of performer, uh, insights to, to spread across the, across the team. The second one, which is a lot more straightforward, is, um, you know, an AR tool, you know, that’s, that’s AI native, uh, which promises to increase productivity by 2X, right? And what that means is it has, you know, these ML algorithms that, uh, that runs through your, uh, that runs through your, uh, systems and recommends the best action. So

what this means is then your teams are spending time on the most important, um, cases and the trickiest ones as opposed to touching every customer. Uh, so this is a lot more straightforward. So there, there are very many AI tools. And like I said, we spent the better part of last year actually evaluating what we need. Um, so in terms of value, yeah, there’s, it’s gonna be a spectrum, right? Some which are more straightforward where you can measure in terms of metrics, like productivity, you know, we just got 50% more efficient, so our teams are able to do a lot more things that they weren’t able to do before, and, you know, the DSO has improved and so on, and some which are gonna be more qualitative that are gonna be measured in terms of customer sat

or sales satisfaction. Right. And, and potentially maybe employee satisfaction. Right. Yeah. ‘Cause it sounds like in the first case, it’s the ability to take your top performers and bring their skills and abilities to share- Take the entire team…. with the rest of the team. Yeah. And in the second case, it’s how do you scale the, y- y- your finance team without having to add more headcount? Right. Yeah. Rachel, what are you, what are you guys seeing? Yeah. So very, (clears throat) very similar to what Madhuri just said. Um, we’re look at it in terms of hard savings, like is it replacing another tool and are you saving money on it? As well as soft savings like efficiency and, uh, team satisfaction

and being able to articulate that. Um, just to give another example, um, when we were rationalizing our purchase of our AI tool for contract review, um, where we really were looking was, um, trying to communicate to our leaderships in terms of reduced close time, because that was something that they really cared about because they wanted to get the numbers much faster. Um, cost savings if we scaled and had (clears throat) more and more contract reviews to do. And then also, um, increased coverage and, um, being able to look at more deals since the tool would process all of the deals. Um, and I will say in that instance, it really paid off. We did see the c- the time savings on the close, um, and we did see the job satisfaction,

um, increase for the team members doing that process, um, because the lift was a lot less once the tool was fully implemented. So… Yeah. All of this fits with where w- we’re seeing things in terms of a lot of the survey work that we’re doing, uh, and the data coming back from that. First, in terms of where you are with adoption, um, roughly based on all the survey work we do, roughly 87% of finance teams are at some level of experimentation or in early production, kind of exactly where you guys are, um, with different AI tools. And the second piece is that the expectation is that there’s gonna be more departmental

spending. Uh, I think it’s roughly 90, you know, over 90% of at least our survey respondents, uh, expect more departmental spending on, uh, you know, software applications, you know, ultimately, and, and a- and AI. Um, let’s take a minute and, and talk about how you’re pricing as two software companies. Uh, how are you pricing AI and how are you thinking about it going forward? Because, one, that affects your competitiveness in the marketplace, but two, it has a big impact on finance teams that have to adapt to new pricing models. Madhuri, you guys are at the forefront of, uh,

in- implementing AI within the product and your market space is kind of out in front in terms of, uh, usage-based, consumption-based, even outcome-based pricing models. What are you guys doing in terms of thinking about new pricing models going forward? So right now, you know, we’ve got a hybrid approach for our AI, uh, features and functionality. Um, you know, regardless of what the expectation is, customers still continue to expect pricing that’s simple and predictable, right? Because when I’m buying tools, that’s my expectation as well. If somebody were to tell me, “I’m gonna price you based on your usage and outcomes,” and I have no idea what that’s gonna be, I’m probably just gonna say no.

Um, you know, so that’s, that’s really what our customers are expecting right now. And, you know, so just to keep it simple and predictable, uh, this has always been our core principle, even when AI, you know, was very nascent, uh, and it remains true with AI as well. And, um, so the pricing for our AI capabilities, um, is really designed to scale with customer adoption, uh, but again, without the negative surprises. So that’s-… at the core of how we are looking at pricing. And this will evolve over time, um, you know, just like every other software vendor out there who’s trying to figure this out. And, and presumably as a business, you want predictability in terms of

both your revenue stream- Right…. and kind of looking forward in terms of what your costs are gonna be. Right. Exactly. Yeah. Rachel, how’s Zuora thinking about this? Yeah. So, so far, um, really consistent with what Madhuri just said. Um, most of our customers are primarily enterprise SaaS customers, and so they really want that predictability. And so, so far, um, what we’ve been doing is really just keeping it embedded in the subscription for the most part. Now, a lot of the usage and outcome-based pricing type discussions do come up, and I could see us in the future, um, maybe going that direction,

but it’s something we’re continuing to evaluate. Just as of now, we’ve kept it, um, in the subscription for that predictability to make sure that our customers feel comfortable with the pricing. Yeah. That makes sense. Um, it’s interesting. Having been in the world of software pricing and licensing for 30-plus years and across now multiple waves of different technology shifts, uh, time and again when you do any type of survey work and ask organizations what they want in terms of software pricing, number one is predictability. Uh, uh, a piece of software can cost, uh, $100,000 or $10 million. No one gets fired for what the cost of

the software is. It’s at the end of the year, if the software blows through someone’s budget, that’s when people get fired. And so ultimately, it’s not about the, the absolute price per se, it’s more about, “I need predictability to make sure I hit my budget number and don’t exceed it.” So predictability, transparency, that’s what people want in, in software pricing. We’re very skeptical about outcome-based pricing. Um, it’s one of those things that organizations may need, um, but at the end of the day, everyone, suppliers and customers both want predictability of cost to serve

and predictability of what it’s gonna cost to, to consume. I think we had a question that just came through, uh, let me see if I can get to that. Yeah. So this is a good question. I’ll put you guys on the spot here. Um, and, “How do you recognize revenue on an outcome-based pricing model? It sounds like deferral until the, uh, un- until the obligation is satisfied.” Any comments there on how you’re thinking about rev rec in light of outcome-based pricing or outcome-based deals? Rachel, do you wanna go first, or do you want me to? So, uh, so we don’t have models like that currently, but I would agree with you, Sunny. I would think

that if you’re doing the outcome-based pricing based on the outcomes, um, you would need to follow, um, the outcomes for the rev rec. But I would need to do more research e- once we actually have the model in place. Yeah. So. I mean, likewise. We, we don’t have the outcome-based pricing right now, but again, it goes back to your ASC 606. You’ve got to allocate, you know, your seat-based pricing, your outcome-based pricing, and then, you know, essentially rev rec when deli- deliverables are delivered. Um, so, uh, you know, right now, I, I’ve spoken to a few people that aren’t there yet in terms of actually incorporating the outcome-based pricing. And e- even if they do, it’s a very small component.

Um, and usually when, um… A lot of these are priced as credits. Um, so when the credits expire, you, you kind of, you know, the, there’s, there’s sort of a timeline within which you can recognize the revenue as well. So it’s a combination of things that I’m seeing out there in the market. Yeah. It, as a side note, I was having a conversation with a senior partner at one of the, you know, major audit firms, uh, last week, and she was explaining how outcome-based pricing really increases the risk, audit risk, uh, because it, it, it, it drives, uh, kind of a manual check on everything, slows deals down. Um, so that’s another one of these reasons why I think this whole push towards outcome-based

pricing, um, it sounds good for VCs who, you know, uh, look at things on a spreadsheet. But in practical terms in a business, it’s much harder to adopt. It is, but it’s, it is, it is not something that we can avoid, so we all have to start thinking about this. Uh, yeah. Yeah. That’s right. That’s right. So I think we’ve got a little bit of time left here. Let’s go through, uh, a little f- bit of fun here, uh, a set of kind of rapid fire questions. (laughs) So first of all, and if you want to expand on this, uh, you can, but first question is, what puts the most… In light of everything that we’ve just talked about, where do you see the most stress on your quote, the cash process, today?

Madhuri? I think for us, it’s the future state systems, right, for AI monetization. You know, hands down, that’s the one that’s top of mind for everybody. Uh, you know, whether it’s how do you process deals, how do you bill, how do you rev rec, and are your systems gonna scale, and are they gonna be ready to, uh, you know, to capture the AI monetization? Rachel?So, um, I’ll just give one, um, that’s top of mind for me right now. It’s the customer portal process. Um, I feel like that’s been, um, something that’s had quite a bit of just manual work, uh, having to upload invoices and all that kinda stuff. And so, we’re very excited to

have such a tool that will help us with that process later this year. So, that’s something that is top of mind for us. Yeah, certainly customers want agency, and they wanna self- Mm-hmm…. serve. Uh, next question, what’s your favorite or your pet AI project at the moment? Madhuri? I think for me personally, it would be building a pricing advisory agent, uh, you know, that recommends optimal price by using data and signals across your lead-to-cash stack. You know, all the way from your CRM to your downstream systems. So, if we can build an agent, uh, you know, that does that, I think that’d be pretty cool. Rachel? So, pet project on the side, like, if I have

any repeatable Excel files, I try to make the AI create a Python script for me so that I can do those files faster (laughs). So… So, in these moments of transition, there’s always, uh, uh, I think it’s always instructive to look at what are, w- w- what’s a quote or what’s a, um, what are the lessons from the past that we can take and apply right now? So, this is kind of an odd one, but what’s a famous quote that comes to mind in this current landscape that you think everyone should be mindful of? Madhuri? (laughs) So, for me, I think it would be two. Uh, one is, you know, only the paranoid survive, and, uh, another one that really resonates, I

think, is I look at, you know, uh, what’s, what’s happening in the market right now, is we really tend to overestimate the effect of a technology in the short run, and underestimate its effect in the long run. Right. Um, and, and that really resonates right now. Yeah, certainly that, that quote from Andy Grove, uh, Intel was at a breaking point in, in, at, at one point in time, and had to fundamentally reinvent itself. Right. Which Grove and his team did successfully. Rachel, how about for you? I think one that I always go back to is one that Todd Orkoffo said to me when he first joined Zuora. Um, he said to me, “Rachel, if sales doesn’t sell anything, you don’t have anything

to count.” So, that always resonates with me when we think about quote to cash, and you think about how your teams are the ones handling exceptions and the nonstandard deals that come through. Just a reminder that, you know, you need to process the deals because you need to enable sales to be able to sell the way they need to. Yeah. Fundamentally, how does finance go from the department of no to the department of go? Mm-hmm. Uh, what’s been your biggest positive surprise in working with the AI tools within, within your organization, um, you know, within finance to date? I think for me, it was really, honestly, the, the biggest and pleasant surprise has been the ingenuity of my team.

Uh, it really reinforced that AI can truly amplify the capability as well as the creativity of our teams. We just need to enable them with the right tools at the right time. Um, so that’s been my, you know, biggest positive surprise. Rachel, what’s been the biggest surprise for you personally? You know, I think, um, when we’ve done it right and we’ve done a really good job and used it in the right way, um, how much it’s really helped the team save on, um, mundane tasks and helped their job satisfaction, made them be able to focus on, uh, things that are more interesting, more important to the, um, overall process. And so, that’s been really cool to see.

Terrific. Well, I think we’re just about at time. Uh, I wanna thank both of you, Madhuri, Rachel. Thanks so much for your insights. And, uh, thanks, everyone, for listening. Um, Rachel, I’ll give it off to you to, to wrap up. Well, um, thank you all for joining us today. Um, please take a minute to fill out the short survey. We’ll be sharing the recording of the conversation via email as well, um, as well as any info on how you can become part of the beta program for Zuora’s AI features. Um, and thank you again for joining. Thanks, everyone. Thanks, everyone. Mm-hmm.