Introduction

If you are a CFO, finance executive, or founder who wants to understand not just where AI fits in finance but where it can genuinely transform the way the role operates, this conversation is for you.

In the tenth edition of the Hyperbots Fireside Chat series, "The Dream AI Agent for a CFO," we sat down with Shalini Urankar, CEO and Managing Partner of IkigAI Advisory, and a former CFO at Iron Mountain and Evolved by Nature. With over 15 years of finance and strategy experience spanning consumer goods, tech, banking, and biotech, and with stints at Intel, P&G, Credit Suisse, and multiple venture-backed startups, Shalini brings a perspective that is unusually broad. She has operated as CFO in both large global enterprises, where she was signing off on SEC documents for the Americas, and in early-stage companies where she was building accounting departments from scratch. She now runs IkigAI Advisory, where she helps growth-stage companies across the $5M to $50M range with finance transformation, automation, and AI adoption. She is also an Advisory Board Member at the Boston CFO Leadership Council and at the Finance and Accounting Technology Expo.

Moderated by Niyati Chhaya, Co-founder and VP of AI/ML at Hyperbots, the session covered where AI is already changing how Shalini works, the full breadth of what a CFO actually does in any given week, the specific finance process areas where AI can deliver the most impact, and what a dream AI agent for a CFO would genuinely need to look like. Shalini is one of the few finance leaders willing to be specific: she names the tools she uses, the exact processes that still embarrass her with their inefficiency, and the one-line vision for what the ideal AI co-pilot would do.

She came into the conversation as someone already using ChatGPT, Perplexity, and Claude daily, with a very clear and differentiated view of what each tool is actually good for. That practical fluency shaped the tone of the entire discussion.

Key Takeaways

• The most effective CFOs are not using one AI tool for everything. They use ChatGPT for brainstorming, Perplexity for factual research, and Claude for financial reasoning and policy analysis.

• Most finance teams are wasting weeks on budgeting, forecasting, and board prep because the operational coordination is still painfully manual.

• Every CFO wants a living cash flow model that updates in real time instead of static Excel sheets that become outdated the moment they are finished.

• Finance teams that automate AP and payment workflows can reduce monthly close time by up to 50 percent and redirect that time toward strategy and analysis.

• Revenue recognition is still heavily dependent on disconnected systems, manual Excel work, and repetitive validation across billing, CRM, and ERP platforms.

• CFO adoption of AI is accelerating because finance leaders are realizing the real value is not replacing judgment, but eliminating operational drag and manual workload.
  
  
  
  

The Conversation

How CFOs Use AI Tools Like ChatGPT, Claude, and Perplexity in Daily Finance Work

Niyati: I'm going to start with a very explicit question. Where exactly do you use AI or a gen AI tool today, especially from a finance leader perspective? What are the key tools and where do you use them?

Shalini: Okay, so I've used ChatGPT, Perplexity, and Claude, though I will say I'm most well-versed with ChatGPT. What I've learned is that ChatGPT is a very powerful tool for a CFO, right? Besides just doing the traditional drafting reports or scenario modeling, I've used it in a lot of other areas like contract management, board investor prep, or business model understanding if I want to dive in and understand something quickly.

You need to be able to ask it the right questions though, that's the caveat I will say. A few cases I found incredibly useful: contract management and negotiation strategy. I would never replace my GC or legal team with it, but it is a great way to initially get some insights on contracts and be able to build an initial proposition of how you want to attack it or how you want to negotiate it. In the old days, and I say old days, maybe a year ago, I would have to sit and read every contract and spend hours and hours on it. Whereas now you're able to kind of turn it around so quickly because at least you have an initial thinking document you can use as a starting point.

Another one is, now in my advisory business, I'll end up speaking to a lot of different clients and have to very quickly learn new businesses and dive into them. So the company research, landscape assessment, and business model understanding part, if you're able to use the right prompts and dive into it, ChatGPT gives you a really, I mean, I'm able to learn about companies within half a day sometimes, and it typically would take me a couple of days. Like going to Google, doing the research, putting it all together, then bulletizing it and summarizing it. The tool does that for you. So I think it's a huge time saver and productivity enhancement.

And then I always use it for a lot of generic things like drafting, summarizing financial information, or drafting emails for communications such as monthly updates or starter slides for board or investor updates. So the biggest advantage has been the time saving behind it. It takes a lot of the manual work and helps you start at a much more efficient point.

A general rule of thumb: I found ChatGPT very useful for brainstorming, learning, building a narrative, and creating starting versions of drafts for communication. Perplexity is usually good for deep research or if you want actual facts and actual data. And then Claude I've used mainly for reasoning or decision support. Like if I want to deep dive into a specific topic like accounting treatment or financial policy, it's been really useful there. I use it every day because it's been incredibly helpful.

What a CFO Actually Does in a Week

Niyati: So I'm hearing two things. One is these tools are extremely powerful and can help a CFO. But more importantly, while you were speaking, I realized that a CFO's daily realities are all kinds of things. It's not just balance sheets. Can you walk me through what your typical week looks like?

Shalini: That's a loaded question. Okay, so on any given week I could be doing anything from reviewing cash runway or investment policy compliance, to negotiating contracts or end of terms, managing audits, preparing board information, or leading a cross-functional planning session with sales, operations, R&D. And all this is besides the one-off tasks, like a 409 evaluation or writing a revenue policy or coming up with a BCP or fundraising work, evaluating vendors. And that's on top of the traditional finance layer, which is reporting, forecasting, variance analysis, and so on.

There is a rhythm or calendar to it. So from a daily, weekly, monthly, quarterly perspective: daily, the kind of things I would get hit with would be reviewing a cash or bank balance because I want to validate the investment policy, or an urgent investor or board email that I need to get back to instantly, or approving a vendor payment or a contract. A weekly thing would be more around outcomes of forecast reviews, meeting budget owners, finalizing payroll, or meeting with my CEO for headcount decisions.

A monthly thing is usually driven by the close calendar: journal entries, reconciliations, variance analysis, preparing a deck to communicate to the board and to my C-suite team, audit follow-up sometimes, KPI tracking things. That's typically monthly. And then quarterly is usually around investor and board reporting. So a lot of time is spent making sure you've tightened your forecast, updated all your cash flow and runway projection scenarios, and then building that deck, reporting it, communicating it to your investors, and preparing for your audit committee meeting. That's how I would summarize the rhythm that we as CFOs face. And of course, there's a lot of ad hoc stuff that goes on. So it's quite a juggling act.

How CFOs Build Board Decks, Financial Narratives, and Investor Presentations

Niyati: Do you actually end up making those slides? Or there are people or teams that do that?

Shalini: Yeah, my team would help. So the thing is, my team would kind of pull the data. They have to do the entire forecasting piece, the scenarios, and so on. So they would pull all that, have the latest data. And initially when you come into a new company, you have to set up that board deck and everything, but then you kind of reuse it. So they would just update it. And then that's when I sit with them, validate everything, make sure things look right, understand how we want to communicate the story and the narrative. That's the initial cut I do with my team. Then I do it with my CEO and COO. And then we build the whole story of how do we want to communicate to the board and what are the things we want to focus on for this quarter.

But I do think as a CFO, you'll end up orchestrating the whole thing. Because you have to make sure the tasks get done on time. You have to have a calendar to get it done so that you're prepared and have enough time so you don't walk into a board discussion blind. We have a calendar, we make sure the board gets the slides at least two days before the discussion so they have the time to absorb it as well. CFOs play a big role in making sure that whole flow goes smoothly and seamlessly so there are no surprises.

Niyati: So it's more like ensuring this whole thing works in a rhythm rather than just ensuring the books are in place?

Shalini: In addition to ensuring the books are clean and the financials are clean and everything makes sense and the story holds and there are no mistakes. That's the status quo, that's the base expectation. But you do have to also manage that entire movie strip, if you can call it that.

Why Financial Planning and Budgeting Still Consume Months of CFO Time

Niyati: Do you see key use cases or pain points where you would really want AI to help?

Shalini: Oh I mean I can think of tons of them. Maybe we can start walking through them one by one. Let's start with the first most basic one, which is financial planning, budgeting, forecasting. That's always like a three-month marathon. You're gathering inputs from every function, reconciling assumptions, building templates, holding budget meetings functionally, as well as all-day meetings with your CEO to bring them up to speed. You have to revise, create decks, revise decks for the board to communicate.

So that's one area where I can see AI capability helping dramatically. There's a lot of time spent in this whole planning process itself. If you could have AI generate budget templates from prior years, look at the previous year and create a shareable template, that would be huge. It would save my analysts so much time. Scheduling and coordinating these meetings, because today it's done manually. Someone from your team has to do it, and that's not really huge manual value add work, it's more administrative.

And once you have the initial budget template rolled in based on inputs from different teams, you could use it to identify outlier spends or headcount changes or things that dramatically immediately stand out so you can start investigating what's the cause for that. And then obviously the presentation side: the initial draft of the board presentation slides, what are the key drivers, what are the assumptions, what is the initial view of the budget. AI could help create a starting draft and that would save tons of time. Because today I'll talk to my controller and my financial analyst and tell them here's how I want this presentation to look and they'll spend probably two to three days creating it. But AI could probably do it in like five minutes. So it would save weeks of time if we could leverage it for some of these budget tasks.

How CFOs Use AI for Faster Variance Analysis and Financial Forecasting

Niyati: And even on the monthly variance and forecasting process?

Shalini: Yeah. I mean, there's today, most finance teams, your analysts will spend time doing the variance analysis, comparing actuals with the forecast, figuring out what the key drivers are. And it's all done with your systems and using Excel. But with AI you can automate that piece. You can save them days of manual updates because you could use this learning to kind of auto-refresh your forecast. And you can again help it to create the story in the sense of what are the insights, because we usually have a monthly review with cross-function leaders because we want them to know how they're doing versus the forecast and what the drivers are and if things need to be changed.

To tell that story, we have to create a deck. AI could pull that together really quick and give us an initial draft and save us time. And if you take it to the next level, you could also push these insights into Slack or Teams, and that way you can do cross-functional alignment much more quickly. You might not even need to have these meetings, you could just have them done through these updates and only meet if needed. We all deal with too many meetings as is. These are typically days and days of work, but it can be turned around much faster if you had AI capability to help with all of these tasks.

Why Real-Time Cash Flow Visibility Is Critical for Modern CFOs

Niyati: There's one thing you've talked about quite a bit, and through all the fireside chats we have done so far, the one thing every CFO thinks about is cash flow. So why is that the case?

Shalini: I mean, cash is survival, right? Especially in startups and especially in biotech, like where I've been in the biotech space, the burn is really high. So every CFO tracks it obsessively, because you can't afford not to know your cash position. I run weekly and monthly cash models with multiple scenarios because you constantly want to know what happens if something goes wrong. What if my collections drop by 20%? What if my sales double? What if a big PO slips this month? What's my runway, what's the impact? Because you don't want to be in a position where your cash position is in a dangerous place.

Even out here, in the companies I work with, we've built cash models and we do these simulations. Usually I lock myself up with my FP&A person in a room, we run these, we understand them, figure out how to present to the CEO, and then figure out the follow-up action items, because in some cases there are measures you have to take to either stop some spend or build strategies to ensure your runway is extended as much as possible.

This kind of work takes a fairly long amount of time, because you have to update everything, update your financials, update all your models, and you can't be dynamic about it because it's still an Excel model. But an AI agent can automatically update these, it could simulate scenarios in real time, it can alert us when burn rates or assumptions deviate. And it can do it as it happens. You don't have to wait for a month. Now you can have a living self-updating model instead of a static Excel. That's every CFO and every CEO's dream. In fact, I know in my last company my CEO would always say, why can't this be more dynamic? Why can't I get these answers on the fly? But it wasn't so easy for us because you had to go do the work behind it to come up with the situation.

How AI Is Transforming Spend Management and P2P Operations for CFOs

Niyati: So there are two parts to cash, right? One is definitely the spend side of things. What's your take on AI for spend management or P2P?

Shalini: I think spend management, the P2P and O2C areas, there's a huge ability for AI to help. Let's talk about the spend management and P2P first. There's so much manual work that happens. Purchase orders, approvals, vendor invoices, all done kind of manually. In smaller companies, people will use QuickBooks Online or Xero, but they're still very painfully manual. The biggest peeves my team will complain about, that's the work they don't want to do.

A smart agent could do a lot of things to take away that manual piece. You can connect your QBO to your ERP via API to your banks directly and all that reconciliation piece becomes so much easier. You can classify spend automatically, flag duplicates, flag policy violations. And there are a lot of bolt-ons today that can help with the spend management side. Just to give you an example, I've done this with my client companies as well as in my previous companies. We were able to bring in tools like Hyperbots and Tipalti for AP automation, and based on company size you need to pick the right tool, but they can really simplify things and take away all the manual work, and be able to give you a team that is not doing all the crunching work but actually looking at the outputs and validating and making sure things are right.

The biggest value add for me was the monthly close timing. I've seen it reduce close timing by 40 to 50 percent by using these technologies and automating your CFO stack. In my last company, we built API interfaces with our banking, used different tools, and we were able to really bring down our close time by half. And we were still very manual in our processes. So if you use more elite AP automation tools, there's even more opportunity to take away the manual stuff that's a huge time sink.

And yes, there's the trust factor of, do you want to turn everything over to agentic AI? The process I advise for companies is a phased approach. Let's do, I don't know, 10% automated and we validate it. It's also a good way for your organization to build trust that the tool can do the right thing. And then you gradually, maybe over a year's time, start doing more and more until you're convinced that the tool does exactly the same thing my controller or someone on my team would do. It's just helping us free up time to do more insightful and value-add work. I found it to be a huge help in freeing up my finance resources' time to repurpose them for more strategic tasks.

How AI Can Simplify Revenue Recognition and Financial Reporting for CFOs

Niyati: What about revenue recognition? On that side, where do you see AI playing a role beyond the repetitive tasks?

Shalini: For revenue recognition, I've worked with companies where a lot of like tech SaaS and product SaaS kind of models, right? It's a lot of work in Excel today. Because you have to correctly interpret your ASC 606 revenue policy, make sure that you're booking the right revenue every month and every quarter. And if you don't have some kind of AI capability or a tool to help you, then you're essentially doing all this in Excel. It's actually days and days of work and it's very tedious because the systems also don't talk to each other. Your CRM doesn't necessarily talk to your ERP, and so you're dumping reports from everywhere, validating, then assimilating it in an Excel sheet, then manually looking through every customer and validating across your invoices and collections and interpreting what's the right revenue recognition number, what's the deferred revenue, because from a revenue forecasting perspective you need to do that.

This whole booking to billing to revenue forecast process is very manual, tedious, and error prone. And it involves a lot of systems: your ERP, your CRM, your billing tools. Many times they don't talk to each other. So leveraging these AI capabilities would be huge. And now there are a lot of tools which can easily bolt onto your existing ERP and really take away all the manual handling and ensure accurate and compliant revenue reporting. It can also help with the collections piece, like pulling up open invoices and predicting who your late payers are. It can draft collection emails. It would be a huge time saver on the RevRec side, and especially companies that have SaaS subscription models would benefit hugely.

How AI Can Help CFOs Simplify Audit, Compliance, and Risk Management

Niyati: So that's the internal looking side. What are your key pain points around audit and compliance, and where do you see the role of AI there?

Shalini: Yeah. Audit and compliance, risk, these are things that often fall under the CFO's responsibilities and they're often thankless but very critical. That's where you can slip. Audit prep is a real grind, especially in smaller companies, sub-25 million. There's not a lot of processes. Collecting the evidence, tracking your PBC lists, doing the follow-ups with departments, and they don't even have repositories sometimes. That's very common.

So I think what AI could do is build this audit repository and always maintain it in a way that it's always ready and updated. Because that's where the slip happens: certain things don't get updated and then you're in the middle of your audit and you don't have the supporting documentation or the right invoices. That's usually where the bottlenecks are. AI can also pack files to the right controls from a compliance perspective. It can auto-alert you: if you give it a list of documents it needs to watch out for, it can auto-alert if something's off or a compliance document is missing.

Investment policies is another one I can think of. After the whole SVB debacle, companies are very stringent on that. All boards want you to have an investment policy and you have to follow it, and they really mandate it. And that's manual work today: your controller has to go in every day, check your bank balances, make sure you have that 60/40 split or whatever you promised. An AI tool could easily just flag that to you every day and tell you exactly where it is. Are you compliant, are you not. Send your reports to whoever wants a report. So you're covered for that. There's a lot of these small recurring things that tend to absorb a lot of bandwidth, but they're perfect candidates for automation in this compliance, risk, and audit space.

The biggest value for all of this is in the SME space, the 5 million to maybe 50 million range, because they're typically the ones with very small lean finance teams managing all of this along with everything else.

Contract and Vendor Management: Shalini's Favorite Use Case

Niyati: Let's go to contract and vendor management. I know you're both passionate about this and have spent a lot of your time on it. What is it you need to solve there?

Shalini: That's my favorite use case, right? Not just the contract management piece I talked about earlier, but also vendor negotiations. Because these can be pages and pages. Vendor contracts can be anywhere from five to 25 pages. And AI agents can easily scan them, summarize them, point out the risky clauses, suggest negotiation levers. I would say it's easily cut my review time by maybe 70, 75 percent. And again, I would never replace it with AI completely. I obviously involve my legal team. But it's a great tool to speed up the process time on the reviewing part and the strengthening the contract part.

AI also has the ability, from a vendor negotiations point, to extract key terms, risk exposure, and renewal alerts. That's all again very manual if you don't have a contract management system in place. A lot of companies will have just an Excel sheet with all their vendors and all these terms put in, but if you don't have a system in place, typically you'll have someone assigned to go verify and look at it quarterly and come up with follow-up tasks. But this can very easily be done through an AI agent versus doing it manually. And in some small companies it's even non-existent, not because of lack of awareness, they know they need to do it, but more because of lack of bandwidth.

Niyati: Would you want your AI agent to do the negotiation on your part?

Shalini: No, never. I pride myself on my negotiation skills. But I'm more than happy to use the help on the levers and the brainstorming because I really do think, it's honestly helped me come up with, I feel like I've negotiated much better contracts because of being able to get that input. Because yes, experience gives you some, but sometimes you're not thinking of everything. And if from a learning perspective, it can spit out everything that's possible, then it's like the world is your oyster. You use your judgment, take what makes sense, and at least you know what the boundaries of the negotiation are. It's a huge enabler.

How CFOs Use AI to Identify the KPIs That Actually Drive Business Performance

Niyati: All of this is fine, but the only way of measuring it is going to be your KPIs. Where do you see AI play a role in ensuring you're looking at the right KPIs?

Shalini: You're very right about the KPI piece. And I'm a huge believer that you don't need more than three to five KPIs for your company. But those three to five are very important because they help drive action and change where it matters. Because KPIs differ by industry. It can be ARR, CAC, LTV for SaaS companies, or throughput or OEE for manufacturing companies. And sometimes, like if you're coming up with a new business model, an AI agent can actually recommend and visualize the right KPIs for your business model. Because you might be in a new industry and it can help you kind of figure out what matters most.

It has that intelligence to look at all your reporting, look at your outputs, and then say, okay, I think these three are the biggest drivers for your business. So you need to use these as part of the storytelling for the rest of the team to drive the right action in the right direction so that you can meet your results for the year. That's where I found it useful. When I look at new businesses, you need to understand like, okay, hey, if I want to drive the right action within this new organization I'm working with, what do I use that is meaningful enough for them to want to work with me on this?

How CFOs Use AI to Track Macroeconomic Trends and Business Risks in Real Time

Niyati: All the while we've been talking about internal looking measurement. Where do you see external or macroeconomic macro movements playing a role? And how do you actually deal with it today?

Shalini: That's a great question. And especially in today's environment where things are changing so quickly and one decision at the macroeconomic level could completely turn around numbers you thought were set for Q4. So I think it would be very helpful to have some kind of dashboard where you pick what those four or five criteria are right for you. It might be oil prices, it might be tariffs, it might be any key underlying driver that impacts your business. Transportation, logistics, depending on what your business is, there's a whole list. And I think it would be great to have a dashboard that AI could generate very easily, one which tracks what are these key drivers and what does it do if this changed by five or 10 percent in either direction. What would that do to my key measures, like my revenue, my profit, gross margin, net margin?

I think that would be an amazing tool for the CFO and the C-suite to keep on top of in real time. If something changes, if an underlying driver changes for my business, here's what it could do to my results for this quarter or for this year. And so I think it also then enables immediate action. Because today we are still very much in this world of month-end close happens, then you know what's happened, then you go do something about it. Whereas having something like that would make it more real time. You would actually be in a position where if something drastic happens, you immediately know and you can go gameplan. How do I address the situation? What do I do? What do I need to change? Do I need to do something or just watch and wait? And you could ask a CFO right, tell me what are your underlying drivers, because most CFOs know their business inside out. So they know what are the things they're tracking and what's going to have that impact. So everyone could customize it for their needs.

How AI Can Help CFOs Simplify Fundraising, Data Rooms, and Due Diligence

Niyati: What's your take on the fundraising and investor side of things?

Shalini: From an investor relations piece, mostly that's the communication piece, and usually you just communicate on a quarterly basis. On the financing side, I definitely see places where AI could help. I'm talking more on like, if you need to do fundraising, either debt or equity, there are many tasks associated with it. You have to compare banks, compare lenders, evaluate terms. And every time you have a conversation with anyone talking to you about financing, you need to create a data room and due diligence folders. You have to put together a lot of data they can use to vet your company and start the discussions.

AI could help you actually shortlist what the best financing options are for a company at your particular stage with your risk profile. And it can start creating those due diligence folders or even summarize term sheets. That will free up the CFO more to have the conversations, focus on the strategy piece, build relationships, be able to sell the value proposition, and spend time on places where they can have the most impact versus chasing the team about, is the data room up, did you send the guy the information. There's a lot of follow-up that happens during these financing discussions, administrative stuff that has to be done, and unless it's taken care of in time, it also reflects badly on the company. So if all that administrative stuff could be taken care of by AI, it frees CFOs up for the relationship side of things, which is ultimately what helps someone say yes or no to a financing decision.

There's also the cap table. Especially in small companies, they have very complicated cap tables. I've been through a cap table transition between tools and it's the worst nightmare you can ever live through. You have to make sure when you transition between tools, your cap table has to be right in the new tool. It was me and my controller sitting for hours validating everything. But an AI tool could easily compare and say, yes, you've got things that are wrong here. Simple things like that, which we just take as part of being in finance, there's opportunity to leverage technology to ease those troubles.

Niyati: So especially in growth organizations going through changes, whether acquisition, mergers, splits, or migration of data from one tool to another, that may be a very strong agentic use case?

Shalini: Yes. I'm actually helping a company right now that had a whole NetSuite integration go completely already. And they're in a really bad situation where systems are not talking to each other and they're not able to get the historic data in. Having an AI tool working with you, not taking over, it would be such a huge enabler. Because it's a lot more difficult to do these things manually since there's so much data and it's very easy to miss something. Whereas a machine usually doesn't make errors in these data-intensive situations. So it can help from a process flow point of view. And then you take it, own it, and run with it. But it takes away the grunt work.

How CFO Adoption Is Actually Evolving

Niyati: While we've been talking about all this cool AI stuff, there is a fact I've accepted with time: CFOs are conservative. And that's the nicest way I'll put it. So what are the guardrails it will take for technologies and for packaging to ensure CFOs are comfortable using these tools? What features for the AI agent absolutely have to exist for adoption? And are you seeing a cultural shift?

Shalini: I think I'm definitely seeing the culture shift. If I compare conversations I had maybe a year ago versus two or three months ago, I feel the trust factor has changed. A year ago, CFOs were like, I'm not going to trust an AI tool or automation to take care of all this. My team needs to do this. What if they make a mistake and it all blows up in my face? To now I feel people are willing to at least take a look, give it a try.

And I think some of these approaches that we've tried, of let's do a phased approach, let's evaluate the tool, let's validate the work it's doing and be convinced that it's doing it right before we use it at a much broader level, those tactics are definitely working. A lot of CFOs now do want to find the right automation to help reduce manual tasks. Especially the three areas: spend management, P2P, and O2C. I see a lot more CFOs being open to that. The struggle for them is the manpower and the bandwidth to do it, because usually smaller companies will have very lean finance teams.

And actually, you can even leverage AI to help with the decision itself: tell the AI about your company, your business model, your workflows, and have it provide you something like a CFO automation advisor, like here are tools you can evaluate that can integrate with your existing infrastructure. Here's the pricing, here's the feature fit, here's API readiness, here's how much we believe it can save in ROI or error reduction. It can help you create an implementation roadmap.

I've definitely seen the culture shift where a lot of CFOs are now open to evaluating their tech stack, seeing what the gaps are, and especially using automation AI where the low-hanging fruit is and where there's intense manual work. Whether they have the money to invest in it is a whole different question. The current macroeconomic situation is also making a lot of them be on hold. But my job is to keep talking to them till they agree. And I also think: if AI can help CFOs strategize more than just put processes in place, CFOs will be happier with AI. It's beyond automation now.

Niyati: Most definitely. And how do you see that playing out?

Shalini: I mean, it's freeing up the CFO to think. To model scenarios, to explore new markets, to manage investors, to think strategically. Think about it right: there is a lot of time you spend drafting emails and verifying and making sure things get done. All that time that's given back to you, you can really help the CFO focus on value creation, not on task completion or data collection. That was the old way of doing things.

I've joined companies as CFO and then in three months I have HR and IT under me, and not many more people added, and you're suddenly managing like half the company. But you don't have more bandwidth. That's becoming increasingly common. So being able to see and recognize that there is something out there that can help you save time and bandwidth is a very liberating thought and a very liberating opportunity for all of us.

What Does Your Dream AI Agent for a CFO Look Like?

Niyati: Maybe if I were to force you to describe your dream agent in one line, what would that be?

Shalini: I would say a co-pilot that's proactive, trusted, something that is able to connect my systems, help me prioritize my day better, and help me make better, faster decisions. That would be a dream AI agent for me. And ideally it would do all this before the board meeting, not after. So that way my stress levels would be down 50 percent.

Niyati: So you want this agent to imagine your calendar and fix things before they are on it?

Shalini: That's fair enough. And something that combines human judgment and intelligent automation. So essentially a CFO workspace is something we are trying to build and should be out there soon.

How Hyperbots Is Helping CFOs Get There

Shalini's description of the gap between where finance runs today and where it needs to be is exactly where Hyperbots operates.

The spend management and P2P areas she described as the clearest low-hanging fruit connect directly to Hyperbots' core. The Invoice Processing Co-Pilot achieves 99.8% accuracy in invoice extraction and 80% straight-through processing, which means the manual classification, duplicate flagging, and policy enforcement work Shalini described as a daily drain on AP teams is handled automatically. That is also where the 40 to 50 percent close timing improvement she described comes from: not from one change, but from the compounding effect of removing manual steps at every stage of the process.

On the cash flow side, Hyperbots' Payment Co-Pilot automates payment scheduling, vendor communication, and ERP posting while giving finance leaders real-time visibility into outflows, which is the kind of dynamic, always-updated cash position that Shalini described as the most urgent gap in her current toolkit.

For the accruals process that is painful at every month-end, Hyperbots automates accrual discovery, booking, and reversal with less than 5% variance between accrued and actual costs, reducing both the effort and the risk of error during close.

Hyperbots does not try to replace the CFO's judgment. It handles the operational layer so that judgment can be applied to the decisions that actually matter.

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