If you’ve ever lost sleep over complex contracts, usage-based pricing, or revenue schedules that live in hundreds spreadsheet tabs, this episode is for you.
I’m joined by Apurv Bansal, co-founder and CEO of Zenskar, to talk about how AI is helping SaaS companies automate billing and revenue recognition end-to-end. We unpack why legacy tools can’t keep up with modern pricing models, what finance teams really need from automation, and how to avoid the most common implementation mistakes.
Special thanks to Zenskar for sponsoring this episode.
Visit zenskar.com/upgrade to learn more.
00:00:04 --> 00:00:06 Welcome back to the Diary of a CFO podcast, the
00:00:06 --> 00:00:08 podcast where finance leaders share the lessons,
00:00:09 --> 00:00:11 challenges, and wins that shape their careers
00:00:11 --> 00:00:14 as well as their organizations. This episode
00:00:14 --> 00:00:17 is brought to you by Zenskar, the AI -powered
00:00:17 --> 00:00:19 order -to -cash platform built for modern finance
00:00:19 --> 00:00:22 teams. With Zenskar, you can automate revenue
00:00:22 --> 00:00:25 recognition journal entries, as well as billing,
00:00:25 --> 00:00:27 collections, and SaaS metrics. Visit zenskar
00:00:27 --> 00:00:30 .com slash upgrade to learn more. The link is
00:00:30 --> 00:00:33 also in the show notes. I'm your host Wassia
00:00:33 --> 00:00:35 Kamon, and today I'm super delighted to have
00:00:35 --> 00:00:38 with me Apurvv Bansal. He is the co -founder
00:00:38 --> 00:00:42 and CEO of Zenskar. Prior to Zenskar, Apurv
00:00:42 --> 00:00:45 held leadership roles at Google, where he experienced
00:00:45 --> 00:00:48 the building and rev -rec pain points firsthand
00:00:48 --> 00:00:52 and realized the need for flexible software that
00:00:52 --> 00:00:56 can automate and simplify this. He took this
00:00:56 --> 00:00:59 as a mission and build and scale Zenskar to streamline
00:00:59 --> 00:01:02 billing and revenue recognition at the highest
00:01:02 --> 00:01:06 level today. Welcome to the show, Apurv. Thanks
00:01:06 --> 00:01:09 for having me, Basia. Awesome. So why don't you
00:01:09 --> 00:01:12 start telling us about your journey and all the
00:01:12 --> 00:01:14 twists and turns along the way and what brought
00:01:14 --> 00:01:17 you to where you are now with Zenskar? Sure.
00:01:17 --> 00:01:21 Yeah, I mean. I started my career at college
00:01:21 --> 00:01:24 was an engineering degree. So I'm an engineer
00:01:24 --> 00:01:27 by qualification. Okay. After studying engineering,
00:01:27 --> 00:01:30 I realized that that's not what I wanted to do
00:01:30 --> 00:01:34 for a living. So I pivoted in my career. I did
00:01:34 --> 00:01:36 management consulting for a couple of years.
00:01:36 --> 00:01:39 I was a Benin company. I was 22 at that point.
00:01:40 --> 00:01:42 I felt the itch to start a company. So left my
00:01:42 --> 00:01:45 job and started an e -commerce company. Ran that
00:01:45 --> 00:01:48 for a few years, eventually, you know, sold that.
00:01:48 --> 00:01:50 After that, I decided to do my MBA. So I went
00:01:50 --> 00:01:52 to Harvard Business School, after which I went
00:01:52 --> 00:01:54 to Google. I was there for a couple of years
00:01:54 --> 00:01:56 and that's kind of where I, you know, I kind
00:01:56 --> 00:01:59 of closely experienced the pain points related
00:01:59 --> 00:02:02 to billing and RevRex that we're now kind of
00:02:02 --> 00:02:04 solving with Zansker. So that was kind of a tipping
00:02:04 --> 00:02:07 point in my journey. I did work with a VC firm
00:02:07 --> 00:02:08 after Google for a couple of years. I was investing
00:02:08 --> 00:02:12 in FinTech startups, FinTech SaaS startups. By
00:02:12 --> 00:02:13 then, you know, I realized that, you know, I
00:02:13 --> 00:02:17 wanted to be an entrepreneur again. and solve
00:02:17 --> 00:02:19 the problem that I had seen very, very closely
00:02:19 --> 00:02:22 at Google, because I saw how much time it was
00:02:22 --> 00:02:25 draining for finance teams, despite having all
00:02:25 --> 00:02:26 the engineering resources that a company like
00:02:26 --> 00:02:30 Google has. That's what kind of led to the birth
00:02:30 --> 00:02:32 of Zanskar about three years ago. Wow, that's
00:02:32 --> 00:02:35 awesome. And it also puts in perspective, right?
00:02:36 --> 00:02:38 We think that when you hear the name Google,
00:02:38 --> 00:02:41 everything is perfect. Like we think that when
00:02:41 --> 00:02:45 it's only a small company problems or a medium
00:02:45 --> 00:02:49 sized company problems when you think about billing
00:02:49 --> 00:02:53 and revenue recognition. So what was that moment
00:02:53 --> 00:02:56 for you where you were like, something like Zenskar
00:02:56 --> 00:03:00 need to be created? What I see was, you know,
00:03:00 --> 00:03:02 that, you know, they were And again, this is
00:03:02 --> 00:03:04 not specific just to Google. This happened at
00:03:04 --> 00:03:07 a lot of companies. Companies build in -house
00:03:07 --> 00:03:10 tooling to automate billing, rev -rack, other
00:03:10 --> 00:03:12 financial operations. The reason they build in
00:03:12 --> 00:03:14 -house tooling is because third -party tools
00:03:14 --> 00:03:18 available in the market are not able to capture
00:03:18 --> 00:03:20 or automate the custom use cases that a company
00:03:20 --> 00:03:22 might have and then they feel the need to build
00:03:22 --> 00:03:24 something in -house. They dedicate engineering
00:03:24 --> 00:03:27 resources to building these tools. What I noticed
00:03:27 --> 00:03:30 was that despite having the best in -house tooling,
00:03:30 --> 00:03:33 finance teams were kind of spending a lot of
00:03:33 --> 00:03:36 time just still on spreadsheets, whether it came
00:03:36 --> 00:03:38 to, you know, sending invoices or falling up
00:03:38 --> 00:03:41 for payments or recognizing revenue. And the
00:03:41 --> 00:03:44 reason for that was that, you know, sales teams,
00:03:44 --> 00:03:48 obviously they get creative to close deals. As
00:03:48 --> 00:03:50 sales teams go and close more deals, you know,
00:03:51 --> 00:03:54 the in -house tooling that was built is not able
00:03:54 --> 00:03:57 to keep up pace with how sales team want to structure
00:03:57 --> 00:04:00 their deals. And that then kind of puts the onus
00:04:00 --> 00:04:03 on finance teams to get the job done through
00:04:03 --> 00:04:04 spreadsheets or manually, because at the end
00:04:04 --> 00:04:06 of the day, you have to send out the invoice
00:04:06 --> 00:04:08 at the end of the month, whether you can do it
00:04:08 --> 00:04:11 through your automated tool or you send it out
00:04:11 --> 00:04:13 manually. That is the high tech of the finance
00:04:13 --> 00:04:16 team. This is the era of AI where, you know,
00:04:16 --> 00:04:18 cars drive themselves and you know, you have
00:04:18 --> 00:04:21 robots doing pretty much all kinds of stuff.
00:04:21 --> 00:04:24 And here I was like, why are people still kind
00:04:24 --> 00:04:27 of sending out invoices manually? The reason
00:04:27 --> 00:04:29 that was happening was because most of the tooling
00:04:29 --> 00:04:32 that existed in the market was built like 10,
00:04:32 --> 00:04:35 15 years ago in an era where software pricing
00:04:35 --> 00:04:38 was more per user per month, per seat per month,
00:04:38 --> 00:04:40 but even like a more straight forward license
00:04:40 --> 00:04:43 fee. As software has evolved, you know, with
00:04:43 --> 00:04:46 the advent of AI, there's more consumption based
00:04:46 --> 00:04:48 pricing with sales led notions. You have all
00:04:48 --> 00:04:51 kinds of, you know, fancy ways of structuring
00:04:51 --> 00:04:53 contracts, whether it's discounts, or peers,
00:04:53 --> 00:04:56 or ramps, or milestones, or, you know, early
00:04:56 --> 00:04:58 payment rewards, or leaping. We saw a gap in
00:04:58 --> 00:05:01 the market when it comes to having tooling that
00:05:01 --> 00:05:04 can tackle these use cases and automate billing
00:05:04 --> 00:05:05 in Revelec. So that's kind of what led us to
00:05:05 --> 00:05:09 start Zanskar. Okay. And so what part of billing
00:05:09 --> 00:05:12 and revenue recognition have you automated with
00:05:12 --> 00:05:15 Zentscar, right? Because when we think of SAS
00:05:15 --> 00:05:18 company, especially when 606 came in, the accounting
00:05:18 --> 00:05:21 standard about revenue recognition and all the
00:05:21 --> 00:05:24 milestone and you get money doesn't mean it's
00:05:24 --> 00:05:27 revenue. It's quite complicated. Yeah. Yeah.
00:05:27 --> 00:05:30 So which part were you able to automate? The
00:05:30 --> 00:05:32 end to end. So basically our vision was that
00:05:32 --> 00:05:37 all we need to know is what is the contracts
00:05:37 --> 00:05:41 signed by our customer with their customers?
00:05:41 --> 00:05:43 I'll go back to the example of Google. Let's
00:05:43 --> 00:05:45 say Google was using Zanskar. And Google had
00:05:45 --> 00:05:48 to send an invoice to their customers. Google
00:05:48 --> 00:05:50 had to recognize revenue based on the contract
00:05:50 --> 00:05:53 signed with their customers. All Zanskar needs
00:05:53 --> 00:05:57 to know is the information in the contract. This
00:05:57 --> 00:05:58 is a contract signed between Google and their
00:05:58 --> 00:06:00 customers. It determines how their customers
00:06:00 --> 00:06:03 pay them, whether it's a subscription fee, it's
00:06:03 --> 00:06:05 a, you know, tiered fee, there's a discount.
00:06:06 --> 00:06:08 Along with the contract, there could be some
00:06:08 --> 00:06:11 elements of usage, how much data was stored,
00:06:11 --> 00:06:14 how many API calls were hit, how many emails
00:06:14 --> 00:06:17 were sent. As long as Zanskar has the information
00:06:17 --> 00:06:20 in the contract and information about usage,
00:06:21 --> 00:06:24 everything else downstream is automated. The
00:06:24 --> 00:06:27 generation of invoices, the sending of invoices,
00:06:27 --> 00:06:31 the application of payments, the sending of kind
00:06:31 --> 00:06:35 of follow -ups for payments, Based on the contracts,
00:06:35 --> 00:06:39 you know, on 606 or IFRS 15, creating your revenue
00:06:39 --> 00:06:41 schedules, recognizing revenue, creating your
00:06:41 --> 00:06:44 journals, creating your journal entries, posting
00:06:44 --> 00:06:47 your journal entries into your ERP. That end
00:06:47 --> 00:06:50 -to -end process is fully automated. Again, the
00:06:50 --> 00:06:53 vision was that we live in an era where machines
00:06:53 --> 00:06:57 can do pretty much anything. So this entire function
00:06:57 --> 00:07:00 should be able to run by a machine using AI.
00:07:00 --> 00:07:03 Now we're an AI native platform, you know, we
00:07:03 --> 00:07:06 use AI in every part of our stack. All we need
00:07:06 --> 00:07:09 is the information on the contract and the information
00:07:09 --> 00:07:11 about users and everything downstream is fully
00:07:11 --> 00:07:15 up. That's awesome. And it brings me back to,
00:07:16 --> 00:07:18 I know this is a great tool, but, and I know
00:07:18 --> 00:07:22 you talk with a lot of CFOs, right? And we often
00:07:22 --> 00:07:25 come in and we either inherit a great tool that
00:07:25 --> 00:07:29 was now well implemented. The thing can have
00:07:29 --> 00:07:33 all the good thing it can do, but the implementation
00:07:33 --> 00:07:35 is such like, it's like you're buying a Mercedes
00:07:35 --> 00:07:38 and you're driving like a golf cart. It's painful,
00:07:38 --> 00:07:40 like you said, because sales team go out, they
00:07:40 --> 00:07:43 do deals and accounting and finance, we're trying
00:07:43 --> 00:07:46 to catch up. So what would be your, you know,
00:07:46 --> 00:07:48 maybe your framework or your advice really for
00:07:48 --> 00:07:51 implementing any tool and make sure that we're
00:07:51 --> 00:07:55 using it to its full potential? Again, we've
00:07:55 --> 00:07:57 seen, we've done countless implementations. So
00:07:57 --> 00:08:00 I do have a few lessons to share. I'd say first
00:08:00 --> 00:08:03 of all, just knowing exactly the entire, like
00:08:03 --> 00:08:05 getting all stakeholders in the company on the
00:08:05 --> 00:08:09 same page about implementing a new tool, whether
00:08:09 --> 00:08:11 it is, you know, the sales team, whether it is
00:08:11 --> 00:08:13 the engineering team, whether it is the finance
00:08:13 --> 00:08:16 team, implementation should not be done in silo
00:08:16 --> 00:08:18 with one team. Others should be kept in loop
00:08:18 --> 00:08:20 from day one that here this tool is coming in.
00:08:21 --> 00:08:23 Understanding. everything that the tool has to
00:08:23 --> 00:08:25 offer, not necessarily using everything that
00:08:25 --> 00:08:27 they have to offer. You could choose to use a
00:08:27 --> 00:08:31 few specific modules, but at least knowing that,
00:08:31 --> 00:08:33 hey, this tool can do these five things for me.
00:08:34 --> 00:08:36 And internally, all stakeholders aligning one.
00:08:36 --> 00:08:38 Okay, we'll start with using module number one
00:08:38 --> 00:08:40 and module number two. Let's see, we're using
00:08:40 --> 00:08:42 Ranskerl. You want to start with billing, start
00:08:42 --> 00:08:44 with the dev rec. You don't want to use collections
00:08:44 --> 00:08:46 today. You don't want to use, you know, reporting
00:08:46 --> 00:08:49 today. That can be a phase two or a phase three.
00:08:49 --> 00:08:50 You may choose to never use that. So just having
00:08:50 --> 00:08:54 full clarity on what the tool offers and what
00:08:54 --> 00:08:57 different stakeholders in the company expect
00:08:57 --> 00:09:00 from automation. The tool could offer, let's
00:09:00 --> 00:09:03 say, an integration with your CRM, which is HubSpot
00:09:03 --> 00:09:07 or Salesforce. But if the sales team tells you
00:09:07 --> 00:09:09 that, hey, the CRM hygiene is not very good.
00:09:09 --> 00:09:12 I don't have information in a good way or shape
00:09:12 --> 00:09:15 in HubSpot or in Salesforce. Then the finance
00:09:15 --> 00:09:18 team trying to get Zanskar connected to the CRM
00:09:18 --> 00:09:21 is just a useless. It doesn't make any sense.
00:09:21 --> 00:09:23 You might as well just have the PDF of the contract
00:09:23 --> 00:09:26 uploaded to Zanskar and have that kind of read
00:09:26 --> 00:09:29 it. That's why it's important to have all internal
00:09:29 --> 00:09:31 stakeholders aligned on, you know, what data
00:09:31 --> 00:09:34 do we have? Where does it live? and making sure
00:09:34 --> 00:09:37 the vendor that you work with gives you like
00:09:37 --> 00:09:40 a detailed implementation plan broken down by
00:09:40 --> 00:09:44 phases on what will happen on day one week one
00:09:44 --> 00:09:46 month one. How long will it take? What are the
00:09:46 --> 00:09:49 different phases? What is needed from different
00:09:49 --> 00:09:51 stakeholders in the company at what point in
00:09:51 --> 00:09:54 time doing all of this takes two to three days.
00:09:54 --> 00:09:58 It gets different stakeholders on the same page.
00:09:58 --> 00:10:01 It saves so much headache later because we've
00:10:01 --> 00:10:04 seen places where you know The finance team starts
00:10:04 --> 00:10:06 implementing the tool and they'll be like, you
00:10:06 --> 00:10:08 know what, you can just extract the consumption
00:10:08 --> 00:10:11 data from my snowflake. They've not spoken to
00:10:11 --> 00:10:13 engineering and then three weeks later engineering
00:10:13 --> 00:10:14 says, you know what, we're not going to give
00:10:14 --> 00:10:17 access to the snowflake. Even read -only access
00:10:17 --> 00:10:19 will not be given. We need to use the APIs to
00:10:19 --> 00:10:23 send data. And that increases the implementation
00:10:23 --> 00:10:25 timeline because they were not looped in on day
00:10:25 --> 00:10:27 one. So getting everybody looped in on day one
00:10:27 --> 00:10:30 is very, very important. I love it. I love that
00:10:30 --> 00:10:33 approach because I can see how it prevents a
00:10:33 --> 00:10:35 lot of headaches. I've seen and I feel like we
00:10:35 --> 00:10:38 all learn implementation the hard way. Now you
00:10:38 --> 00:10:41 have an engineering background. How do you think
00:10:41 --> 00:10:45 it really helped you coming into making the lives
00:10:45 --> 00:10:49 of finance people easier? Honestly, the engineering
00:10:49 --> 00:10:51 background helps because I understand the fundamentals
00:10:51 --> 00:10:54 of technology. I understand how the software
00:10:54 --> 00:10:57 works. the intricacies of it, not just what outcomes
00:10:57 --> 00:11:01 it delivers for the end user, which is, you know,
00:11:01 --> 00:11:04 somebody in the finance team, but also how the
00:11:04 --> 00:11:07 software works under the hood. So when I'm in
00:11:07 --> 00:11:09 a conversation with somebody and, you know, they're
00:11:09 --> 00:11:13 like, Hey, can you get data from this stable
00:11:13 --> 00:11:16 in a warehouse? And can it be aggregated in a
00:11:16 --> 00:11:19 certain way? Or, you know, can I have my revenue
00:11:19 --> 00:11:21 distributed based on usage or can it be more
00:11:21 --> 00:11:24 straight mind? Given that I understand how the
00:11:24 --> 00:11:28 tool works from an engineering perspective, the
00:11:28 --> 00:11:32 ability to connect the how, which is the tech
00:11:32 --> 00:11:35 layer, with the what, which is the outcome that
00:11:35 --> 00:11:37 is being produced, which is the invoice of the
00:11:37 --> 00:11:40 journal entry. There's no disconnect because
00:11:40 --> 00:11:42 I understand both of those parts and thereby,
00:11:42 --> 00:11:44 you know, I'm able to kind of marry those two
00:11:44 --> 00:11:47 and give a very solidified holistic perspective
00:11:47 --> 00:11:50 to the end user. That's awesome. And I think
00:11:50 --> 00:11:54 in addition to You coming from an engineering
00:11:54 --> 00:11:56 background. You also come from another country
00:11:56 --> 00:12:00 That's like the top in how many engineers I think
00:12:00 --> 00:12:04 was square mile So what was that transition for
00:12:04 --> 00:12:08 you? I'm curious coming to the us and learning
00:12:08 --> 00:12:11 Because you knew already the engineering by the
00:12:11 --> 00:12:13 working with people because that's what finance
00:12:13 --> 00:12:17 business partners are today With ai with so many
00:12:17 --> 00:12:20 things the hard work for us is more and more
00:12:20 --> 00:12:23 becoming dealing with people. So what is your
00:12:23 --> 00:12:26 take on it? What has helped you in bringing people
00:12:26 --> 00:12:29 on board with AI, with different technology and
00:12:29 --> 00:12:32 just work in general? Yeah, I mean, I think it
00:12:32 --> 00:12:35 just boils down to understanding the culture,
00:12:35 --> 00:12:39 which I came to the US for my MBA, which was
00:12:39 --> 00:12:42 about nine, 10 years ago. So at that time, of
00:12:42 --> 00:12:45 course, I was a little bit of a cultural difference
00:12:45 --> 00:12:47 but you know I made good friends with all of
00:12:47 --> 00:12:50 my classmates at business school understood the
00:12:50 --> 00:12:53 cultural nuances understood you know how people
00:12:53 --> 00:12:56 what people like what they dislike how they talk
00:12:56 --> 00:12:58 what they like to eat you know where they like
00:12:58 --> 00:13:01 to go what music they listen to you know what
00:13:01 --> 00:13:05 sport they watch and uh as I kind of became more
00:13:05 --> 00:13:07 ingrained with the culture it helped me kind
00:13:07 --> 00:13:11 of build a rapport honestly it wasn't that hard
00:13:11 --> 00:13:13 because The cultural differences aren't that
00:13:13 --> 00:13:17 significant. It didn't take that long to kind
00:13:17 --> 00:13:19 of assimilate. It was maybe like a few weeks,
00:13:20 --> 00:13:22 a couple of months here and there. Now, obviously,
00:13:23 --> 00:13:25 you know, you mentioned AI and getting people
00:13:25 --> 00:13:28 on board with AI. There's like a mixed bag. Some
00:13:28 --> 00:13:31 people like want to incorporate AI. Some people
00:13:31 --> 00:13:34 are like scared of AI, not just because of, okay,
00:13:35 --> 00:13:37 it may eliminate jobs, which it is, but also,
00:13:37 --> 00:13:39 you know, how safe is it? How secure is our data?
00:13:39 --> 00:13:43 Is our data being used to train models? Once
00:13:43 --> 00:13:46 you understand what people think about the latest
00:13:46 --> 00:13:49 technology, what they're looking forward to,
00:13:49 --> 00:13:51 what they're skeptical about, especially what
00:13:51 --> 00:13:53 they're skeptical about, which could be security,
00:13:53 --> 00:13:56 privacy, you know, elimination of jobs, accuracy,
00:13:57 --> 00:14:00 and knowing how it works under the hood, you're
00:14:00 --> 00:14:02 able to kind of paint a true holistic picture
00:14:02 --> 00:14:05 of, okay, your data will be used in so -and -so
00:14:05 --> 00:14:08 way. it's going to be used to automate your workflow,
00:14:08 --> 00:14:10 but it won't be used to train a model that's
00:14:10 --> 00:14:12 helping to automate somebody else's workflows.
00:14:12 --> 00:14:16 And the data is not being exposed outside the
00:14:16 --> 00:14:19 environment that's meant for you. It's safe,
00:14:19 --> 00:14:22 it's secure, and just understanding people's
00:14:22 --> 00:14:24 apprehensions and understanding how technology
00:14:24 --> 00:14:26 works helps you alleviate those apprehensions
00:14:26 --> 00:14:30 much better. Yes, yes, definitely. I like how
00:14:30 --> 00:14:33 you said understanding, but also it means being
00:14:33 --> 00:14:36 open, right? Because when we think about the
00:14:36 --> 00:14:39 modern CFO tech stack. We used to what, having
00:14:39 --> 00:14:43 an accounting system, maybe a FPNA software,
00:14:44 --> 00:14:48 and we should be good. Well, in reality, it's
00:14:48 --> 00:14:51 a bit more complicated than that. Like what has
00:14:51 --> 00:14:54 been your experience and what do you think the
00:14:54 --> 00:14:57 ideal CFO tech stack should look like? See, I
00:14:57 --> 00:15:00 mean, the CFO tech stack can be as fragmented
00:15:00 --> 00:15:03 as you'd like it to be. and, or you could consolidate
00:15:03 --> 00:15:06 everything in one place. I feel like it's hard
00:15:06 --> 00:15:08 to consolidate all the functions into one tool.
00:15:09 --> 00:15:11 You do need specialist tooling for different
00:15:11 --> 00:15:15 functions. So whether it's your code to cache,
00:15:15 --> 00:15:17 which is where Zanskar comes in, whether it's
00:15:17 --> 00:15:20 let's say your receivables function, you need
00:15:20 --> 00:15:23 that tooling for FPNA for your planning and forecasting
00:15:23 --> 00:15:25 and coding. You need an ERP of course. You need
00:15:25 --> 00:15:29 a a payroll provider, you need a expense management
00:15:29 --> 00:15:32 tool, a payables management tool, of course a
00:15:32 --> 00:15:36 closing tool, tags. There's like tools for, it's
00:15:36 --> 00:15:38 so easy to spin up a tool today. There's tools
00:15:38 --> 00:15:40 for literally every part in the function. I think
00:15:40 --> 00:15:43 the ideal stack varies for every company. It
00:15:43 --> 00:15:46 depends upon the stage of the company, the maturity
00:15:46 --> 00:15:49 of the company. You don't need to automate everything.
00:15:50 --> 00:15:52 You should automate things that are becoming
00:15:52 --> 00:15:56 a pain to do manually. Automation is important,
00:15:56 --> 00:15:58 but not for everything until it becomes a pain
00:15:58 --> 00:16:00 point. So it's important to understand, okay,
00:16:00 --> 00:16:03 these are the different parts of this tech. What
00:16:03 --> 00:16:05 are the key pain points for me today? Like, you
00:16:05 --> 00:16:08 know, is my business is growing. The volume of
00:16:08 --> 00:16:10 customers is increasing. I don't want to run
00:16:10 --> 00:16:13 billing manually, but if you only have 10 customers,
00:16:14 --> 00:16:16 do you really want to implement the billing tool?
00:16:16 --> 00:16:17 You can, and let's say they're all on Android
00:16:17 --> 00:16:20 Invoicing. You could send out 10 invoices on
00:16:20 --> 00:16:22 email. It takes you 10 minutes. You don't need
00:16:22 --> 00:16:25 to automate that. So it's very contextual. but
00:16:25 --> 00:16:28 say the various parts of the stack. And today
00:16:28 --> 00:16:31 I think the one other fundamental question is
00:16:31 --> 00:16:34 how much of AI should I be using in my tech stack?
00:16:35 --> 00:16:38 AI is evolving so rapidly, the pace at which
00:16:38 --> 00:16:41 things are changing is so transformative that
00:16:41 --> 00:16:43 what you have today versus what you have in the
00:16:43 --> 00:16:44 year will be so different. So you want tools
00:16:44 --> 00:16:49 that are AI natives and are using the latest
00:16:49 --> 00:16:50 technology to make sure the business is huge
00:16:50 --> 00:16:53 because we have competitors. have an edge over
00:16:53 --> 00:16:55 you let's say using outdated tooling and your
00:16:55 --> 00:16:57 competitors are using tooling that helps them
00:16:57 --> 00:17:01 do a lot more with the same amount of headcount
00:17:01 --> 00:17:03 and the same amount of investment in tooling
00:17:03 --> 00:17:05 that they're going to kind of be more productive
00:17:05 --> 00:17:07 than you and they can do a lot more they'll move
00:17:07 --> 00:17:08 ahead of you so that's what's also important
00:17:08 --> 00:17:12 to have you know so if i kind of broke it down
00:17:12 --> 00:17:15 code to cache of course there's nscar ftna there's
00:17:15 --> 00:17:19 tools like drive crane there's mosaic ERP for
00:17:19 --> 00:17:21 SaaS companies. Of course, you have the QuickBooks
00:17:21 --> 00:17:24 in the NetSuite, but you also have the more wage,
00:17:24 --> 00:17:27 you know, campfires and rivets. Closing, of course,
00:17:27 --> 00:17:30 there's tools like Floca's. Tax, you have Avalara,
00:17:30 --> 00:17:33 but you also have tools like Android and SaaS
00:17:33 --> 00:17:37 Focus. Yeah, again, it also boils down to kind
00:17:37 --> 00:17:40 of figuring out what parts of my stack do I want
00:17:40 --> 00:17:42 to automate, but also what parts of my stack
00:17:42 --> 00:17:45 do I want to eliminate. like you may have a manual
00:17:45 --> 00:17:48 closed checklist that could be eliminated. You
00:17:48 --> 00:17:50 may have static board decks. You don't need static
00:17:50 --> 00:17:52 board tests anymore. Those could be eliminated.
00:17:53 --> 00:17:57 At one point you talked about AI native. What
00:17:57 --> 00:18:01 is the AI native tool? What does it look like?
00:18:01 --> 00:18:05 What does it do? Why is it valuable? An AI native
00:18:05 --> 00:18:12 tool is a tool that leverages the latest in AI.
00:18:12 --> 00:18:17 To solve the same problems that you have been
00:18:17 --> 00:18:21 solving for decades, either manually or through
00:18:21 --> 00:18:24 more legacy tooling. An example of that would
00:18:24 --> 00:18:26 be, let's say you're using a billing tool where,
00:18:27 --> 00:18:29 let's say you're a company that sells contracts,
00:18:30 --> 00:18:33 that has a sales led motion. Your contracts are
00:18:33 --> 00:18:36 not in a CRM because your sales team has poor
00:18:36 --> 00:18:40 data hygiene. Your contracts are in... Google
00:18:40 --> 00:18:43 Drive, you have 200 customers, you have 200 contracts
00:18:43 --> 00:18:46 in a Google Drive. Let's say you implemented
00:18:46 --> 00:18:49 a billing tool. Now, if the tool was not AI native,
00:18:50 --> 00:18:54 you would have to open every contract, read the
00:18:54 --> 00:18:56 information on the contract and manually punch
00:18:56 --> 00:18:58 it into the billing tool. And once the contact
00:18:58 --> 00:19:00 is in the billing tool, the billing tool generates
00:19:00 --> 00:19:04 the inputs. So this is an example of a non AI
00:19:04 --> 00:19:07 tool. An AI native tool will solve the same problem,
00:19:07 --> 00:19:10 but instead of you having to read the contract
00:19:10 --> 00:19:13 200 times and manually punch in the data, you
00:19:13 --> 00:19:16 can upload the contract to the AI native building
00:19:16 --> 00:19:19 tool. It would be able to extract the terms in
00:19:19 --> 00:19:23 the contract, read them automatically using AI,
00:19:23 --> 00:19:26 the latest in AI, and then generate your ring
00:19:26 --> 00:19:28 voices. So again, you're solving the same problem,
00:19:28 --> 00:19:30 but with the AI native tool, you're saving more
00:19:30 --> 00:19:34 time because AI has now done the job that you
00:19:34 --> 00:19:37 were doing. of manually inputting data into the
00:19:37 --> 00:19:40 tool. That's a very small example. But what I
00:19:40 --> 00:19:43 mean by an AI native tool is it is leveraging
00:19:43 --> 00:19:49 AI to solve a lot of the manual workflows that
00:19:49 --> 00:19:52 still exist despite you having to use software.
00:19:53 --> 00:19:55 Another example could be, you know, let's say
00:19:55 --> 00:19:59 you are again having a billing tool. You wanted
00:19:59 --> 00:20:03 it to kind of reconcile which invoices got paid
00:20:03 --> 00:20:06 and which didn't get paid. Now you connect it
00:20:06 --> 00:20:09 to your ERP, the ERP is connected to your bank.
00:20:09 --> 00:20:12 They tell the payment information syncs in, and
00:20:12 --> 00:20:15 now you have to kind of manually apply the payments
00:20:15 --> 00:20:17 to every invoice to see what the schedule does
00:20:17 --> 00:20:20 not say. The AI native pool will take away that
00:20:20 --> 00:20:24 manual step because it will be smart enough to
00:20:24 --> 00:20:27 understand who the payment was came in from.
00:20:27 --> 00:20:29 Let's say I have three invoices for a customer
00:20:29 --> 00:20:33 for. $100, $150, $200. And there was one payment
00:20:33 --> 00:20:36 that came in from the same customer for $450.
00:20:37 --> 00:20:40 AI is smart enough to know, okay, this $450 is
00:20:40 --> 00:20:43 for both three invoices. So let this be applied
00:20:43 --> 00:20:45 as opposed to you have to do it manually. So
00:20:45 --> 00:20:48 that's what I mean by AI native tool. Oh, great.
00:20:48 --> 00:20:53 This is very helpful. And in terms of company
00:20:53 --> 00:20:56 maturity, you said something about how depending
00:20:56 --> 00:20:59 on where your company is, you will decide to
00:20:59 --> 00:21:02 automate certain things, right? When you think
00:21:02 --> 00:21:05 about a SaaS company, because I know you work
00:21:05 --> 00:21:08 a lot with SaaS companies, at which point do
00:21:08 --> 00:21:10 you think they should focus, like when you look
00:21:10 --> 00:21:13 at their maturity curve, when they're starting,
00:21:13 --> 00:21:16 and when is the peak versus when they're mature,
00:21:17 --> 00:21:19 at which point do you think they need to automate
00:21:19 --> 00:21:23 certain things? that cycle like what things typically
00:21:23 --> 00:21:27 become a pain point so people can also be Proactive
00:21:27 --> 00:21:29 so that the CFO that are let's say at a startup
00:21:29 --> 00:21:32 How can they be proactive knowing that they will
00:21:32 --> 00:21:35 need different things at different stages of
00:21:35 --> 00:21:38 their companies? Yeah, I mean I think the best
00:21:38 --> 00:21:41 way to do this is just to see what is starting
00:21:41 --> 00:21:44 to become a time sync and Whatever is starting
00:21:44 --> 00:21:48 to become a time sync instead of waiting for
00:21:48 --> 00:21:50 the problem to blow up before you automate it
00:21:50 --> 00:21:54 you kind of proactively take that action immediately.
00:21:55 --> 00:21:58 So again, let's say, again, it's not just a function
00:21:58 --> 00:22:00 of company maturity, but it's also a function
00:22:00 --> 00:22:02 of some other company in one. So let's say, you
00:22:02 --> 00:22:05 know, you are two companies, both had 200 customers.
00:22:06 --> 00:22:10 One company had every contract just like a flat
00:22:10 --> 00:22:14 annual see a recurring revenue yearly. Now for
00:22:14 --> 00:22:16 them to recognize the revenue, all the 200 contracts
00:22:16 --> 00:22:20 are just kind of. straight line over 12 months.
00:22:20 --> 00:22:22 There's no fancy distribution happening over
00:22:22 --> 00:22:25 there. So they probably don't need to automate
00:22:25 --> 00:22:28 it until the number blows up to say 500 customers
00:22:28 --> 00:22:31 because it's not taking that long with even 200
00:22:31 --> 00:22:34 customers. It's the same rule, similar spreadsheet
00:22:34 --> 00:22:37 template. However, if you had usage -based pricing,
00:22:37 --> 00:22:39 now you have to recognize revenue based on when
00:22:39 --> 00:22:44 the usage happened. And now the rule based on
00:22:44 --> 00:22:46 which the revenue has to be distributed or recognized
00:22:47 --> 00:22:50 is no longer the same for every customer. You
00:22:50 --> 00:22:53 have to marry it to the consumption data. This
00:22:53 --> 00:22:55 can become a pain point at even 50 customers,
00:22:55 --> 00:22:58 forget 200 customer. So you have to then start
00:22:58 --> 00:23:01 to automate it much sooner. So it's not just
00:23:01 --> 00:23:04 a function of company scale. It's also very nuanced
00:23:04 --> 00:23:06 based on the company's business model and the
00:23:06 --> 00:23:09 company's pricing and the company's go -to -market
00:23:09 --> 00:23:11 motion. Is it more sales led? Is it more product
00:23:11 --> 00:23:14 led? Is it hybrid? Is it across multiple geographies?
00:23:15 --> 00:23:18 Is it all local in the US? the closest proxy
00:23:18 --> 00:23:21 to me is okay which of my functions whether it
00:23:21 --> 00:23:24 is billing or collections or recognition of revenue
00:23:24 --> 00:23:26 or you know reporting or planning or budgeting
00:23:26 --> 00:23:32 or forecasting or payments which of these is
00:23:32 --> 00:23:35 progressively month on month the time investment
00:23:35 --> 00:23:38 is increasing significantly that starts tell
00:23:38 --> 00:23:42 you which of these needs to be automated sooner
00:23:42 --> 00:23:46 than later but yeah Realistically, I would say
00:23:46 --> 00:23:49 companies, they automate billing before they
00:23:49 --> 00:23:53 automate RevREC because RevREC is more internal,
00:23:53 --> 00:23:57 billing is more external. You want your invoices
00:23:57 --> 00:24:00 to go out on time. If you recognize revenue one
00:24:00 --> 00:24:02 or two days later, it's still more internal.
00:24:02 --> 00:24:05 I can live with that. So those are the kind of
00:24:05 --> 00:24:07 factors that come into play when deciding when
00:24:07 --> 00:24:11 to automate. I love it. It's very simple, very
00:24:11 --> 00:24:14 practical. So thanks for sharing that. But I
00:24:14 --> 00:24:18 love the way you understand the pain of doing
00:24:18 --> 00:24:21 revenue recognition after the fact. Okay. So
00:24:21 --> 00:24:24 some of our listeners may not understand the
00:24:24 --> 00:24:26 complexities of SaaS companies when it comes
00:24:26 --> 00:24:29 to revenue recognition, right? Cause I feel like
00:24:29 --> 00:24:32 when I was going to school, a lot of the examples
00:24:32 --> 00:24:34 in the textbook for accounting was a manufacturing
00:24:34 --> 00:24:37 company. You are selling a pen. And so when you
00:24:37 --> 00:24:40 sell the pen, you get the money, you recognize
00:24:40 --> 00:24:43 revenue, big deal. But for SaaS company, it is
00:24:43 --> 00:24:48 not the same. So maybe share a story about what
00:24:48 --> 00:24:52 it means to do rev rec. in that complicated schedule
00:24:52 --> 00:24:55 of you have a customer in the contract, there
00:24:55 --> 00:24:59 is a usage, there is a customer, there is a log,
00:24:59 --> 00:25:01 there is a lot of things that can happen. So
00:25:01 --> 00:25:04 can you speak to the pain points you probably
00:25:04 --> 00:25:07 heard from CFOs going through that where billing
00:25:07 --> 00:25:10 is okay, we're sending the bills on time, and
00:25:10 --> 00:25:15 then revenue is like, eh, we don't know. Let's
00:25:15 --> 00:25:17 take a quick break to talk about a problem most
00:25:17 --> 00:25:21 SaaS finance teams run into. Sales closes a deal
00:25:21 --> 00:25:24 with flexible pricing and custom terms, but the
00:25:24 --> 00:25:27 accounting system isn't built to handle it. So
00:25:27 --> 00:25:30 finance ends up juggling manual fixes, prep sheets,
00:25:30 --> 00:25:33 working with developers just to bill and recognize
00:25:33 --> 00:25:36 revenue correctly. That's where today's sponsor
00:25:36 --> 00:25:39 comes in. With ZenScar, all you have to do is
00:25:39 --> 00:25:41 send your contracts and usage to the platform.
00:25:42 --> 00:25:45 The AI power platform automates everything downstream,
00:25:45 --> 00:25:48 billing, revenue recognition, collections. and
00:25:48 --> 00:25:51 SAS metrics. The best part is Zenskar scales
00:25:51 --> 00:25:54 with you, and their team is hands on every step
00:25:54 --> 00:25:57 of the way. So if that sounds like an upgrade
00:25:57 --> 00:26:00 your team needs, visit zenskar .com slash upgrade
00:26:00 --> 00:26:03 to learn more and book a demo. The link is also
00:26:03 --> 00:26:05 in the show notes. Now let's get back to the
00:26:05 --> 00:26:09 episode. Yeah, no, it can get pretty hairy pretty
00:26:09 --> 00:26:12 quickly. So I'll take an example of a company.
00:26:12 --> 00:26:14 You know, there's a customer of ours. I won't
00:26:14 --> 00:26:17 take their name, but they have The pricing model
00:26:17 --> 00:26:20 is more subscription based but they also have
00:26:20 --> 00:26:23 some elements of usage with every contract and
00:26:23 --> 00:26:26 then they also have this interesting milestone
00:26:26 --> 00:26:29 based pricing. The first milestone is when the
00:26:29 --> 00:26:32 implementation is over and then they have a couple
00:26:32 --> 00:26:36 of other milestones based on when the service
00:26:36 --> 00:26:40 is delivered and what phases you know phase one,
00:26:40 --> 00:26:42 phase two, phase three are different milestones
00:26:42 --> 00:26:45 when it gets completed. So they have these different
00:26:45 --> 00:26:47 types of pricing and if some customers could
00:26:47 --> 00:26:50 just be on usage, some could just be on subscription,
00:26:50 --> 00:26:53 some could be on usage plus subscription, some
00:26:53 --> 00:26:56 could have a milestone based pricing. And so
00:26:56 --> 00:27:00 they had like 250 customers and they had this
00:27:00 --> 00:27:03 multiple spreadsheets with one tab for every
00:27:03 --> 00:27:05 customer. So across multiple spreadsheets, they
00:27:05 --> 00:27:10 have 250 tabs because every customer is a separate
00:27:10 --> 00:27:13 spreadsheet tab. And now Given that the contract
00:27:13 --> 00:27:15 for every customer is different, the way the
00:27:15 --> 00:27:17 revenue is recognized for every customer, the
00:27:17 --> 00:27:19 template is different, the rules are different.
00:27:20 --> 00:27:23 So some of the customers, you know, I think 30,
00:27:23 --> 00:27:25 40 of them are very simple, just like straight
00:27:25 --> 00:27:27 line across 12 months. So there was just the
00:27:27 --> 00:27:30 same rule. But in some other cases, when usage
00:27:30 --> 00:27:34 came in, they had to then get a download of the
00:27:34 --> 00:27:36 usage data from their engineering team every
00:27:36 --> 00:27:39 month. And then they had to, you know, upload
00:27:39 --> 00:27:43 it into the spreadsheet. it was linked to a cell
00:27:43 --> 00:27:46 in the Rebrecht template where, okay, this is
00:27:46 --> 00:27:48 the users that came in with the data which it
00:27:48 --> 00:27:51 comes in. And the rule for users is of course
00:27:51 --> 00:27:53 different because let's say the usage happened
00:27:53 --> 00:27:56 this month. I recognize the revenue for that
00:27:56 --> 00:27:58 usage this month. I'm not gonna straight line
00:27:58 --> 00:28:00 it over 12 months because the usage happens this
00:28:00 --> 00:28:02 month. And then sometimes they had, you know,
00:28:02 --> 00:28:06 minimum commitments. And let's say the, if the
00:28:06 --> 00:28:09 contract with a minimum commitment of $10 .
00:28:09 --> 00:28:12 So for four months, you know, there was no usage.
00:28:12 --> 00:28:14 So I'm just like, you know, taking the minimum
00:28:14 --> 00:28:17 of 10 and straightlining it over four months.
00:28:17 --> 00:28:18 And now suddenly in month five, I have a lot
00:28:18 --> 00:28:22 of usage and the usage exceeds $10 . So now
00:28:22 --> 00:28:24 my minimum goes for a toss. And now I have to
00:28:24 --> 00:28:27 make a judgment call on, you know, the previous
00:28:27 --> 00:28:29 period is already closed. I can't go back and
00:28:29 --> 00:28:32 change the way the revenue was optimized for
00:28:32 --> 00:28:36 that period. So now. period, do I front load
00:28:36 --> 00:28:38 it? Do I kind of distribute it equally? Do I
00:28:38 --> 00:28:41 back load the difference? All those nuances require
00:28:41 --> 00:28:44 manual judgment. And each of these is kind of
00:28:44 --> 00:28:46 put into a formula and a spreadsheet. And that
00:28:46 --> 00:28:48 just made it a living mess. Like when you saw
00:28:48 --> 00:28:52 that spreadsheet, I was just like, holy. I'm
00:28:52 --> 00:28:57 in 257. When I think about it, because I've seen
00:28:57 --> 00:29:01 how internally the sales team will come up with
00:29:01 --> 00:29:05 those great ideas of making it flexible and easy
00:29:05 --> 00:29:08 for the customer. You can do this option, this
00:29:08 --> 00:29:12 option, this option, but every option is a hurdle,
00:29:12 --> 00:29:15 a roadblock for the financing when it comes to
00:29:15 --> 00:29:19 revenue recognition. So how would you say life
00:29:19 --> 00:29:22 is being easier between both finance and sales
00:29:22 --> 00:29:25 team when they're in sync from the beginning
00:29:25 --> 00:29:29 on the revenue recognition and billing? I mean,
00:29:29 --> 00:29:31 if they're in sync, That would mean that, you
00:29:31 --> 00:29:35 know, finance has told sales that, hey, please
00:29:35 --> 00:29:38 don't go and sell anything outside these types
00:29:38 --> 00:29:41 of contracts. Because if you sell something that
00:29:41 --> 00:29:43 is, let's say, finance has created templates
00:29:43 --> 00:29:45 to recognize revenue for five different types
00:29:45 --> 00:29:48 of contracts. Now they've told sales, hey, you
00:29:48 --> 00:29:51 know, please don't sell a sixth type of contract.
00:29:51 --> 00:29:54 That is what it means for finance and sales to
00:29:54 --> 00:29:57 be in sync. That will not work, though, because
00:29:57 --> 00:30:00 sales has to go close deals, rightly so to drive
00:30:00 --> 00:30:04 revenue for the company. When that happens, finance
00:30:04 --> 00:30:07 is happy because revenue came in, but they're
00:30:07 --> 00:30:09 also like, okay, now I have a new type of contract
00:30:09 --> 00:30:12 that I hadn't created a template for and I have
00:30:12 --> 00:30:14 to go back and create a new template and this
00:30:14 --> 00:30:17 is going to increase the time that it takes me
00:30:17 --> 00:30:20 to recognize the revenue and close my books.
00:30:20 --> 00:30:23 But the simplest cases that both teams live in
00:30:23 --> 00:30:26 harmony is when there is, you know, not too much
00:30:26 --> 00:30:28 creativity happening in the sales process. They
00:30:28 --> 00:30:31 have a very set way of selling. Finance has already
00:30:31 --> 00:30:33 accommodated for all those days of selling, and
00:30:33 --> 00:30:35 there's nothing new happening in the business.
00:30:35 --> 00:30:37 But that really happens, honestly. Businesses
00:30:37 --> 00:30:39 grow, they launch new products, they launch new
00:30:39 --> 00:30:43 pricing models. As you go upmarket, right, as
00:30:43 --> 00:30:46 you go acquire enterprise customers, they demand
00:30:46 --> 00:30:48 more creative ways in which they want to purchase,
00:30:49 --> 00:30:51 rightly so. They're paying you a lot of money,
00:30:51 --> 00:30:54 so they want to have flexibility so that... Harmony
00:30:54 --> 00:30:59 is not as easy to find unfortunately Yeah, yeah,
00:30:59 --> 00:31:02 so I was hoping you said with AI it's easier
00:31:02 --> 00:31:05 to add that six contract Like when they go and
00:31:05 --> 00:31:10 they do five and six is easier. Maybe maybe Absolutely.
00:31:10 --> 00:31:13 So that's why that's why I said AI native tooling
00:31:13 --> 00:31:18 like Zanskar makes it easier for sure than if
00:31:18 --> 00:31:20 you had to do it on spreadsheet or if you had
00:31:20 --> 00:31:23 to do it in a tool that was and a more legacy.
00:31:24 --> 00:31:27 The point was there's very few companies where
00:31:27 --> 00:31:31 there is this harmony. So given that sales teams
00:31:31 --> 00:31:33 will get creative, I think the easiest ways of
00:31:33 --> 00:31:36 finance to adapt tooling that does not need them
00:31:36 --> 00:31:38 to push back to sales and they can be they can
00:31:38 --> 00:31:40 tell sales team say throw whatever you want to
00:31:40 --> 00:31:43 throw at me. I got it. I'll take care of it.
00:31:44 --> 00:31:46 For that you need flexible modern AI native tooling.
00:31:47 --> 00:31:50 For sure. What would you say is a recent trend
00:31:50 --> 00:31:53 or shift that you think leaders are not paying
00:31:53 --> 00:31:56 enough attention to? Right because I know we
00:31:56 --> 00:32:00 talk a lot about AI and I feel like with AI Came
00:32:00 --> 00:32:03 also an increase in fraud and cyber security
00:32:03 --> 00:32:06 issues, right because everybody's getting smarter
00:32:06 --> 00:32:08 What have you seen? How are you staying ahead
00:32:08 --> 00:32:13 of things like this? This is not new like every
00:32:13 --> 00:32:17 time the new technology comes there will be security
00:32:17 --> 00:32:21 issues there will be fraud and then cyber security
00:32:21 --> 00:32:24 will also beef up to the next level like it's
00:32:24 --> 00:32:28 the it's a game between the fraudsters and the
00:32:28 --> 00:32:30 cops for the lack of a better word as to who's
00:32:30 --> 00:32:34 kind of ahead in the technology race with all
00:32:34 --> 00:32:36 the changes in AI of course there are security
00:32:36 --> 00:32:40 concerns you have to you know take a measured
00:32:40 --> 00:32:45 approach as to okay how much is the risk is the
00:32:45 --> 00:32:50 risk worth the return that I'm getting in some
00:32:50 --> 00:32:54 cases it's not in which case you should not take
00:32:54 --> 00:32:57 the risk but you know in some cases if what's
00:32:57 --> 00:33:00 at risk is let's say proprietary customer data
00:33:00 --> 00:33:04 you could fear your data being leaked to be used
00:33:04 --> 00:33:07 to train some other model and giving away key
00:33:07 --> 00:33:11 business IP that's a risk you cannot live with
00:33:11 --> 00:33:13 okay let's say I'm trying to create my board
00:33:13 --> 00:33:16 deck I got AI to do some projections Now, there's
00:33:16 --> 00:33:19 a risk I could do the wrong projections, but
00:33:19 --> 00:33:23 it would do it much faster. And that's a risk
00:33:23 --> 00:33:26 I'm okay taking because you know what? It's still
00:33:26 --> 00:33:30 more internal. It's a projection. I got it wrong
00:33:30 --> 00:33:34 because of a risk that I took. But, you know,
00:33:34 --> 00:33:36 if I got it right, I was able to do it much more
00:33:36 --> 00:33:39 smartly, much more sooner with a lot less time
00:33:39 --> 00:33:42 investment. And over there, the risk is not less
00:33:42 --> 00:33:44 than the return. So it's That's kind of how I
00:33:44 --> 00:33:47 look at the risk versus reward trade -offs. Okay,
00:33:47 --> 00:33:50 it's pretty much a balance and how you can mitigate
00:33:50 --> 00:33:54 what comes up. Okay, last few questions. I will
00:33:54 --> 00:33:57 say what was the best career advice you've ever
00:33:57 --> 00:34:01 received? I'd say one was just like always think
00:34:01 --> 00:34:04 long -term. I feel like people underestimate
00:34:04 --> 00:34:07 how much the power of long -term thinking can
00:34:07 --> 00:34:11 help on a day -to -day basis when you as an entrepreneur
00:34:11 --> 00:34:14 are worrying about Hey, my revenue is not going
00:34:14 --> 00:34:18 fast enough, or, you know, some very important
00:34:18 --> 00:34:20 team member decided to leave and I'm feeling
00:34:20 --> 00:34:23 bad about it. If you start to take a long -term
00:34:23 --> 00:34:26 lens and you're like, okay, it's a long journey.
00:34:26 --> 00:34:28 We're building company here. Boom wasn't built
00:34:28 --> 00:34:31 in a day. And, you know, it's like running a
00:34:31 --> 00:34:33 marathon. You fall a couple of times. You get
00:34:33 --> 00:34:35 up, you go back crying. It's, it's okay. And,
00:34:35 --> 00:34:38 you know, that long -term vantage point really
00:34:38 --> 00:34:41 helps me. Like, you know, if you tell yourself,
00:34:41 --> 00:34:43 hey, I have a lot of time. to achieve my goal.
00:34:44 --> 00:34:46 I'm not running a sprint where I have to do things
00:34:46 --> 00:34:49 in a short period of time. It just like puts
00:34:49 --> 00:34:52 things in perspective. So for me, the best advice
00:34:52 --> 00:34:55 was incorporate long -term thinking in how you
00:34:55 --> 00:34:57 make every decision. An example for that would
00:34:57 --> 00:35:01 be I could choose to kind of work with, I have
00:35:01 --> 00:35:04 two options, get a more, you know, a cheaper
00:35:04 --> 00:35:07 option versus a more secure option. Again, depending
00:35:07 --> 00:35:09 upon the risk versus the reward, in some cases
00:35:09 --> 00:35:11 I choose the most secure option because that's
00:35:11 --> 00:35:14 a long -term right thing to do. Even like, you
00:35:14 --> 00:35:17 know, working with customers, thinking about,
00:35:17 --> 00:35:20 hey, my reputation compounds long -term. Short
00:35:20 --> 00:35:22 -term, it's okay if I had some losses here and
00:35:22 --> 00:35:24 there, but long -term thinking really helps.
00:35:24 --> 00:35:27 Yes. And especially today, when I think about
00:35:27 --> 00:35:30 social media and people comparing themselves
00:35:30 --> 00:35:33 of where you are in your career, but then you
00:35:33 --> 00:35:36 realize it's long -term. It's the long term,
00:35:36 --> 00:35:37 you know, you know, you're not chasing shining
00:35:37 --> 00:35:40 out a long term. I love it. My last questions.
00:35:40 --> 00:35:44 What is the career advice you usually give people?
00:35:45 --> 00:35:47 I tell people to follow their gut. Lots of nice
00:35:47 --> 00:35:50 young people come to me and they're like, hey,
00:35:50 --> 00:35:53 should I start a company? Should I, you know,
00:35:53 --> 00:35:56 switch my role? I'm not enjoying marketing. Should
00:35:56 --> 00:35:59 I think of doing sales? Should I go for an MBA?
00:35:59 --> 00:36:02 You know, will it help? Will it help? You know,
00:36:02 --> 00:36:05 what's going to make me more money? my advice
00:36:05 --> 00:36:07 is always say just follow your gut your gut usually
00:36:07 --> 00:36:11 knows what there's no like right or wrong decision
00:36:11 --> 00:36:14 that's how i made a lot of my decisions my gut
00:36:14 --> 00:36:17 told me to start a company it wasn't the most
00:36:17 --> 00:36:19 logical decision to make but it just felt right
00:36:19 --> 00:36:22 i advise often i often am as equal to follow
00:36:22 --> 00:36:25 their gut when they're stuck and not try to like
00:36:25 --> 00:36:29 over optimize like it's funny that day this some
00:36:29 --> 00:36:32 someone called me he's like you know what i've
00:36:32 --> 00:36:35 gotten admitted into Harvard Business School
00:36:35 --> 00:36:39 and Stanford. Which one do I choose? I'm like,
00:36:39 --> 00:36:42 dude, you're like one of X people who's gotten
00:36:42 --> 00:36:47 into both. It's like both of those decisions
00:36:47 --> 00:36:49 are going to be okay. Just like, you know, go
00:36:49 --> 00:36:51 with, it's like, you know, but if I get a better
00:36:51 --> 00:36:54 job, where do I make more money? Is Boston a
00:36:54 --> 00:36:56 better city or should I live in the Bay Area?
00:36:57 --> 00:36:59 And he was like trying to kind of, you know,
00:36:59 --> 00:37:03 create a spreadsheet based decision making framework.
00:37:03 --> 00:37:05 So I'm like, no, that's not how you can make
00:37:05 --> 00:37:10 those decisions. Follow your gut. Yes. Yes. Oh,
00:37:10 --> 00:37:13 wow. I think we have so much information these
00:37:13 --> 00:37:17 days. It's easy to get bogged down by too much
00:37:17 --> 00:37:21 information. So love that advice as well. Well,
00:37:21 --> 00:37:24 thank you so much, Apoor, for being on the show.
00:37:24 --> 00:37:27 I really enjoyed the conversation and the insight
00:37:27 --> 00:37:29 you shared. No, it's great. Great. Thanks for
00:37:29 --> 00:37:32 having me. And, you know, looking forward to,
00:37:32 --> 00:37:36 you know, having more of these. And that's it
00:37:36 --> 00:37:39 for today's episode of The Diary of a CFO. Thank
00:37:39 --> 00:37:41 you so much for tuning in. If you enjoyed the
00:37:41 --> 00:37:44 show, don't forget to like, review, subscribe
00:37:44 --> 00:37:48 and share with others. Our website is TheDiaryOfACFO
00:37:48 --> 00:37:51 .com. That's where you can find all the episodes.
00:37:52 --> 00:37:55 access all the guests' profiles, see their bios
00:37:55 --> 00:37:58 and their social media links. It is also the
00:37:58 --> 00:38:00 place where you can apply to be a guest on the
00:38:00 --> 00:38:03 podcast and have information about how you can
00:38:03 --> 00:38:06 sponsor the show. As always, if there is any
00:38:06 --> 00:38:08 topic you would like me to cover in the future,
00:38:08 --> 00:38:12 just email me at ask at the diary of a CFO .com.
00:38:13 --> 00:38:16 Again, the email is ask at the diary of a CFO
00:38:16 --> 00:38:17 .com. See you soon.
