Lauren Pearl is a CFO, advisor, three-time founder, and educator who teaches financial modeling at NYU Stern's Berkeley Center for Entrepreneurship.
A former software developer turned finance leader, she has helped more than 300 companies build a financial foundation to scale and currently serves as fractional strategic CFO at North Pole Security, focused on AI security for finance leaders.
In this episode, Lauren shares the AI danger triangle framework: three conditions that, when combined, create serious risk for any finance team using AI tools. She explains why startup experience changes how CFOs approach risk, how she onboards into new organizations using a consulting-style diagnostic, and why experience and judgment will become the rarest, most valuable skills in finance as AI makes technical ability more accessible.
She also tells the story of discovering her family business had to close after reading its financial statements for the first time at business school.
Today's episode is sponsored by Lineos: bring your finance lifecycle into one line of sight, from planning to reporting. Learn more at lineos.com
00:00:00 --> 00:00:02 Today's episode is brought to you by Lineos.
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00:00:28 --> 00:00:31 the show notes. Welcome back to the Diary of
00:00:31 --> 00:00:33 a CFO podcast. I'm your host Wassia Kamon,
00:00:33 --> 00:00:35 a sitting CFO with a background in accounting
00:00:35 --> 00:00:37 and FP &A. I started this show because I believe
00:00:37 --> 00:00:39 that the way company build and lead their finance
00:00:39 --> 00:00:42 function determines whether they scale or fall
00:00:42 --> 00:00:45 apart. Each week, I sit down with CFOs, CEOs,
00:00:45 --> 00:00:47 and strategic business partners to talk about
00:00:47 --> 00:00:50 how that actually looks like in real life, building
00:00:50 --> 00:00:52 the right teams, getting systems and controls
00:00:52 --> 00:00:55 in place, partnering with the business, and doing
00:00:55 --> 00:00:57 it all hopefully without burning out. Today I'm
00:00:57 --> 00:00:59 here with Lauren Elizabeth Pearl. Lauren Pearl
00:00:59 --> 00:01:02 is a CFO, advisor, educator, and three -time
00:01:02 --> 00:01:05 founder who believes finance is the most under
00:01:05 --> 00:01:08 thought skill in entrepreneurship. and she spent
00:01:08 --> 00:01:11 her career fixing that. A former software developer,
00:01:11 --> 00:01:13 she has a particular knack for working with engineers,
00:01:13 --> 00:01:16 founders, and translating finance into a language
00:01:16 --> 00:01:19 they can actually use. Through her firm, she
00:01:19 --> 00:01:22 has helped more than 300 great companies build
00:01:22 --> 00:01:25 a financial foundation to scale. Currently, she
00:01:25 --> 00:01:27 serves as Fractional Strategy CFO at North Pole
00:01:27 --> 00:01:30 Security, where her technical background informs
00:01:30 --> 00:01:33 a growing body of work on AI security for finance
00:01:33 --> 00:01:36 leaders. and teaches financial modeling at NYU
00:01:36 --> 00:01:39 Stern's Berkeley Center for Entrepreneurship.
00:01:39 --> 00:01:42 Lauren also writes a newsletter called The Daily
00:01:42 --> 00:01:44 CFO and co -hosts the Growth Money CFO podcast,
00:01:45 --> 00:01:47 which had a pleasure of being a guest recently.
00:01:48 --> 00:01:51 Welcome to the show, Lauren. Thank you, Melzia,
00:01:51 --> 00:01:54 for having me. I'm so psyched to be on your podcast
00:01:54 --> 00:01:56 now that you were such a wonderful guest on ours.
00:01:57 --> 00:02:06 Oh, don't make me turn red. So glad to have you
00:02:06 --> 00:02:09 on I'm so glad we get into you know, actually
00:02:09 --> 00:02:15 finally have this Yeah months I'm so grateful
00:02:15 --> 00:02:17 especially for your career right you started
00:02:17 --> 00:02:20 as a developer So I'm curious to hear how did
00:02:20 --> 00:02:23 you go from developer to accounting and finance?
00:02:24 --> 00:02:27 Yeah, it was kind of a wild ride. I must say
00:02:27 --> 00:02:31 initially like I had no idea I was going to end
00:02:31 --> 00:02:35 up in finance. I started out, as you said, my
00:02:35 --> 00:02:38 first job was on the tech side of things. I was
00:02:38 --> 00:02:41 developing internal web applications for big
00:02:41 --> 00:02:44 companies. And I was coming from a little bit
00:02:44 --> 00:02:45 of a business background because I grew up in
00:02:45 --> 00:02:49 a family business. So that engineering focus
00:02:49 --> 00:02:53 was a bit of rebellion years. And I was working
00:02:53 --> 00:02:56 on the tech side of things. And I liked that,
00:02:56 --> 00:02:59 but I kept feeling pulled to get back into sort
00:02:59 --> 00:03:01 of the business side. I was, uh, in a consulting
00:03:01 --> 00:03:04 environment and I wanted to work with code and
00:03:04 --> 00:03:06 they kept putting me in client meetings because
00:03:06 --> 00:03:08 I was, I was good at kind of the people aspect.
00:03:08 --> 00:03:11 And eventually, um, I got actually pulled back
00:03:11 --> 00:03:14 into my family's business because my father wanted
00:03:14 --> 00:03:18 to retire. And given that I was kind of feeling
00:03:18 --> 00:03:20 a knack for the business side of things, I went
00:03:20 --> 00:03:24 and I did it and I got there and. I had worked
00:03:24 --> 00:03:26 there sort of on and off my entire life. But
00:03:26 --> 00:03:28 what struck me is that things were going wrong
00:03:28 --> 00:03:32 and I had no idea like what was happening, how
00:03:32 --> 00:03:35 to fix it. I just felt really under qualified.
00:03:35 --> 00:03:38 So it was then that I applied to business school
00:03:38 --> 00:03:40 figuring like, okay, if I'm going to take this
00:03:40 --> 00:03:42 business over from my father, I have to know
00:03:42 --> 00:03:43 how to run a business. I'll go to business school
00:03:43 --> 00:03:45 and I'll figure it out and I'll come back. And
00:03:45 --> 00:03:48 when at business school, that was sort of the
00:03:48 --> 00:03:50 moment that I started to get the inkling that
00:03:50 --> 00:03:52 I was going to move into finance. Cause I sat
00:03:52 --> 00:03:56 down and as a fun exercise while learning how
00:03:56 --> 00:03:59 to read financial models, I figured I would read
00:03:59 --> 00:04:02 my family businesses, financial statements to
00:04:02 --> 00:04:04 see like what was going on with the business.
00:04:04 --> 00:04:06 And I had my friend Brian sitting down with me.
00:04:07 --> 00:04:08 He had more experience helping walk me through.
00:04:09 --> 00:04:11 And he looked at me after looking at the statement
00:04:11 --> 00:04:14 and said, Oh my god, Lauren, this business has
00:04:14 --> 00:04:18 to close. And he walked me through just how much
00:04:18 --> 00:04:20 money it was losing, how the inventory was going
00:04:20 --> 00:04:23 out of control, all of these things. And there
00:04:23 --> 00:04:28 was this like aha moment of I have worked alongside
00:04:28 --> 00:04:33 this business for 27 years and I didn't know
00:04:33 --> 00:04:36 as much about it as this person was able to for
00:04:36 --> 00:04:39 looking at the financial statements for one minute.
00:04:39 --> 00:04:43 And it was just this moment of like, oh my gosh,
00:04:44 --> 00:04:46 this is where all the information is. Like if
00:04:46 --> 00:04:48 I want to run a business, this is the language
00:04:48 --> 00:04:51 I have to learn to speak. And so like from that
00:04:51 --> 00:04:54 moment on, I think I started heading more towards
00:04:54 --> 00:04:57 the finance function because I realized the power
00:04:57 --> 00:05:00 that came from sitting with those dashboards,
00:05:00 --> 00:05:02 sitting with the data and understanding what
00:05:02 --> 00:05:05 was actually going on. From there, I had a couple
00:05:05 --> 00:05:07 of other roles. I founded three companies. I
00:05:07 --> 00:05:09 was a management consultant, you know, yada,
00:05:09 --> 00:05:12 yada, yada. But that's really what got me started
00:05:12 --> 00:05:15 in that finance journey. Wow. Amazing. And I
00:05:15 --> 00:05:17 like how you said engineer was like the rebellious
00:05:17 --> 00:05:20 thing. I wouldn't think about engineering for
00:05:20 --> 00:05:22 being rebellious, but okay, very smart woman.
00:05:24 --> 00:05:27 But I'm now I'm curious to hear because saying
00:05:27 --> 00:05:30 that you grew up in a family business already
00:05:30 --> 00:05:33 shaped. how you view the world and then you go
00:05:33 --> 00:05:37 into engineering and then you add actual schooling
00:05:37 --> 00:05:40 on top of it and experience. How would you say
00:05:40 --> 00:05:42 those experiences have really helped you being
00:05:42 --> 00:05:45 the CFO you are today? That's a great question.
00:05:46 --> 00:05:49 I think it's complicated because that's a, you
00:05:49 --> 00:05:52 know, that's, that's sort of a very multifaceted
00:05:52 --> 00:05:57 place that I'm coming from. I think that coming
00:05:57 --> 00:06:02 from a very like multi -discipline nary background
00:06:02 --> 00:06:05 in general as a CFO gives me a very broad view.
00:06:06 --> 00:06:09 I think I get little extra things from each aspect,
00:06:09 --> 00:06:14 right? Like from that sort of brief stint working
00:06:14 --> 00:06:17 on the tech side, I think I got a ton of like
00:06:17 --> 00:06:20 first principles thinking and sort of thinking
00:06:20 --> 00:06:23 about how things are built from the ground up.
00:06:23 --> 00:06:26 And also in working with a lot of engineering
00:06:26 --> 00:06:29 CEOs now, got a little bit of a shared language
00:06:29 --> 00:06:33 that I can now use when explaining how finance
00:06:33 --> 00:06:36 works and why it's useful. Because in fact, one
00:06:36 --> 00:06:38 of the things that's funny about working, especially
00:06:38 --> 00:06:40 with CEOs coming from engineering background
00:06:40 --> 00:06:44 is finances is kind of a crazy system when you
00:06:44 --> 00:06:45 think about it, like the way that accounting
00:06:45 --> 00:06:49 works. It can be, it can feel really wild. for
00:06:49 --> 00:06:54 a CEO to have financial data not be up to date
00:06:54 --> 00:06:56 minute by minute. Like most databases, most modern
00:06:56 --> 00:07:00 databases, you can like know the actuals immediately.
00:07:00 --> 00:07:02 So it's very odd to have a system that you can
00:07:02 --> 00:07:06 only know accurate data like once, like 10 days
00:07:06 --> 00:07:08 after the month, right? So that doesn't make
00:07:08 --> 00:07:11 any sense to most engineering CEOs. But if you
00:07:11 --> 00:07:14 explain that like, oh, this is not just like
00:07:14 --> 00:07:16 a normal database, this is the high assurance
00:07:16 --> 00:07:19 system of record. then they get it, right? Because
00:07:19 --> 00:07:22 it's just a different database type and a different
00:07:22 --> 00:07:24 informational system. And then they can work
00:07:24 --> 00:07:26 within that structure. Yeah, and coming from
00:07:26 --> 00:07:29 a family business, I think it just kind of gives
00:07:29 --> 00:07:32 that sort of ownership mentality. Like that's
00:07:32 --> 00:07:35 something that I've spoken with other CFOs on
00:07:35 --> 00:07:38 the podcast about how they learn to be a CFO.
00:07:38 --> 00:07:41 And many of them, like a surprising number, have
00:07:41 --> 00:07:44 mentioned startup experience as that moment that
00:07:44 --> 00:07:47 They transitioned from someone in accounting,
00:07:47 --> 00:07:51 someone in FP &A into CFO mindset because it
00:07:51 --> 00:07:54 gives you the perspective of like running the
00:07:54 --> 00:07:56 whole business, which is so valuable if you're
00:07:56 --> 00:07:59 going to be the finance leader. Yeah. So I don't
00:07:59 --> 00:08:01 know. I think, I think there's lots of advantages
00:08:01 --> 00:08:04 to having those experiences. Oh, I can, and I
00:08:04 --> 00:08:07 can tell how you care, right? Because you've
00:08:07 --> 00:08:11 seen and you've seen a business, I mean, for
00:08:11 --> 00:08:13 over 20 years, like you said, and you wanted
00:08:13 --> 00:08:18 to succeed. at a different level, right? That's
00:08:18 --> 00:08:21 very amazing. And also when you think about the
00:08:21 --> 00:08:25 startup experience, for those who haven't worked
00:08:25 --> 00:08:28 in a startup, like what can other finance leaders
00:08:28 --> 00:08:32 or CFOs learn from that startup experience? So,
00:08:32 --> 00:08:36 so much. I was actually super surprised when
00:08:36 --> 00:08:38 I started hearing this trend on the Growth Minded
00:08:38 --> 00:08:43 podcast because I kind of figured that a lot
00:08:43 --> 00:08:46 of CFOs at larger companies would have this very
00:08:46 --> 00:08:51 specific path, that they would start out maybe
00:08:51 --> 00:08:54 in banking and then transition to the FP &A team
00:08:54 --> 00:08:58 and then get promoted internally or come from
00:08:58 --> 00:09:01 accounting. And you kind of stay at this big
00:09:01 --> 00:09:05 company space in finance with a capital F function
00:09:05 --> 00:09:10 and eventually become CFOs. And it was so surprising
00:09:10 --> 00:09:13 to hear that a huge number of the CFOs we were
00:09:13 --> 00:09:16 interviewing took a stint at a startup, either
00:09:16 --> 00:09:19 started a company themselves, which is really
00:09:19 --> 00:09:24 cool, or were the CFO of a smaller company. I
00:09:24 --> 00:09:25 think there's a lot of different things that
00:09:25 --> 00:09:28 you get. Ownership of that company, that sense
00:09:28 --> 00:09:32 of what it means to truly be the guy holding
00:09:32 --> 00:09:35 the bag at the end of the day is huge. I think
00:09:35 --> 00:09:37 that's probably like the foundational piece of
00:09:37 --> 00:09:40 it, but there's other stuff too. Like one of
00:09:40 --> 00:09:42 the things I think is really cool about having
00:09:42 --> 00:09:46 a startup background is getting comfortable in
00:09:46 --> 00:09:49 very uncomfortable situations and learning how
00:09:49 --> 00:09:51 to just like make your way through it. So as
00:09:51 --> 00:09:55 a startup CFO, you're put in situations you would
00:09:55 --> 00:09:57 never face as a large company CFO, right? When
00:09:57 --> 00:10:00 you come on in day one as a startup CFO and you're
00:10:00 --> 00:10:03 making your financial model, you have no data.
00:10:03 --> 00:10:08 Like, none, right? You have to make it. People,
00:10:08 --> 00:10:11 when they build financial models, will sometimes
00:10:11 --> 00:10:13 poo -poo at a model that has too many assumptions
00:10:13 --> 00:10:16 that are like, no, these are estimates, and these
00:10:16 --> 00:10:18 aren't real. And in a startup, you don't have
00:10:18 --> 00:10:21 any real numbers. You don't have real assumptions
00:10:21 --> 00:10:24 or historicals. You just have to deal with the
00:10:24 --> 00:10:26 best guesses you have in this moment and move
00:10:26 --> 00:10:31 forward. So learning how to make decisions despite
00:10:31 --> 00:10:35 that huge data gap, That's a really big deal.
00:10:35 --> 00:10:37 And then when you're working in a larger organization,
00:10:38 --> 00:10:40 you'll have much more data. But when you have
00:10:40 --> 00:10:42 a gap like that, it doesn't just stop you in
00:10:42 --> 00:10:44 your tracks because you have that experience
00:10:44 --> 00:10:47 to say, yeah, I know we don't have perfect data
00:10:47 --> 00:10:49 on what's going to happen in two years with this
00:10:49 --> 00:10:52 metric, but you know what? We can kind of extrapolate
00:10:52 --> 00:10:54 it. We can take a guess and correct it later.
00:10:54 --> 00:10:57 We can make a bunch of scenarios based off of
00:10:57 --> 00:10:59 this and plan around it. You know what the options
00:10:59 --> 00:11:02 are and you don't freak out. So I'm sure there's
00:11:02 --> 00:11:04 lots of other things, but those are the two big
00:11:04 --> 00:11:06 ones that come to mind immediately. Yeah. Being
00:11:06 --> 00:11:09 comfortable in uncomfortable situation, like
00:11:09 --> 00:11:11 that's the whole summary of being a CFO these
00:11:11 --> 00:11:17 days. Yeah. We're all uncomfortable. Yeah. So
00:11:17 --> 00:11:20 people look up to you for direction, for clarity,
00:11:20 --> 00:11:23 and you yourself are looking for direction and
00:11:23 --> 00:11:27 clarity. Right. It's so true. This is like a
00:11:27 --> 00:11:31 very weird time to be the chief risk officer
00:11:31 --> 00:11:33 of a company because what do we have right now
00:11:33 --> 00:11:36 other than unknown? So it's like a very relevant
00:11:36 --> 00:11:39 time to really understand how to operate without
00:11:39 --> 00:11:41 a lot of information and just like move the company
00:11:41 --> 00:11:46 forward anyway. Because I really want to dive
00:11:46 --> 00:11:49 a little deeper on the, you know, getting started.
00:11:49 --> 00:11:51 Because I think if it feels, whether you start
00:11:51 --> 00:11:54 up a smaller company, like, how do you get on
00:11:54 --> 00:11:56 board it, right? Because there is usually no
00:11:56 --> 00:11:59 reference point. There is no template to refresh.
00:12:00 --> 00:12:02 There is no, let me take the schedule that so
00:12:02 --> 00:12:06 and so did before me. Like how, like, what is
00:12:06 --> 00:12:08 your routine? Especially because you're a fractional
00:12:08 --> 00:12:11 CFO to different companies. Like, how do you
00:12:11 --> 00:12:13 get started? Actually, the consulting work I've
00:12:13 --> 00:12:16 done with startups has really helped in this
00:12:16 --> 00:12:19 regard, because you do. You get a lot of when
00:12:19 --> 00:12:24 you work with 30 companies in a year, you really
00:12:24 --> 00:12:26 have to get good at quickly getting understanding
00:12:26 --> 00:12:30 of the situation. In my consulting practice,
00:12:30 --> 00:12:33 I developed a bit of a diagnostic process that
00:12:33 --> 00:12:37 I really like to start with each time. And it's
00:12:37 --> 00:12:42 a pretty fast paced, just like data dump and
00:12:42 --> 00:12:44 information gathering that then results in a
00:12:44 --> 00:12:48 report. So again, this is very consulting -y.
00:12:48 --> 00:12:50 So I don't necessarily recommend that a full
00:12:50 --> 00:12:52 -time CFO coming onto the role takes this exact
00:12:52 --> 00:12:54 process, because you might get looked at a little
00:12:54 --> 00:12:58 funny. But what I've done is I'll come in. I
00:12:58 --> 00:13:01 ask for standard reporting, all of the financial
00:13:01 --> 00:13:04 statements, any model that they currently have.
00:13:04 --> 00:13:07 But I'll also ask for um, and review a lot of
00:13:07 --> 00:13:09 documents that have to do with the operations
00:13:09 --> 00:13:11 of the company. Like what are the processes you
00:13:11 --> 00:13:14 have documented? Uh, what does your org chart
00:13:14 --> 00:13:18 look like? Um, what are the large, like your,
00:13:18 --> 00:13:21 if you are comfortable sharing like your customer
00:13:21 --> 00:13:23 list of like who you're serving, any of your
00:13:23 --> 00:13:26 kind of key marketing docs that help people who
00:13:26 --> 00:13:29 are trying to understand how you go to market,
00:13:29 --> 00:13:31 like what do you, what do you give them to ramp
00:13:31 --> 00:13:34 up on that profile target customer? And then
00:13:34 --> 00:13:37 also who you are to them. So kind of every document
00:13:37 --> 00:13:40 I can get around like, who is this company? But
00:13:40 --> 00:13:42 then the place I get the most information is
00:13:42 --> 00:13:45 I ask to do interviews with key folks. So I want
00:13:45 --> 00:13:47 to interview. Again, it's a startup. So advantage
00:13:47 --> 00:13:50 of coming on board to a startup is you can talk
00:13:50 --> 00:13:52 to five people and that might be half the company.
00:13:52 --> 00:13:55 So that's great. So I'll try to speak to somewhere
00:13:55 --> 00:13:57 between three to five folks and have like an
00:13:57 --> 00:13:59 hour to two hour interview with them where I'm
00:13:59 --> 00:14:02 asking them questions about their role, about
00:14:02 --> 00:14:04 the company, about the stuff that's going on.
00:14:04 --> 00:14:07 great, the stuff that's going wrong, and just
00:14:07 --> 00:14:10 really get a view into their specific world as
00:14:10 --> 00:14:15 an individual. From there, I put together, essentially,
00:14:16 --> 00:14:19 this report. And the report will first talk about
00:14:19 --> 00:14:23 what things I heard and found. I've seen a lot
00:14:23 --> 00:14:25 of different companies, so I'm kind of pattern
00:14:25 --> 00:14:28 matching to what stuff sounds like. You're doing
00:14:28 --> 00:14:31 best practices. What stuff sounds like, oh, this
00:14:31 --> 00:14:33 is really funky. What stuff sounds like, oh,
00:14:33 --> 00:14:35 this is really interesting, like different and
00:14:35 --> 00:14:37 good, and kind of putting that stuff into that
00:14:37 --> 00:14:40 bucket. And then I try to come up with a thesis,
00:14:40 --> 00:14:42 sort of a recommendation given everything that
00:14:42 --> 00:14:46 I've heard on if I could only do three to five
00:14:46 --> 00:14:49 things in this company, like over the next year,
00:14:50 --> 00:14:53 what would be the most high impact stuff to focus
00:14:53 --> 00:14:55 on for this company? And then, you know, this
00:14:55 --> 00:14:57 is a report for consulting. So I also try to
00:14:57 --> 00:14:59 give them like a step -by -step guide and like,
00:14:59 --> 00:15:00 okay, if you want to do these three to five things,
00:15:00 --> 00:15:02 here's exactly the steps you should take. So
00:15:02 --> 00:15:03 they could take that report and work with me
00:15:03 --> 00:15:06 or work with another consultant to get those
00:15:06 --> 00:15:08 things done or their internal team or what have
00:15:08 --> 00:15:10 you. I do think this is kind of an interesting
00:15:10 --> 00:15:12 idea for folks coming on full time. I've spoken
00:15:12 --> 00:15:15 with a couple of CFOs on the podcast who've done
00:15:15 --> 00:15:18 something actually kind of similar, like CFO.
00:15:18 --> 00:15:21 Carla Cooper, I think came from an investor relations
00:15:21 --> 00:15:24 background and she wrote a prospectus on a company
00:15:24 --> 00:15:27 when she was coming in to help her get her head
00:15:27 --> 00:15:30 around it. So I think having enough information
00:15:30 --> 00:15:33 and trying to use that to form an opinion is
00:15:33 --> 00:15:37 really, really, really powerful when coming into
00:15:37 --> 00:15:39 an org, if you can do it. But the final thing
00:15:39 --> 00:15:42 I'll say about just context and coming into an
00:15:42 --> 00:15:44 org that I think is also worth mentioning at
00:15:44 --> 00:15:48 the same time is This has been really powerful
00:15:48 --> 00:15:50 for me and it sounds like for Carla. The other
00:15:50 --> 00:15:53 thing I keep hearing again and again from CFOs
00:15:53 --> 00:15:56 is when coming into an organization, the importance
00:15:56 --> 00:15:59 of patience. I've heard this from, I remember
00:15:59 --> 00:16:01 Rita Colley was one of the folks who mentioned
00:16:01 --> 00:16:04 that she was a regional CFO for Roche Pharmaceuticals
00:16:04 --> 00:16:06 and would move often from region to region. And
00:16:06 --> 00:16:08 she had talked about her way that she came on
00:16:08 --> 00:16:12 board in any given location. And she said her
00:16:12 --> 00:16:15 kind of lesson learned over and over again was
00:16:15 --> 00:16:19 to wait before taking any action or making any
00:16:19 --> 00:16:22 changes if she could, because however much she
00:16:22 --> 00:16:25 thought she knew, something would always surprise
00:16:25 --> 00:16:28 her. If it is just something about the local
00:16:28 --> 00:16:31 context or the particular unit or the team, like
00:16:31 --> 00:16:33 something was not as she expected. And so she
00:16:33 --> 00:16:36 always made a rule to try and not take any big
00:16:36 --> 00:16:39 actions like for a month end and to spend that
00:16:39 --> 00:16:41 first month exclusively listening if she could.
00:16:42 --> 00:16:45 which I think is also a counterpoint to the go
00:16:45 --> 00:16:47 write a thesis and figure out all the things
00:16:47 --> 00:16:50 you want to change. That is so hard though, but
00:16:50 --> 00:16:54 it's a very fine balance because the reason you
00:16:54 --> 00:16:57 hired as a CFO is usually because we want you
00:16:57 --> 00:16:59 to fix something. We want to go to the next level.
00:16:59 --> 00:17:01 There is a reason why you were hired and it wasn't
00:17:01 --> 00:17:05 just to stand still. The pressure, whether it's
00:17:05 --> 00:17:09 from the board or the CEO, the expectations is
00:17:09 --> 00:17:13 for you. to do stuff but yes once you get in
00:17:13 --> 00:17:16 I learned it in my own journey um first off I
00:17:16 --> 00:17:19 learned that the by the time the information
00:17:19 --> 00:17:22 comes to you it is often filtered yeah so you
00:17:22 --> 00:17:26 don't always know you have that big picture you
00:17:26 --> 00:17:28 don't always know all the details all the duct
00:17:28 --> 00:17:31 tape that's around a process like the foundation
00:17:31 --> 00:17:34 has a lot of duct tape you try to rip it off
00:17:34 --> 00:17:40 we may all fall right there for a reason. So
00:17:40 --> 00:17:44 I'm also curious, given your developer background,
00:17:44 --> 00:17:46 where, you know, you get data, you get a quote,
00:17:46 --> 00:17:49 a code, you there, how do you balance that need
00:17:49 --> 00:17:54 for speed, but also the need to deeply understand
00:17:54 --> 00:17:58 things before you change anything? Yeah. I think
00:17:58 --> 00:18:02 when I come into a new space, especially in a
00:18:02 --> 00:18:06 full -time role, I. I'm doing that analysis in
00:18:06 --> 00:18:08 my head of like building a hypothesis. And it's
00:18:08 --> 00:18:11 funny, actually consulting background is really
00:18:11 --> 00:18:13 helpful for building kind of a framework around
00:18:13 --> 00:18:16 this, like the management consulting, like big
00:18:16 --> 00:18:18 management consulting background. Because when
00:18:18 --> 00:18:21 you're first learning how to get into a firm,
00:18:21 --> 00:18:23 they give you case interviews. In case interviews,
00:18:24 --> 00:18:27 you essentially have to solve like a gigantic
00:18:27 --> 00:18:30 global crazy problem with very little information.
00:18:30 --> 00:18:35 And you need to kind of do it. quickly. And so
00:18:35 --> 00:18:38 the same principles, I think, from this kind
00:18:38 --> 00:18:41 of case interview end up being really applicable
00:18:41 --> 00:18:43 when you're coming into a new role where you're
00:18:43 --> 00:18:47 sort of, you get kind of the problem with like
00:18:47 --> 00:18:50 the baseline information and immediately you
00:18:50 --> 00:18:52 start to form a hypothesis, but it is not your
00:18:52 --> 00:18:56 conclusion, right? So you from day one are trying
00:18:56 --> 00:18:58 to like build the hypothesis and then you're
00:18:58 --> 00:19:01 pulling on information and trying to gather information
00:19:01 --> 00:19:06 or deduce information to either prove your hypothesis
00:19:06 --> 00:19:11 or disprove your hypothesis and change it as
00:19:11 --> 00:19:14 you go. And it means that you can get to that
00:19:14 --> 00:19:18 solution more quickly and kind of progress in
00:19:18 --> 00:19:20 and in and in until you've like narrowed in on
00:19:20 --> 00:19:23 it. I think that when I'm coming into an organization,
00:19:23 --> 00:19:25 I'm kind of thinking about things the same way.
00:19:26 --> 00:19:29 I'm not preventing myself from having any gut
00:19:29 --> 00:19:31 instincts around what's going on. I am trying
00:19:31 --> 00:19:36 to guess at it and just build confidence in that
00:19:36 --> 00:19:39 guess as I go along. And I think if you take
00:19:39 --> 00:19:42 that skill and then you pair it with identifying
00:19:42 --> 00:19:45 things that feel like easy, quick wins, That's
00:19:45 --> 00:19:46 the key. So as you're going along, you've got
00:19:46 --> 00:19:48 your hypothesis of sort of like what's going
00:19:48 --> 00:19:51 on, where the value is, where the big problems
00:19:51 --> 00:19:53 are. And there's going to be gigantic stuff you
00:19:53 --> 00:19:56 can change. That's going to be huge risk. It
00:19:56 --> 00:19:58 might be the duct tape you're ripping off, but
00:19:58 --> 00:20:00 then there's probably other stuff that you could
00:20:00 --> 00:20:03 both do that could be like a quick win and also
00:20:03 --> 00:20:06 further could help confirm. like disprove your
00:20:06 --> 00:20:08 hypothesis. So like, could help you learn more,
00:20:08 --> 00:20:11 but also could kind of give you that feel of
00:20:11 --> 00:20:13 like, we're taking actions, right? Something
00:20:13 --> 00:20:16 like, oh, we're going to implement a temporary,
00:20:16 --> 00:20:18 like let's, we're going to do a deep dive of
00:20:18 --> 00:20:19 like one -to -ones with the whole department
00:20:19 --> 00:20:22 for two weeks and do like an info gathering thing.
00:20:22 --> 00:20:24 You're doing something, you're building information.
00:20:24 --> 00:20:26 It's temporary. So if everyone hates it, it's
00:20:26 --> 00:20:29 fine. It's two weeks of everyone's life where
00:20:29 --> 00:20:31 they have like a one hour meeting once a week.
00:20:31 --> 00:20:35 Or if you're going to. I'm trying to think of
00:20:35 --> 00:20:37 another like on the fly good example. Just looking
00:20:37 --> 00:20:40 for something that's a little that you can kind
00:20:40 --> 00:20:42 of like fix. You're not ripping off any duct
00:20:42 --> 00:20:44 tape, but you're maybe just like adding your
00:20:44 --> 00:20:46 own little duct tape in a place where there's
00:20:46 --> 00:20:50 been a leak forever that isn't permanent, that
00:20:50 --> 00:20:54 isn't a huge lift, just makes someone's life
00:20:54 --> 00:20:56 a little bit easier like along the way as you
00:20:56 --> 00:20:58 plan for your big moves that you're going to
00:20:58 --> 00:21:00 make later on when you have full context and
00:21:00 --> 00:21:04 info and trust. Nice. Nice. I like, I like that
00:21:04 --> 00:21:08 framework, um, um, idea because, and habit, if
00:21:08 --> 00:21:11 I should say, because it allows you to manage
00:21:11 --> 00:21:13 risk because that's what really the CFO comes
00:21:13 --> 00:21:17 in to do. It's not the no person, but you, the
00:21:17 --> 00:21:22 why. Yeah. We do this, um, because you don't
00:21:22 --> 00:21:24 want to have, you don't want to have all these
00:21:24 --> 00:21:27 great ideas become like go fund me campaign because
00:21:27 --> 00:21:29 it doesn't make sense for the balance sheet.
00:21:29 --> 00:21:32 Right? So what are some of the things that can
00:21:32 --> 00:21:35 tell your business is actually sustainable versus
00:21:35 --> 00:21:37 just surviving? Like how, what is your approach
00:21:37 --> 00:21:42 to managing risk? Managing risk. You mean, so.
00:21:42 --> 00:21:45 Sustainable so whether the company is taking
00:21:45 --> 00:21:47 on too much risk you mean either too much risk
00:21:47 --> 00:21:51 because you as a CFO coming in Helping them understand
00:21:51 --> 00:21:53 what how is the business doing? Where are the
00:21:53 --> 00:21:56 risks like where should we be careful? What are
00:21:56 --> 00:21:58 the things we're trying to mitigate like what
00:21:58 --> 00:22:01 how do you manage that like build your your risk
00:22:01 --> 00:22:05 profile for a company? Great question. Now let's
00:22:05 --> 00:22:07 take a quick break here so I can tell you more
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00:22:10 --> 00:22:12 have a data problem. They have a fragmentation
00:22:12 --> 00:22:15 problem. Planning runs in one system, closing
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00:22:18 --> 00:22:21 spend their time reconciling systems instead
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00:22:24 --> 00:22:28 finance workflow into one line of sight. One
00:22:28 --> 00:22:31 platform, one login with AI built into the workflow.
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00:22:41 --> 00:22:45 .com. That's L -I -N -E -O -S .com. I will drop
00:22:45 --> 00:22:47 the link in the show notes. Now let's get back
00:22:47 --> 00:22:51 to the episode. I, again, coming from a startup
00:22:51 --> 00:22:55 background, I am always prioritizing, which I
00:22:55 --> 00:22:58 think is helpful for a risk conversation, especially
00:22:58 --> 00:23:01 in a context where you need to grow, grow, grow,
00:23:01 --> 00:23:04 grow, grow. Um, and it's funny, actually risk
00:23:04 --> 00:23:06 management when you're at like a big company
00:23:06 --> 00:23:08 that just needs to sustain for years and years
00:23:08 --> 00:23:10 and years as a really different thing than if
00:23:10 --> 00:23:13 you're working for like a VC backed company where
00:23:13 --> 00:23:15 we got a shoe for the moon. And if we don't hit
00:23:15 --> 00:23:17 the moon, that's failure. So it's like, okay,
00:23:18 --> 00:23:19 what, what risk can you take? The whole company
00:23:19 --> 00:23:23 is a risk. So step one, I think maybe it's like
00:23:23 --> 00:23:27 know where you are and, and, and, and what the
00:23:27 --> 00:23:31 tolerance should be and not being like. too cautious,
00:23:31 --> 00:23:34 just because that's your role, but instead recognizing
00:23:34 --> 00:23:39 the prioritization of any degree of risk management
00:23:39 --> 00:23:42 within what the company's trying to do. I think
00:23:42 --> 00:23:44 as CFO, sometimes we can get hung up working
00:23:44 --> 00:23:48 for a high risk company, feeling like high risk
00:23:48 --> 00:23:50 companies are doing something like bad, like
00:23:50 --> 00:23:53 almost like it's morally wrong, right? But it's,
00:23:53 --> 00:23:55 it's not, it's just a strategy. It's like just
00:23:55 --> 00:23:57 a mode and you kind of need to calibrate that
00:23:57 --> 00:24:00 to that appropriately. So it's like one is like,
00:24:00 --> 00:24:03 I'm always making sure I'm calibrating to like,
00:24:03 --> 00:24:05 well, what are their goals? Cause if it's be
00:24:05 --> 00:24:09 a billion dollar business from 10 employees or
00:24:09 --> 00:24:11 failure, like, yeah, we got to take a lot of
00:24:11 --> 00:24:14 risks to get there. And then I think it's about
00:24:14 --> 00:24:17 again, prioritization, but like within the aspects
00:24:17 --> 00:24:21 of the firm thinking about for this company.
00:24:21 --> 00:24:24 What are the biggest risks? What does stop business?
00:24:24 --> 00:24:27 What is something we couldn't come back from
00:24:27 --> 00:24:30 versus what is something that's like, it's going
00:24:30 --> 00:24:31 to be annoying. It's going to be a slog. It must
00:24:31 --> 00:24:34 cost a lot of money, but like taking that risk
00:24:34 --> 00:24:37 is worth it in order to get the thing that we
00:24:37 --> 00:24:39 want to get. So you're almost kind of doing like
00:24:39 --> 00:24:43 a risk taxonomy of the different aspects of the
00:24:43 --> 00:24:45 business and thinking through like, where is
00:24:45 --> 00:24:47 the like golden egg value here that we cannot
00:24:47 --> 00:24:51 destroy? and that where we need to be like extra
00:24:51 --> 00:24:53 careful and where are the aspects where it's
00:24:53 --> 00:24:55 like, yeah, we can kind of push that off because
00:24:55 --> 00:24:58 if something goes wrong there, it's not a huge
00:24:58 --> 00:25:02 deal. So I think in both aspects, it's just prioritization
00:25:02 --> 00:25:04 and figuring out what's important and what mode
00:25:04 --> 00:25:07 we're operating. And it's interesting because
00:25:07 --> 00:25:09 I'm curious to also hear because the CFO role
00:25:09 --> 00:25:13 compared to when I started in. accounting and
00:25:13 --> 00:25:15 finance is very different than the one today.
00:25:16 --> 00:25:17 So I'm curious to hear what you've been hearing
00:25:17 --> 00:25:21 from your guests on the Growth Mining CFO. Like,
00:25:22 --> 00:25:24 what else is coming under the umbrella of the
00:25:24 --> 00:25:29 CFO? And how can we just keep up? That's the
00:25:29 --> 00:25:32 question we all want to answer, right? How can
00:25:32 --> 00:25:33 we calm ourselves? I don't know. What are you
00:25:33 --> 00:25:38 hearing? I think the two trends that it feels
00:25:38 --> 00:25:42 like CFOs currently are feeling the most pressure
00:25:42 --> 00:25:44 with in terms of like falling behind or trying
00:25:44 --> 00:25:47 to keep up. I think the major one we all are
00:25:47 --> 00:25:49 thinking about is AI and how are we going to
00:25:49 --> 00:25:51 deal with this new technology in our organization,
00:25:52 --> 00:25:54 which we could talk about at length. The other
00:25:54 --> 00:25:57 one that I think is interesting that I've seen,
00:25:57 --> 00:25:59 at least in the CFOs I've been interviewing,
00:26:00 --> 00:26:03 is this diversification of the CFO role and what's
00:26:03 --> 00:26:10 expected of us. I think this one I think is driven
00:26:10 --> 00:26:14 by, there's a lot more folks that I've spoken
00:26:14 --> 00:26:18 with who are both CFOs and COO. There's a lot
00:26:18 --> 00:26:21 of CFOs who are running the IT team. There's
00:26:21 --> 00:26:25 a lot of CFOs who are simply taking on way more,
00:26:25 --> 00:26:33 right? Me too. It's just not. I think there's
00:26:33 --> 00:26:36 this old school notion of a CFO as being kind
00:26:36 --> 00:26:40 of like the like ultimate transformation, like
00:26:40 --> 00:26:42 the ultimate form of an accountant, right? Like
00:26:42 --> 00:26:44 you go up all the way from like bookkeeper to
00:26:44 --> 00:26:46 controller to like staff accountant all the way.
00:26:46 --> 00:26:50 And then finally your CFO. And now the CFO is
00:26:50 --> 00:26:53 just expected to do so much play outside the
00:26:53 --> 00:26:56 finance organization that you really need a greater
00:26:56 --> 00:27:00 diversity of skills. Yes, it's those two. Let's
00:27:00 --> 00:27:02 dive into the first one because it's all over
00:27:02 --> 00:27:07 my feed AI. Yeah. How do we deal with this AI
00:27:07 --> 00:27:10 thing? Is it going away? Is it here to stay?
00:27:10 --> 00:27:13 I don't think it's going away. No, it's not.
00:27:13 --> 00:27:16 I don't think it is. But I also think there is
00:27:16 --> 00:27:19 a lot of hype around it because it's like everything
00:27:19 --> 00:27:21 is with a sprinkle of AI. It's like ketchup and
00:27:21 --> 00:27:25 fries. all over everything. So I'm curious to
00:27:25 --> 00:27:28 hear from a CFO standpoint, what are your thoughts
00:27:28 --> 00:27:31 on what AI means for businesses and also for
00:27:31 --> 00:27:34 how the finance function will evolve eventually?
00:27:34 --> 00:27:37 I'm actually super excited. I feel like in this
00:27:37 --> 00:27:41 AI era, I oscillate between depression and huge
00:27:41 --> 00:27:43 excitement when it comes to AI. I don't know
00:27:43 --> 00:27:48 if that's also how you feel about it. I think
00:27:48 --> 00:27:53 it's been very interesting that AI technology
00:27:53 --> 00:27:57 or kind of innovation has been really happening
00:27:57 --> 00:27:59 in the finance function of all the functions
00:27:59 --> 00:28:03 because I've seen a lot of this hype coming from
00:28:03 --> 00:28:06 different companies around like, let's vibe code
00:28:06 --> 00:28:08 everything. And I have AI bots that are doing
00:28:08 --> 00:28:11 my reconciliations and like, let's open -claw
00:28:11 --> 00:28:16 yellow everything. watching that and I as a technologist
00:28:16 --> 00:28:19 and like excited that like, oh, I can like whip
00:28:19 --> 00:28:21 together an application without having to like,
00:28:22 --> 00:28:25 get into code. And I can, I can think about all
00:28:25 --> 00:28:28 these different solutions to problems that used
00:28:28 --> 00:28:30 to be so manual as like making so many more things
00:28:30 --> 00:28:33 automated. And at the same time, like we're the
00:28:33 --> 00:28:38 risk management department, like why are we so
00:28:38 --> 00:28:42 cavalier? And I've had this amazing opportunity
00:28:42 --> 00:28:45 recently working so much again in security. I
00:28:45 --> 00:28:48 was a security CFO before I became a fractional
00:28:48 --> 00:28:52 CFO for a security research company and I'm in
00:28:52 --> 00:28:55 security again. And I recently went to a couple
00:28:55 --> 00:28:58 of different security conferences and they're
00:28:58 --> 00:29:01 talking about AI and the vibe in the room is
00:29:01 --> 00:29:03 not like excitement and oh my gosh, and this
00:29:03 --> 00:29:05 is crazy. It's a bunch of engineers. So they're
00:29:05 --> 00:29:07 all using AI to do coding, but their vibe is
00:29:08 --> 00:29:10 Oh no, it's the apocalypse. Like they are freaking
00:29:10 --> 00:29:14 out because with every new kind of huge emergent
00:29:14 --> 00:29:17 technology that gets adopted, there's a whole
00:29:17 --> 00:29:21 new attack vector that's coming. online and people
00:29:21 --> 00:29:24 are going to get burned and they have to figure
00:29:24 --> 00:29:26 out a whole slew of new security practices to
00:29:26 --> 00:29:28 keep people safe when they're using this tech.
00:29:29 --> 00:29:31 You know, the same thing happened when the internet
00:29:31 --> 00:29:34 became a thing where people just had like, you
00:29:34 --> 00:29:36 know, private information dangling out there
00:29:36 --> 00:29:38 for you went to find and they were getting, you
00:29:38 --> 00:29:41 know, hacked and attacked and, and, and it took
00:29:41 --> 00:29:43 years to put those like security things into
00:29:43 --> 00:29:48 place. Um, and so, you know, I think for security
00:29:48 --> 00:29:51 or for finance professionals, we're really going
00:29:51 --> 00:29:54 to have to contend with balancing our excitement
00:29:54 --> 00:29:57 of all the things that we can now do with AI,
00:29:57 --> 00:30:00 which is super exciting and very helpful, and
00:30:00 --> 00:30:04 our education around what's risky, going back
00:30:04 --> 00:30:09 to your risk conversation. It's hard to understand
00:30:09 --> 00:30:12 whether working with AI is risky or not right
00:30:12 --> 00:30:15 now. So we really have to look at that more carefully.
00:30:16 --> 00:30:20 I agree because AI can make something bad look
00:30:20 --> 00:30:24 so good, right? Yeah. So the kind of emails I
00:30:24 --> 00:30:27 get, they're so slick. Like before you can, you
00:30:27 --> 00:30:30 could tell easily that this is a scam. Like this
00:30:30 --> 00:30:33 is, this is not real. Like the request for invoice,
00:30:33 --> 00:30:36 request for payments, like they're becoming slicker
00:30:36 --> 00:30:38 and slicker. And I can understand how, you know,
00:30:38 --> 00:30:42 being a in cybersecurity space, like, okay, wow.
00:30:42 --> 00:30:46 It means they can really deepfake a person, a
00:30:46 --> 00:30:49 voice, so many things. And that to me, yes, is
00:30:49 --> 00:30:51 scary. I do like to see reconciliations being
00:30:51 --> 00:30:56 done like this, getting closer to zero -day close,
00:30:56 --> 00:30:59 becoming a standard. That's exciting. But in
00:30:59 --> 00:31:03 terms of security, it's scary. It is scary. It
00:31:03 --> 00:31:05 is scary. I've been working, trying to put together
00:31:05 --> 00:31:09 some frameworks to help other finance professionals.
00:31:10 --> 00:31:13 understand the types of risks that could happen.
00:31:13 --> 00:31:16 Because I feel like when we're learning about
00:31:16 --> 00:31:19 new technology, we can build an instinct for
00:31:19 --> 00:31:22 what's safe and what's dangerous. And the way
00:31:22 --> 00:31:24 you build that is you have to have a sense of
00:31:24 --> 00:31:26 the buckets of stuff. The risk that you just
00:31:26 --> 00:31:29 described with phishing emails, I think, goes
00:31:29 --> 00:31:32 under the bucket of just new attacks. And so
00:31:32 --> 00:31:35 what's happening with the typical attacks that
00:31:35 --> 00:31:37 you might find, especially like phishing attacks,
00:31:38 --> 00:31:40 the quality is getting better because of the
00:31:40 --> 00:31:43 AI and the volume is going up because it doesn't
00:31:43 --> 00:31:46 take work to build more of them. And so you're
00:31:46 --> 00:31:49 just going to see this gigantic volume, those
00:31:49 --> 00:31:52 attacks coming in and looking really good. And
00:31:52 --> 00:31:56 that is one attack vector. But there's like other
00:31:56 --> 00:31:57 stuff where it's like instead there's another
00:31:57 --> 00:32:00 attack vector around prompt injection, which
00:32:00 --> 00:32:03 has to do with if you have an AI model that you're
00:32:03 --> 00:32:07 using for work and it has exposure to the internet,
00:32:07 --> 00:32:10 like it can take in information from the internet.
00:32:10 --> 00:32:14 A prompt injection attack is a little piece of
00:32:14 --> 00:32:17 code or even just text that an attacker will
00:32:17 --> 00:32:20 hide in something that that AI is going to ingest,
00:32:20 --> 00:32:22 but then will cause that AI bot to like dump
00:32:22 --> 00:32:25 information to a website that they own so they
00:32:25 --> 00:32:27 can steal it, right? And again, this is like
00:32:27 --> 00:32:31 just a new kind of attack that people are going
00:32:31 --> 00:32:35 to encounter that's not produced by AI, but it's
00:32:35 --> 00:32:38 because they're using AI. And again, that's like
00:32:38 --> 00:32:40 just two of them. There's so many other things
00:32:40 --> 00:32:42 that - Oh, I want to hear all the other examples
00:32:42 --> 00:32:45 you have. Yeah, give me more so I can be scared
00:32:45 --> 00:32:48 after this. More examples of reasons to be scared?
00:32:48 --> 00:32:56 Cool, cool, cool. Again, I'm still working on
00:32:56 --> 00:32:58 an exhaustive list, but the other things that
00:32:58 --> 00:33:01 when I am working with AI that I'm worried about,
00:33:02 --> 00:33:04 actually, the better way I think to talk about
00:33:04 --> 00:33:06 this is thinking about a structure that someone's
00:33:06 --> 00:33:08 already created. Someone very clever whose name
00:33:08 --> 00:33:10 I can't remember right now, unfortunately, came
00:33:10 --> 00:33:14 up with this idea of the danger triangle. Again,
00:33:14 --> 00:33:16 I even might be saying it wrong, but it's this
00:33:16 --> 00:33:19 idea when you're using AI, when you're like building
00:33:19 --> 00:33:21 with AI, you're working with an LLM, there's
00:33:21 --> 00:33:25 sort of three different things that should give
00:33:25 --> 00:33:27 you little alarm bells on like, uh -oh, I'm reaching
00:33:27 --> 00:33:30 unsafe territory. And those three little things
00:33:30 --> 00:33:36 are, if it can take in untrusted inputs, which
00:33:36 --> 00:33:40 means like strangers can put stuff into the LLM,
00:33:40 --> 00:33:43 and if it has access to the internet, that applies,
00:33:44 --> 00:33:47 right? So that's one. The other aspect is if
00:33:47 --> 00:33:52 it has access to private information. So it has
00:33:52 --> 00:33:54 some data that's like very valuable that you
00:33:54 --> 00:33:57 wouldn't want to share externally. And then the
00:33:57 --> 00:34:00 third is Can it change stuff? And this one is
00:34:00 --> 00:34:04 toggling between now it used to be, can it like
00:34:04 --> 00:34:07 send data other places? Like, can it communicate
00:34:07 --> 00:34:09 with the outside world, like send email on your
00:34:09 --> 00:34:12 behalf? But now that OpenClaus become a thing
00:34:12 --> 00:34:15 and agents are able to have right access, it's,
00:34:15 --> 00:34:18 I think, widening up to like, can it like change
00:34:18 --> 00:34:21 things on your machine or others? In this triangle,
00:34:21 --> 00:34:24 it's like, if you have any one of them, you're
00:34:24 --> 00:34:27 kind of okay, not too much risk. your LLM can
00:34:27 --> 00:34:30 do two of them, you're in a risky situation.
00:34:30 --> 00:34:33 If it can do three, if there's an attack, you
00:34:33 --> 00:34:37 will get burned. So you're in a bad posture.
00:34:37 --> 00:34:42 So when I'm working in my own environment, working
00:34:42 --> 00:34:44 with cloud, I'm always thinking about those three
00:34:44 --> 00:34:48 things in mind, knowing that I want to avoid
00:34:48 --> 00:34:50 all three. So if I'm working with private data,
00:34:51 --> 00:34:52 I have to make sure I'm trying to think like,
00:34:52 --> 00:34:54 okay, I can work with private data, but it can't
00:34:54 --> 00:34:56 have access to the internet. And it certainly,
00:34:56 --> 00:34:59 you know, can't have like the ability to communicate
00:34:59 --> 00:35:02 anything outside my own environment, right? or
00:35:02 --> 00:35:06 I'm always thinking about if I'm using Claude
00:35:06 --> 00:35:08 to build something to do with productivity, I'm
00:35:08 --> 00:35:11 always a little worried and wary because if that
00:35:11 --> 00:35:14 means I have to give it my data, that's one of
00:35:14 --> 00:35:18 the areas of the trifecta. That to me has been
00:35:18 --> 00:35:21 the most useful thing to keep in mind to prevent
00:35:21 --> 00:35:24 from getting safe. One more dangerous scenario
00:35:24 --> 00:35:26 that I'll mention because I think it's the other
00:35:26 --> 00:35:30 category that's really important. is we've seen
00:35:30 --> 00:35:33 a lot of data destruction. We hear a lot about
00:35:33 --> 00:35:36 data leaking from AI attacks, which is scary.
00:35:37 --> 00:35:40 But the other scary thing is if we're using OpenClaw,
00:35:40 --> 00:35:43 OpenClaw can go ahead and do a whole bunch of
00:35:43 --> 00:35:46 things on your behalf, right? And it doesn't
00:35:46 --> 00:35:49 know right from wrong. So you might tell it to
00:35:49 --> 00:35:52 go get data from some database somewhere, and
00:35:52 --> 00:35:54 it might struggle to get the data, trying to
00:35:54 --> 00:35:56 scan it, and then it might find a SQL injection
00:35:56 --> 00:35:59 and just hack the database, right? We've seen
00:35:59 --> 00:36:03 that happen. It might want to change something
00:36:03 --> 00:36:05 about your repo and then discover that it didn't
00:36:05 --> 00:36:07 do it right. So it wants to wipe and start over
00:36:07 --> 00:36:10 and then delete your entire database. We've seen
00:36:10 --> 00:36:13 that happen. So we just want to have this sense
00:36:13 --> 00:36:17 of like AI agents are this weird combination
00:36:17 --> 00:36:20 of like a super, super, super junior employee
00:36:20 --> 00:36:22 who does not yet have wisdom, but they're also
00:36:22 --> 00:36:26 an expert hacker. They can do a lot of very powerful.
00:36:27 --> 00:36:29 That's right. They're very powerful, but not
00:36:29 --> 00:36:36 very smart. It sounds like a very powerful toddler.
00:36:36 --> 00:36:40 I'm a big Marvel fan, so I'm imagining the Avengers
00:36:40 --> 00:36:46 suit of a toddler that have all the power. And
00:36:46 --> 00:36:51 here's Jarvis. Jarvis is a toddler. Wasn't it
00:36:51 --> 00:36:53 like the baby from The Incredibles had this issue
00:36:53 --> 00:37:00 too? Baby Jack Jack. Yeah. Oh, yeah. I love that
00:37:00 --> 00:37:04 show. Yeah, it was great. Yeah, because I feel
00:37:04 --> 00:37:09 like CFOs now somehow are expected to be superheroes
00:37:09 --> 00:37:13 at some point, so we may as well. There's a blog
00:37:13 --> 00:37:15 post there. We're all meant to be the Incredibles,
00:37:15 --> 00:37:20 but no one's accounting for Baby Jack. No, we're
00:37:20 --> 00:37:22 just hoping and praying Baby Jack doesn't happen
00:37:22 --> 00:37:24 because you don't know what you do with it, right?
00:37:25 --> 00:37:28 Because that's always the thing with AI is how
00:37:28 --> 00:37:30 can you tell when it's wrong? Because it can
00:37:30 --> 00:37:35 make good very look. make bad look very good.
00:37:35 --> 00:37:37 But also, how do you fix when it's wrong? Like
00:37:37 --> 00:37:40 you mentioned, if it destroys your data, how
00:37:40 --> 00:37:43 do you bring it back? Because I heard the story
00:37:43 --> 00:37:48 about how the agent deleted the backup because
00:37:48 --> 00:37:52 it saw that there was no first version to backup.
00:37:52 --> 00:37:54 So hey, you probably don't need a backup. There
00:37:54 --> 00:37:57 is no first instance. Those things are definitely
00:37:57 --> 00:37:59 scary. But I'm curious to hear, what's the one
00:37:59 --> 00:38:03 thing you're most excited about AI? Coolest thing
00:38:03 --> 00:38:07 right now about AI and functions. Development
00:38:07 --> 00:38:11 being that much closer to the user, I think is
00:38:11 --> 00:38:14 just so interesting. Like we're looking at a
00:38:14 --> 00:38:18 world where we are all going to have the ability
00:38:18 --> 00:38:21 to sort of be an engineer, right? We're all going
00:38:21 --> 00:38:24 to have this capability of seeing something we
00:38:24 --> 00:38:28 want to have exist in the world and then bringing
00:38:28 --> 00:38:33 it in to existence. I'm so interested in what
00:38:33 --> 00:38:36 that world will look like, because there's just
00:38:36 --> 00:38:39 a whole fleet of problems we won't have to deal
00:38:39 --> 00:38:43 with. And then we have other problems to deal
00:38:43 --> 00:38:48 with that I think we will. I think that also
00:38:48 --> 00:38:51 it's going to really shift around what things
00:38:51 --> 00:38:56 are valuable in someone in an organization. I
00:38:56 --> 00:38:59 think that even though I think there's a lot
00:38:59 --> 00:39:01 of fear, I think around AI that it's going to
00:39:01 --> 00:39:03 take everyone's jobs and that, you know, unless
00:39:03 --> 00:39:05 you're building with AI, you're going to be left
00:39:05 --> 00:39:06 behind and you're going to have no value to an
00:39:06 --> 00:39:09 organization. I think that's pretty silly. I
00:39:09 --> 00:39:11 think that's throwing out the baby with the bathwater
00:39:11 --> 00:39:22 because AI has the ability to do a lot of stuff
00:39:22 --> 00:39:25 to like that 80 % marker, right? Like it can
00:39:25 --> 00:39:28 write. But it's not engaging writing. It can
00:39:28 --> 00:39:31 do like financial modeling, but the modeling
00:39:31 --> 00:39:35 is a little off. It can do things to like the
00:39:35 --> 00:39:38 degree where someone who's like pretty okay at
00:39:38 --> 00:39:43 it can do. And it will get smarter, but in a
00:39:43 --> 00:39:46 world where everybody has that capability. There's
00:39:46 --> 00:39:48 going to be other stuff that's rare. Those like
00:39:48 --> 00:39:51 spiky skills. I think there was a lot of fear
00:39:51 --> 00:39:53 when Chachi P .T. first kind of came out that
00:39:53 --> 00:39:56 like the writing skill was going to go away.
00:39:56 --> 00:39:58 Like, oh, writers are dead. We don't need them
00:39:58 --> 00:40:00 anymore. And what I've seen happen in that area
00:40:00 --> 00:40:03 is like writers have never been more valuable
00:40:03 --> 00:40:06 because when there's so much content that's just
00:40:06 --> 00:40:10 mediocre, the standard for what you have to be
00:40:10 --> 00:40:12 able to achieve as a writer to be engaging and
00:40:12 --> 00:40:14 have people be excited about it has gone up.
00:40:15 --> 00:40:19 And now if you have that like 1 % skill set,
00:40:19 --> 00:40:23 you are very rare indeed and very valuable. So
00:40:23 --> 00:40:28 yeah, I'm also interested for that shakeup. Okay.
00:40:28 --> 00:40:30 I'm curious to hear what you think that that
00:40:30 --> 00:40:37 skill will be for finance people. I would think
00:40:37 --> 00:40:40 experience is going to be one of the biggest
00:40:40 --> 00:40:43 things. I feel like in finance, you so often
00:40:43 --> 00:40:47 get into these nuanced situations where you have
00:40:47 --> 00:40:51 to make a decision. And there's so many factors
00:40:51 --> 00:40:55 at play that to really get to the right call,
00:40:55 --> 00:41:01 you're going to need so many reps, so many past
00:41:01 --> 00:41:04 experiences, so much extra relevant information,
00:41:04 --> 00:41:09 this kind of like judgment and AI, again, I think
00:41:09 --> 00:41:12 it's it's sort of that 1 % like everyone can
00:41:12 --> 00:41:17 sort of learn how to do Excel, but not everyone
00:41:17 --> 00:41:20 is like the best, right? The person who really
00:41:20 --> 00:41:22 gets it, people are going to be AI is going to
00:41:22 --> 00:41:25 know the 10 tenants that you have to follow if
00:41:25 --> 00:41:28 you're doing an M &A deal. But that doesn't mean
00:41:28 --> 00:41:29 you're then going to be able to successfully
00:41:29 --> 00:41:33 close a deal. Right? These things require so
00:41:33 --> 00:41:37 much nuance and experience. And AI skills are
00:41:37 --> 00:41:39 something that you can build weekend, right?
00:41:39 --> 00:41:42 You can take a course on AI and suddenly be coding
00:41:42 --> 00:41:44 quickly. And that learning curve is just going
00:41:44 --> 00:41:47 to go shallower, right? AI companies want us
00:41:47 --> 00:41:48 to adopt the software. So it's going to be easier
00:41:48 --> 00:41:52 and easier and easier to catch up building with
00:41:52 --> 00:41:56 AI and doing AI things. But everyone's focused
00:41:56 --> 00:41:59 there. If you instead are honing your leadership
00:41:59 --> 00:42:02 skills, your deal -making skills, those kinds
00:42:02 --> 00:42:07 of complicated, messy problems that involve people
00:42:07 --> 00:42:10 and nuance and numbers and all this sort of thing,
00:42:10 --> 00:42:12 and you're getting your reps there, you're going
00:42:12 --> 00:42:15 to be super rare. And you're of course going
00:42:15 --> 00:42:17 to be better than AI because where is it going
00:42:17 --> 00:42:20 to get its data on that from an individual going
00:42:20 --> 00:42:21 through it? There aren't enough people blogging
00:42:21 --> 00:42:25 about that for it to be good. Yes. Yes. And that
00:42:25 --> 00:42:27 goes to what we started a conversation on when
00:42:27 --> 00:42:31 you start a new, when you start on, you become
00:42:31 --> 00:42:35 a new CFO at a new place. And how do you onboard
00:42:35 --> 00:42:37 yourself? Like I can tell from speaking with
00:42:37 --> 00:42:40 you, you've done it so well. You have a system
00:42:40 --> 00:42:44 now. So yes. It's like definitely that experience
00:42:44 --> 00:42:46 and judgment to know, OK, this is where I need
00:42:46 --> 00:42:49 to to go look for. This is the these are the
00:42:49 --> 00:42:52 questions I need to ask because AI is only as
00:42:52 --> 00:42:55 good as what he gets, what he gets fed with.
00:42:55 --> 00:42:59 Right. Yeah. Yeah. And it's also I feel like
00:42:59 --> 00:43:03 it's been working with AI has been the most helpful
00:43:03 --> 00:43:06 in areas where I either know nothing or I know
00:43:06 --> 00:43:09 a ton. Because if I, and I'm using it for different
00:43:09 --> 00:43:12 things in those regards, if I'm using AI in a
00:43:12 --> 00:43:14 place where I already have expertise, I'm using
00:43:14 --> 00:43:17 it like a junior employee. I'm using it to like
00:43:17 --> 00:43:19 do the grunt work of the thing that I already
00:43:19 --> 00:43:21 know how to build from all the experiences I've
00:43:21 --> 00:43:24 had as a senior employee for years and years.
00:43:25 --> 00:43:26 And that in that way, it's just like exciting.
00:43:26 --> 00:43:29 And I'm trying to get to that 1 % product, but
00:43:29 --> 00:43:34 I might be using AI's help to get there. Or if
00:43:34 --> 00:43:37 I know nothing about an area, I'm using AI to
00:43:37 --> 00:43:39 get to like prototype level because my standards
00:43:39 --> 00:43:41 are so low that it doesn't matter, right? If
00:43:41 --> 00:43:44 I'm not trying to get something production level
00:43:44 --> 00:43:46 ready, but it does let me get to that like first
00:43:46 --> 00:43:50 MVP version. But everywhere in the middle, it
00:43:50 --> 00:43:52 kind of falls short, right? Like if you want
00:43:52 --> 00:43:54 to have like a production ready software and
00:43:54 --> 00:43:56 you don't know how to engineer something, like
00:43:56 --> 00:43:59 AI is not going to get you to the same point
00:43:59 --> 00:44:01 that someone who's really skilled in the area
00:44:01 --> 00:44:06 is going to get. Drew. Good stuff ahead. I'm
00:44:06 --> 00:44:09 curious outside of AI and all that and work,
00:44:09 --> 00:44:11 what is your favorite thing to do outside of
00:44:11 --> 00:44:14 work? I always like to ask my guests. Oh, well,
00:44:14 --> 00:44:18 I can show you because I have all of these pots
00:44:18 --> 00:44:23 behind me. My pottery is a thing that I really
00:44:23 --> 00:44:26 love to do outside of work. You made that yourself?
00:44:26 --> 00:44:33 Yeah. Wow. This is what I do on the weekends.
00:44:35 --> 00:44:40 That is so neat. Wow. I started doing pottery
00:44:40 --> 00:44:43 actually when I first broke off on my own. I
00:44:43 --> 00:44:47 was at a full -time CFO role and I rage quit
00:44:47 --> 00:44:49 that role and started a startup and a consulting
00:44:49 --> 00:44:52 firm at the same time, which was very stressful.
00:44:52 --> 00:44:56 And my partner encouraged me to take a pottery
00:44:56 --> 00:44:59 class to deal with the stress and it is how I
00:44:59 --> 00:45:04 survived as a CFO for hire for six years. Very
00:45:04 --> 00:45:12 centering. No pun intended. That is so cool.
00:45:12 --> 00:45:16 That is so cool. Yeah. I wish I knew how to do
00:45:16 --> 00:45:18 something else with my hands, except for cooking
00:45:18 --> 00:45:21 and eating. I mean, the two activities go together.
00:45:21 --> 00:45:24 Yeah. I wish I could cook because I can't eat
00:45:24 --> 00:45:27 those pots. I need something to put in them.
00:45:27 --> 00:45:30 So you and I should have some collaborate. We
00:45:30 --> 00:45:34 need to do some collaboration. We need another
00:45:34 --> 00:45:38 collab. Thank you. Thank you so, so much, Lauren,
00:45:38 --> 00:45:41 for being on the show. You're so brilliant. It's
00:45:41 --> 00:45:43 such a joy to also connect with another lady,
00:45:43 --> 00:45:46 powerful lady in finance. Thank you. Thank you
00:45:46 --> 00:45:49 so much. Thank you so much for having me. This
00:45:49 --> 00:45:52 was so much fun, Wassia. It was so, so nice to
00:45:52 --> 00:45:55 get to chat with you. Thank you. Thank you. And
00:45:55 --> 00:45:56 that's it for today's episode for the Diary of
00:45:56 --> 00:45:59 a CFO podcast. I really hope you enjoyed it because
00:45:59 --> 00:46:02 I know I did. Don't forget to leave a review
00:46:02 --> 00:46:04 and subscribe on whatever platform you're listening
00:46:04 --> 00:46:06 on as it helps this conversation reach other
00:46:06 --> 00:46:09 finance leaders who need them. As always, if
00:46:09 --> 00:46:11 you want to go deeper on anything, feel free
00:46:11 --> 00:46:15 to reach out at AskAtTheDiaryOfACFO .com. Make
00:46:15 --> 00:46:19 sure you follow Lauren Pearl on LinkedIn and
00:46:19 --> 00:46:22 also listen to her podcast. As I mentioned during
00:46:22 --> 00:46:25 our episode, I was recently a guest and I spoke
00:46:25 --> 00:46:28 on how to build a personal brand as a senior
00:46:28 --> 00:46:30 finance leader and how it has helped me land
00:46:30 --> 00:46:33 my first VP role and the CFO role I'm currently
00:46:33 --> 00:46:35 on. So thanks for tuning in. Definitely check
00:46:35 --> 00:46:38 out the podcast, The Growth Money CF. and I'll
00:46:38 --> 00:46:39 see you in the next one.