Johan Dovander is the CFO of Rillion, an AI-driven finance automation company, with more than 20 years of experience in senior finance roles at companies including Microsoft Sweden, Seigate, and Trivec, where he also served as Interim CEO twice. He has led finance teams across Sweden, Ireland, France, Belgium, the Netherlands, Germany, Switzerland, and Russia.
In this episode, Johan explains why there are no shortcuts with AI in finance and why the real value comes from challenging your processes before layering AI on top. He shares findings from a 250-CFO survey on AI adoption, including why 63% of CFOs expect finance headcount to stay the same or grow, and why most are investing in AI for FP&A when the bigger opportunity may be in transaction processing.
He also discusses what leading finance across six countries taught him about what never changes in the function, and how stepping into the CEO seat twice reshaped how he partners with CEOs today.
Download survey: https://share.google/ra9LiW8px22OUFXZH
00:00:00 --> 00:00:02 Today's episode is brought to you by Rillion.
00:00:02 --> 00:00:05 They recently released their 2026 AI in Finance
00:00:05 --> 00:00:09 report based on the survey of 250 US finance
00:00:09 --> 00:00:12 leaders. It explores how finance teams are using
00:00:12 --> 00:00:15 AI, where they're seeing opportunities and what's
00:00:15 --> 00:00:17 holding adoption back. I was glad to contribute
00:00:17 --> 00:00:20 my perspective alongside other finance leaders.
00:00:20 --> 00:00:22 So if you are thinking about AI's role in your
00:00:22 --> 00:00:24 own finance team, download the report at Rillion
00:00:24 --> 00:00:28 .com slash AI report. You will find the link
00:00:28 --> 00:00:30 in the show notes. Let's get to it. Welcome back
00:00:30 --> 00:00:32 to the Diary of a CFO podcast. I'm your host
00:00:32 --> 00:00:35 Wassia Kamon, a sitting CFO with a background
00:00:35 --> 00:00:37 in accounting and FP &A. I started this show
00:00:37 --> 00:00:39 because I believe the way company build and lead
00:00:39 --> 00:00:41 their finance function determines whether they
00:00:41 --> 00:00:43 scale or fall apart. Each week, I sit down with
00:00:43 --> 00:00:46 CFOs, CEOs, and strategic business partners to
00:00:46 --> 00:00:49 talk about what that actually looks like in reality.
00:00:50 --> 00:00:52 Getting systems and controls in place, building
00:00:52 --> 00:00:54 the right teams, partnering with the business,
00:00:54 --> 00:00:56 and doing it all hopefully without burning out.
00:00:56 --> 00:00:59 My guest today is Johan Dovander. Johan is a
00:00:59 --> 00:01:03 CFO of Rillion, an AI -driven finance automation
00:01:03 --> 00:01:06 company. It has more than 20 years of experience
00:01:06 --> 00:01:09 in senior finance roles at international technology
00:01:09 --> 00:01:12 and SaaS companies. Previously, he served as
00:01:12 --> 00:01:15 group CFO and interim CEO at European restaurant
00:01:15 --> 00:01:19 tech company Trivac and held CFO roles at Sidegate
00:01:19 --> 00:01:21 and Microsoft Sweden. Throughout his career,
00:01:21 --> 00:01:23 he has led transformation in financial management
00:01:23 --> 00:01:26 and organizational development, working closely
00:01:26 --> 00:01:28 with leadership teams in both corporate and private
00:01:28 --> 00:01:31 equity environments. Drawing on experience with
00:01:31 --> 00:01:34 global companies and growing businesses, Joanne
00:01:34 --> 00:01:37 focuses on developing the finance function into
00:01:37 --> 00:01:40 a more strategic partner to the business. Today,
00:01:40 --> 00:01:43 we'll dig into how AI is changing the finance
00:01:43 --> 00:01:46 function, what it really takes to get value from
00:01:46 --> 00:01:48 it, and what Johan has learned from leading finance
00:01:48 --> 00:01:51 across six countries. Welcome to the show, Johan.
00:01:52 --> 00:01:55 Thank you. Pleasure to be here, Basir. Oh, thank
00:01:55 --> 00:01:59 you. Curious to hear. First off, what drew you
00:01:59 --> 00:02:02 into the world of finance and accounting? So
00:02:02 --> 00:02:04 take us back to your early career. Yeah, no,
00:02:04 --> 00:02:07 that's a great, great question. Thanks for asking
00:02:07 --> 00:02:11 it. So I started my career in the oil and gas
00:02:11 --> 00:02:15 industry for the lovely company BP. And in those
00:02:15 --> 00:02:20 early days, the first job I got landed a transformation
00:02:20 --> 00:02:23 project. It was a business process re -engineering.
00:02:23 --> 00:02:25 project is about changing business processes,
00:02:26 --> 00:02:29 implementing ERP systems, BI systems, et cetera.
00:02:30 --> 00:02:33 And very quickly into the job, I got landed the
00:02:33 --> 00:02:36 responsibility to implement the BI system part
00:02:36 --> 00:02:39 of the change business processes. And that kind
00:02:39 --> 00:02:42 of got me hooked on data structures, presenting
00:02:42 --> 00:02:46 data, driving insights. That's really how it
00:02:46 --> 00:02:50 all started. So working with data and business
00:02:50 --> 00:02:53 insights from data. That's how I started my career.
00:02:53 --> 00:02:56 That's like you went straight into the fire almost.
00:02:57 --> 00:02:59 Absolutely. And in that time, I mean, I've been
00:02:59 --> 00:03:03 to business school, but to be honest, was I sure
00:03:03 --> 00:03:06 I wanted to work in kind of business finance?
00:03:06 --> 00:03:10 No, I didn't. So I landed that project thrown
00:03:10 --> 00:03:14 into the deep end of a business process re -engineering
00:03:14 --> 00:03:17 project. I mean, I knew I liked numbers, but
00:03:17 --> 00:03:21 that kind of that role really got me more clearly
00:03:21 --> 00:03:23 refined on what I wanted to do. So after I'd
00:03:23 --> 00:03:26 done that role is, okay, I think this is kind
00:03:26 --> 00:03:29 of where I want to try to focus my career. But
00:03:29 --> 00:03:32 having said that, you know, my career has spanned
00:03:32 --> 00:03:36 different areas working from finance at the core,
00:03:37 --> 00:03:40 but then with other functions in the past 20.
00:03:40 --> 00:03:44 Wow. And you stepped as interim CEO twice. So
00:03:44 --> 00:03:49 I'm curious to see how that changed, how you
00:03:49 --> 00:03:51 saw the business since you were in that chair.
00:03:52 --> 00:03:54 You were in that chair. Yeah. No, it's a great
00:03:54 --> 00:03:59 question. I think that as a CFO, you have a great
00:03:59 --> 00:04:02 partner to bounce ideas off with your CEO, even
00:04:02 --> 00:04:04 though that you kind of responsibility for your
00:04:04 --> 00:04:06 function. I think what changed it for me stepping
00:04:06 --> 00:04:10 into the interim CEO role twice is that the buck
00:04:10 --> 00:04:14 stops with you. The accountability stops with
00:04:14 --> 00:04:17 you. And it's no longer about, you know, and
00:04:17 --> 00:04:19 I'm not saying that's the role of the CFO, making
00:04:19 --> 00:04:21 recommendations, driving decision support, you
00:04:21 --> 00:04:23 have to make a lot of decisions in the CFO role
00:04:23 --> 00:04:26 as well. But there is there's nobody else behind
00:04:26 --> 00:04:30 you in the CEO role. And you can have great conversations
00:04:30 --> 00:04:33 with the chairman of the board. But somehow,
00:04:33 --> 00:04:35 you're the most knowledgeable to kind of set
00:04:35 --> 00:04:37 a strategy, drive the whole people agenda for
00:04:37 --> 00:04:41 the company. And you need to be kind of comfortable
00:04:41 --> 00:04:43 with that accountability. So I think that was
00:04:43 --> 00:04:46 a huge, huge difference compared to being in
00:04:46 --> 00:04:49 the CFO role. And it kind of makes you also value
00:04:49 --> 00:04:53 the role of CFO a lot more, having had that experience.
00:04:53 --> 00:04:56 Yes. So what was it like to actually step into
00:04:56 --> 00:05:00 the CEO seat versus stepping into the CFO seat?
00:05:00 --> 00:05:04 Like, what was your first three months, let's
00:05:04 --> 00:05:06 say, when you stepped into each role? I think,
00:05:06 --> 00:05:11 to be honest, at first, scary, you know, I'll
00:05:11 --> 00:05:14 be brutally honest, scary in both of the situations.
00:05:14 --> 00:05:16 I was kind of in the leadership team already
00:05:16 --> 00:05:19 asked to see you for working with my peers. And
00:05:19 --> 00:05:22 when you're asked to step up, all of a sudden
00:05:22 --> 00:05:24 you're supposed to manage your peers. So, you
00:05:24 --> 00:05:27 know, how will they think about me having kind
00:05:27 --> 00:05:30 of been on part of the same team for a couple
00:05:30 --> 00:05:32 of years? So it's like, you know, a lot of things
00:05:32 --> 00:05:35 kind of going through your head, even though
00:05:35 --> 00:05:37 in both situations, I've worked with a lot of
00:05:37 --> 00:05:40 the individuals at least for two years, you know,
00:05:40 --> 00:05:43 so you kind of knew a lot of the environment.
00:05:43 --> 00:05:47 you came into. The other thing that was kind
00:05:47 --> 00:05:51 of new from both of my CFO roles is, you know,
00:05:51 --> 00:05:54 in the role of CEO, there will be a lot more
00:05:54 --> 00:05:56 relationship building with customers and partners
00:05:56 --> 00:06:00 that, you know, yes, I've met a few customers
00:06:00 --> 00:06:02 and partners in my role as CFO, but here it's
00:06:02 --> 00:06:04 kind of at a different level. You're expected
00:06:04 --> 00:06:07 to perform at that different level. So I think
00:06:07 --> 00:06:10 working through that, but also being humble of
00:06:10 --> 00:06:13 thinking, OK, where can I make an impact? and
00:06:13 --> 00:06:16 where do I better leave the table, leave the
00:06:16 --> 00:06:20 team who are more experienced in this to manage
00:06:20 --> 00:06:24 it. So, kind of gauging those different scenarios
00:06:24 --> 00:06:28 and then really thinking about what is the tone
00:06:28 --> 00:06:32 I'm setting with the right of. with the organization,
00:06:32 --> 00:06:35 because as a CEO, you're part of the same leadership
00:06:35 --> 00:06:40 team as everybody else. You have to think about
00:06:40 --> 00:06:42 what messages you want to send from a leadership
00:06:42 --> 00:06:46 standpoint. But when you are the CEO, you need
00:06:46 --> 00:06:50 to set that whole tone. And you need to be considerate
00:06:50 --> 00:06:53 and cautious about how you communicate internally
00:06:53 --> 00:06:57 and externally, about goals, about people, about
00:06:57 --> 00:07:00 organizations. So a little bit more... careful,
00:07:00 --> 00:07:03 I would say, and more considerate in how you
00:07:03 --> 00:07:08 drive and communicate things. If that, let's
00:07:08 --> 00:07:11 say, if you're driving a business transformation,
00:07:12 --> 00:07:15 a cost reduction, think about the impact on the
00:07:15 --> 00:07:17 people and the organization in a broader context
00:07:17 --> 00:07:21 than from the CFO chair. Those were a couple
00:07:21 --> 00:07:24 of things that I worked through anyway in both
00:07:24 --> 00:07:27 of those situations. Thanks for sharing. How
00:07:27 --> 00:07:30 would you say having stepped into the interim
00:07:30 --> 00:07:34 CEO role changed the way you now partner as a
00:07:34 --> 00:07:38 CFO with the CEO today? Yeah, great question.
00:07:38 --> 00:07:42 I think, you know, it's rarely the numbers themselves
00:07:42 --> 00:07:46 that keep CEOs awake at night, but rather the
00:07:46 --> 00:07:49 kind of the impact that those numbers will have
00:07:49 --> 00:07:53 over the organization. And what I think, I hope,
00:07:53 --> 00:07:56 I do better now than I did 10 years ago, is thinking
00:07:56 --> 00:08:00 through those scenarios. What would the CEO have
00:08:00 --> 00:08:03 to work through? What decisions will he or she
00:08:03 --> 00:08:07 have to make? And when making recommendations
00:08:07 --> 00:08:10 that they're maybe not so black and white, because
00:08:10 --> 00:08:12 the world is never that black and white, that
00:08:12 --> 00:08:17 we like maybe things square in finance. So when
00:08:17 --> 00:08:20 making recommendations about a particular org
00:08:20 --> 00:08:24 change, for example, that really partnering with
00:08:24 --> 00:08:27 the CEO on those issues instead of just simply
00:08:27 --> 00:08:30 saying, okay, we just need to make a cost reduction
00:08:30 --> 00:08:34 of X. And here are some broader thoughts, really
00:08:34 --> 00:08:37 being that business partner in terms of organizational
00:08:37 --> 00:08:41 change and setup is something I think I do a
00:08:41 --> 00:08:43 lot more today than I would have 10 years ago.
00:08:43 --> 00:08:47 Yeah, and it goes with what you said earlier
00:08:47 --> 00:08:48 about you have to be more considerate because
00:08:48 --> 00:08:51 you have to talk to so many partners about this
00:08:51 --> 00:08:55 one thing, this one initiative, and make sure
00:08:55 --> 00:08:57 you deliver to those audiences the right message
00:08:57 --> 00:09:01 and the right tone as a CEO. So now it makes
00:09:01 --> 00:09:03 another CFO that you're now thinking broader.
00:09:03 --> 00:09:05 Your CEO is probably so grateful to have you
00:09:05 --> 00:09:11 on board with that perspective. a lot of pressure
00:09:11 --> 00:09:15 on CFOs to use AI, especially from their CEOs
00:09:15 --> 00:09:18 and boards. What do you wish more people understood
00:09:18 --> 00:09:23 about AI in finance? Good. That there are no
00:09:23 --> 00:09:27 shortcuts is what I'd love for people to understand
00:09:27 --> 00:09:32 more. You can put AI on top of an existing business
00:09:32 --> 00:09:36 process, whether that be an AP or AR or forecasting.
00:09:37 --> 00:09:40 but really the true value you're going to get
00:09:40 --> 00:09:44 from challenging your existing processes. I mean
00:09:44 --> 00:09:46 I mentioned I started my career in business process
00:09:46 --> 00:09:48 redesign and business process re -engineering
00:09:48 --> 00:09:51 and working with AI it's kind of the same thing
00:09:51 --> 00:09:55 with a different tool set that yes you will drive
00:09:55 --> 00:09:58 some sort of impact, positive impact with applying
00:09:58 --> 00:10:02 AI tools on top. But to drive full impact, and
00:10:02 --> 00:10:04 if you kind of want to take, for example, you
00:10:04 --> 00:10:05 want to drive better value, you want to take
00:10:05 --> 00:10:08 cost out whatever from transformation, you need
00:10:08 --> 00:10:12 to do some more work. and working through your
00:10:12 --> 00:10:16 processes. To take one example, so in the Rillian,
00:10:16 --> 00:10:19 we're focused on AP automation, and now part
00:10:19 --> 00:10:22 of that process starts with capturing the invoice,
00:10:23 --> 00:10:26 an AP invoice, and you can capture it all right
00:10:26 --> 00:10:30 with AI, and you can drive coding from AI, which
00:10:30 --> 00:10:33 we do with our application, but if you've got
00:10:33 --> 00:10:37 a very complicated setup with lots of different
00:10:37 --> 00:10:40 coding across lots of different dimensions, Yeah,
00:10:40 --> 00:10:44 AI can help to some extent, but it also find
00:10:44 --> 00:10:48 it hard to get things right. So challenging yourself
00:10:48 --> 00:10:50 and your business processes is going to drive
00:10:50 --> 00:10:54 a lot more meaningful impact with AI. I wish
00:10:54 --> 00:10:58 people knew that more. Everything is not so simple
00:10:58 --> 00:11:00 just because you've got an AI tool. Yeah, there
00:11:00 --> 00:11:03 is no shortcut with AI. That's a great line.
00:11:04 --> 00:11:07 So why are you seeing AI have the most practical
00:11:07 --> 00:11:11 impact inside finance? Yeah, I see it in a couple
00:11:11 --> 00:11:14 of different areas. Where we see meaningful impact
00:11:14 --> 00:11:17 is a lot around the transactional processing
00:11:17 --> 00:11:20 element where you have relatively large volumes.
00:11:20 --> 00:11:24 such an AP in AR and where decision -making is
00:11:24 --> 00:11:29 sometimes more repetitive. There I see a lot
00:11:29 --> 00:11:32 of a meaningful impact. That being, for example,
00:11:32 --> 00:11:35 capture coding. I see a lot of impact in reconsolations,
00:11:36 --> 00:11:38 for example, bank reconsolation, account reconsolation,
00:11:38 --> 00:11:41 lots of value that you can drive there. Also
00:11:41 --> 00:11:43 kind of drafting your first management report,
00:11:44 --> 00:11:47 doing cost variance analysis. You can see a lot
00:11:47 --> 00:11:49 of meaningful initial impact. So that's to name
00:11:49 --> 00:11:52 a few. different areas where I think all of today
00:11:52 --> 00:11:55 we do see a lot of impact from AI. Okay. And
00:11:55 --> 00:11:59 I know really I recently surveyed like 250 CFOs
00:11:59 --> 00:12:02 about AI in finance. What was the finding that
00:12:02 --> 00:12:05 surprised you the most and why? Yeah, no, that's
00:12:05 --> 00:12:09 a great question. We surveyed 250 CFOs across
00:12:09 --> 00:12:13 the US. We also did a similar survey in Europe.
00:12:15 --> 00:12:18 Can I name more than one? Okay. Yeah, of course.
00:12:18 --> 00:12:22 Go ahead. I think that that stuck out to me.
00:12:23 --> 00:12:26 One of the things that we asked them is, you
00:12:26 --> 00:12:27 know, what are the things that you're concerned
00:12:27 --> 00:12:31 about, about AI? Yes, we also asked them how
00:12:31 --> 00:12:33 many people, you know, are you using it a lot?
00:12:33 --> 00:12:35 And no surprises there. A lot of people do use
00:12:35 --> 00:12:39 it. And also people trust AI. I didn't don't
00:12:39 --> 00:12:41 think that was a huge surprise, but a couple
00:12:41 --> 00:12:43 of things that surprised me. So a lot of people
00:12:43 --> 00:12:46 see concerns with AI. I think CFOs are not the
00:12:46 --> 00:12:50 only ones. a broad spectrum in society. But 34
00:12:50 --> 00:12:54 % also said that they were afraid of lack of
00:12:54 --> 00:12:58 skills and expertise would come from AI. And
00:12:58 --> 00:13:00 it surprised me in the way, because when coming
00:13:00 --> 00:13:05 to new technology shift, it would be to think
00:13:05 --> 00:13:07 that people are gonna keep the same knowledge
00:13:07 --> 00:13:11 of the old ways of doing things. and also adding
00:13:11 --> 00:13:13 the new, I think would be a little bit naive
00:13:13 --> 00:13:16 to think. And it's almost, to use an analogy,
00:13:17 --> 00:13:19 using the pen and the paper compared to today
00:13:19 --> 00:13:22 when you're typing things on a PC or tablet.
00:13:23 --> 00:13:27 In that context, some expertise and skill will
00:13:27 --> 00:13:32 change and will be not needed anymore. And yeah,
00:13:32 --> 00:13:34 okay, there could be that your AI tools are not
00:13:34 --> 00:13:37 working and you need to find other ways to solve
00:13:37 --> 00:13:40 the task. But I think it's kind of natural. I
00:13:40 --> 00:13:42 mean, I have the benefit of having worked in
00:13:42 --> 00:13:45 a few different countries, as you say, and I
00:13:45 --> 00:13:47 speak a few different languages as well. And
00:13:47 --> 00:13:50 I've had great pleasure. of that, interacting
00:13:50 --> 00:13:52 and learning the culture better. But there is
00:13:52 --> 00:13:55 also a world today where most of that you can
00:13:55 --> 00:13:58 do with AI. And does that mean, for example,
00:13:58 --> 00:14:01 that speaking six, seven languages tomorrow is
00:14:01 --> 00:14:03 going to be equally important as yesterday? Probably
00:14:03 --> 00:14:07 not. So I think that that's kind of a natural
00:14:07 --> 00:14:10 change. The other thing that surprised me a bit,
00:14:10 --> 00:14:13 we asked the C4 leaders in terms of priorities
00:14:13 --> 00:14:16 of making AI investments and finance. Where do
00:14:16 --> 00:14:19 you see them? Where do you stack rank them? And
00:14:19 --> 00:14:23 the 40, I think 45 % or 43 % to be exact said,
00:14:24 --> 00:14:27 you know, we're going to invest a lot in FP &A.
00:14:27 --> 00:14:29 So financial reporting, forecasting and analysis,
00:14:29 --> 00:14:34 but only 30%. or 34 % for AR looking at transaction
00:14:34 --> 00:14:37 processing. So significantly more thinking that
00:14:37 --> 00:14:40 they're going to invest more in AI in FP &A.
00:14:40 --> 00:14:44 And for me, it kind of surprised me from two
00:14:44 --> 00:14:46 perspectives. A, transaction processing, that's
00:14:46 --> 00:14:49 where you have a lot of people, a lot of repetitive
00:14:49 --> 00:14:53 tasks. What I would think here is lots of potential
00:14:53 --> 00:14:56 for actually driving meaningful impact with AI
00:14:56 --> 00:15:01 and bringing people further up to strategic finance.
00:15:02 --> 00:15:04 But kind of thinking about that, is it because
00:15:04 --> 00:15:06 organizations think they've already done it?
00:15:06 --> 00:15:08 My experience during meeting a lot of customers
00:15:08 --> 00:15:11 is there is a lot more to be done in that. territory.
00:15:11 --> 00:15:14 People might have administration but not automation.
00:15:15 --> 00:15:17 The second thing is I think that the FP &A agenda
00:15:17 --> 00:15:21 perhaps is closer to the CFO transactional processing
00:15:21 --> 00:15:25 and hence it more inclined to invest and prioritize
00:15:25 --> 00:15:28 FP &A, which I don't think I don't say it's not
00:15:28 --> 00:15:30 important. It is important. But I would think
00:15:30 --> 00:15:32 that actually you have more opportunities to
00:15:32 --> 00:15:35 work on the transactional processing part of
00:15:35 --> 00:15:38 it. So that's two kind of small little surprises.
00:15:38 --> 00:15:42 And if I can mention the biggest surprise of
00:15:42 --> 00:15:45 them all from the study, it is we asked people
00:15:45 --> 00:15:48 to think three to five years ahead. And how do
00:15:48 --> 00:15:51 you think the final staffing of the organization
00:15:51 --> 00:15:52 is going to change? Do you believe it's going
00:15:52 --> 00:15:54 to be roughly the same number? Is it going to
00:15:54 --> 00:15:56 be more? Is it going to be less? And I think
00:15:56 --> 00:16:01 63 % said it's going to be at the same level
00:16:01 --> 00:16:04 or more. So actually 33 % said it would be more
00:16:04 --> 00:16:07 headcount and only 34 % said less. And given
00:16:07 --> 00:16:10 everything else we hear in society today about
00:16:10 --> 00:16:14 AI and what it will do to to labor, that was
00:16:14 --> 00:16:17 a surprising insight that so many thought that
00:16:17 --> 00:16:21 there would be no change or an increased staffing.
00:16:22 --> 00:16:24 So there you have it. Three different. You asked
00:16:24 --> 00:16:26 me for one, you got three. Thank you. Buy one,
00:16:26 --> 00:16:29 get two free. We love deals in the U .S. There
00:16:29 --> 00:16:33 you go. There you go. Um, two things though,
00:16:33 --> 00:16:36 like let's take a quick break to talk about invoice
00:16:36 --> 00:16:39 processing. Maybe your finance team still enters
00:16:39 --> 00:16:42 invoice data by hand, or maybe you've moved to
00:16:42 --> 00:16:45 OCR scanning, but someone still has to build
00:16:45 --> 00:16:48 templates, maintain them and fix what the system
00:16:48 --> 00:16:51 misses. Today's sponsor Rellian takes a different
00:16:51 --> 00:16:54 approach with AI invoice capture. Think chat
00:16:54 --> 00:16:57 GPT, but for invoices. It reads the information
00:16:57 --> 00:17:00 on an invoice without needing a template or upfront
00:17:00 --> 00:17:03 training. From there, William can route approvals,
00:17:03 --> 00:17:06 match invoices to purchase orders, and help manage
00:17:06 --> 00:17:10 payments all connected to your ERP. This will
00:17:10 --> 00:17:13 allow you to give your AP team less data entry
00:17:13 --> 00:17:15 and more room to focus on exceptions and decisions.
00:17:16 --> 00:17:19 See it in action at realign .com slash AP dash
00:17:19 --> 00:17:21 automation dash software. I'll drop the link
00:17:21 --> 00:17:24 in the show notes. Now let's get back to the
00:17:24 --> 00:17:27 episode. The last one, I'm surprised that, you
00:17:27 --> 00:17:30 know, I'm also surprised that people will think
00:17:30 --> 00:17:35 that there will be even more people. in department,
00:17:35 --> 00:17:38 but also the FP &A part. I'm not too surprised
00:17:38 --> 00:17:40 that there is more attention to FP &A. For me,
00:17:40 --> 00:17:44 it's more because it's almost like a show. It's
00:17:44 --> 00:17:46 something you can kind of do show and tell. And
00:17:46 --> 00:17:50 it's beautiful to have beautiful dashboards and
00:17:50 --> 00:17:53 things and graphs and scenarios that moves easily
00:17:53 --> 00:17:56 and commentary. I feel like that's the quote
00:17:56 --> 00:18:00 unquote sexy part of finance. And I think that
00:18:00 --> 00:18:02 that's probably also why, in addition to what
00:18:02 --> 00:18:04 you mentioned, because it's close to the CFO,
00:18:05 --> 00:18:07 that it may be getting the attention. But I do
00:18:07 --> 00:18:10 agree that the actual work, the run of the work
00:18:10 --> 00:18:14 is in the transactions. actions are not right.
00:18:14 --> 00:18:17 Doesn't matter how cute the dashboard is, is
00:18:17 --> 00:18:19 it really useful? That's right. And I would say,
00:18:19 --> 00:18:22 you know, one of the other things that came out
00:18:22 --> 00:18:25 of the report is that I think more than 80 %
00:18:25 --> 00:18:29 of the CFOs believe that the data in their organizations
00:18:29 --> 00:18:33 are so good that they can fully use AI on it
00:18:33 --> 00:18:37 for, for example, those forecasts or analytics
00:18:37 --> 00:18:39 around it. And I think, I mean, if that was the
00:18:39 --> 00:18:44 case, that is great indeed. My impression is
00:18:44 --> 00:18:47 that the data, at least what I've seen so far
00:18:47 --> 00:18:52 working with AI is that now we make numbers at
00:18:52 --> 00:18:55 democracy and everybody can run numbers through
00:18:55 --> 00:18:58 AI. But if you don't got your data in order,
00:18:59 --> 00:19:00 basically people are going to look at the same
00:19:00 --> 00:19:03 data set and come up with lots of different results.
00:19:03 --> 00:19:08 So there needs to be a fundamental investment
00:19:08 --> 00:19:10 into data platform and the structures around
00:19:10 --> 00:19:13 it so that you can trust what's in it and people
00:19:13 --> 00:19:16 with a number similar to thing you can interpret
00:19:16 --> 00:19:19 in many different ways for sure that you can
00:19:19 --> 00:19:22 spin the story but you don't necessarily want
00:19:22 --> 00:19:24 to argue what is the churn rate is it you know
00:19:24 --> 00:19:28 10 5 15 depending on how people have prompted
00:19:28 --> 00:19:32 the data then it becomes a problem in itself
00:19:32 --> 00:19:36 and I think that yes a lot of time has been kind
00:19:36 --> 00:19:38 of spent on driving dashboards. We've done that
00:19:38 --> 00:19:42 in our organization as well. But sometimes the
00:19:42 --> 00:19:46 investment to automate a dashboard that was already
00:19:46 --> 00:19:49 kind of semi -automated, the payback on that.
00:19:49 --> 00:19:52 is relatively little. True. So how are you using
00:19:52 --> 00:19:55 AI in your own finance team today? Yeah, thanks
00:19:55 --> 00:19:59 for asking. Maybe kind of starting with the transaction
00:19:59 --> 00:20:01 processing part and then we can kind of work
00:20:01 --> 00:20:05 through the whole spectrum. So obviously we use
00:20:05 --> 00:20:09 our own tool. for AP automation that kind of
00:20:09 --> 00:20:12 helps us with driving, capture, driving the invoices
00:20:12 --> 00:20:15 in with AI and the coding and workflows. So there
00:20:15 --> 00:20:19 we use it ourselves, which makes sense. We use
00:20:19 --> 00:20:22 from a kind of a transaction AR perspective,
00:20:22 --> 00:20:25 we use it in the deal flow. Not in all processes.
00:20:26 --> 00:20:29 I mean, we're now checking how we can do Dunningham.
00:20:29 --> 00:20:32 payment reminder letters, which we outsourced
00:20:32 --> 00:20:34 today. But now we're looking to bring it in -house
00:20:34 --> 00:20:37 and to drive AI through that. But we're doing,
00:20:37 --> 00:20:40 for example, sales commissions calculations.
00:20:40 --> 00:20:43 We're running that in AI with verification. from
00:20:43 --> 00:20:45 people in the end, because it's about paying
00:20:45 --> 00:20:48 money. We're doing it for partner kickbacks in
00:20:48 --> 00:20:51 our SaaS business. We use it when it comes to
00:20:51 --> 00:20:54 accounting. In the month -end close, we got lovely
00:20:54 --> 00:20:56 dashboards showing where we are with different
00:20:56 --> 00:20:58 steps in the process. We use bank reconciliation
00:20:58 --> 00:21:02 and other reconciliation with AI. We use for
00:21:02 --> 00:21:07 the monthly reporting pack. We use first various
00:21:07 --> 00:21:10 analysis to pick up the differences. We ask AI
00:21:10 --> 00:21:14 to do the first draft of the report. after we
00:21:14 --> 00:21:17 validate. And we also do it for our commercial
00:21:17 --> 00:21:21 analytics that we run AI. We also use it for
00:21:21 --> 00:21:25 cash flow reporting and forecasting for the next
00:21:25 --> 00:21:28 30 to 60 days. So there's lots of different usage
00:21:28 --> 00:21:31 areas, but more to come. More to come, I think.
00:21:31 --> 00:21:33 But so far, we've actually managed to touch a
00:21:33 --> 00:21:36 lot of the different processes in finance. When
00:21:36 --> 00:21:41 it comes to forecasting, Not so much yet. Certainly,
00:21:41 --> 00:21:43 we're using, we're running a budget process at
00:21:43 --> 00:21:47 the moment and using AI to kind of build some
00:21:47 --> 00:21:51 of the framework, asking AI to prompt and ask
00:21:51 --> 00:21:54 for replies, refining some of the analytics in
00:21:54 --> 00:22:00 it. But for sure, we can do more of AI usage
00:22:00 --> 00:22:02 in all areas. But that's a couple of examples.
00:22:03 --> 00:22:07 That's awesome. So how did you, like, actually
00:22:07 --> 00:22:10 get it inside your workflows? Like, what type
00:22:10 --> 00:22:15 of governance or boundaries or buy -in or, you
00:22:15 --> 00:22:17 know, how did you get people on board? How did
00:22:17 --> 00:22:20 you validate the tools you were using before,
00:22:20 --> 00:22:22 you know, knowing that it was the right time
00:22:22 --> 00:22:24 to bring, for example, AI in a process? Just
00:22:24 --> 00:22:28 curious to see what was your thought process
00:22:28 --> 00:22:30 and the steps you took to get there. Yeah, I
00:22:30 --> 00:22:33 know, great. Great question. For us, and for
00:22:33 --> 00:22:36 me, it started with us having a huge focus on
00:22:36 --> 00:22:39 on AI. So we've had an ambitious agenda for the
00:22:39 --> 00:22:43 past two years. And I remember before I joined
00:22:43 --> 00:22:46 this company, I was two and a half years ago
00:22:46 --> 00:22:49 at the kind of a big CFO event where they asked
00:22:49 --> 00:22:52 roughly 50 CFOs how much AI they use. At that
00:22:52 --> 00:22:54 time, there was one person raising their hands,
00:22:54 --> 00:22:56 you know, one of them. So things have obviously
00:22:56 --> 00:22:59 moved very, very fast. And some people haven't
00:22:59 --> 00:23:02 started yet either. And it's good to get started.
00:23:02 --> 00:23:05 But for us, it really started with a very clear
00:23:05 --> 00:23:09 agenda. of using AI in our product, getting AI
00:23:09 --> 00:23:13 usage across the different teams. I joined at
00:23:13 --> 00:23:16 the beginning of this year, and then we started
00:23:16 --> 00:23:19 to use AI in a few different parts of the team.
00:23:19 --> 00:23:22 But for me, it started with a clear focus on
00:23:22 --> 00:23:26 making everyone use it, including myself. And
00:23:26 --> 00:23:29 so you kind of have to lead by example using
00:23:29 --> 00:23:33 it. And number two, for some people who haven't
00:23:33 --> 00:23:37 used it, start small. but making sure that there
00:23:37 --> 00:23:40 are AI use cases in their OKRs or in their goals
00:23:40 --> 00:23:43 for the year and make sure to follow them up
00:23:43 --> 00:23:47 and creating a culture of it's okay to be at
00:23:47 --> 00:23:49 different levels, it's okay to test, it's okay
00:23:49 --> 00:23:52 to make mistakes, but really setting that culture
00:23:52 --> 00:23:56 of working with AI. That's what's been my focus
00:23:56 --> 00:23:58 this year and so far I think we've made great
00:23:58 --> 00:24:01 inroads. Okay. And at some point you were mentioning
00:24:01 --> 00:24:03 on how you were using AI in your team, you said
00:24:03 --> 00:24:08 when it comes to paying people, you let AI draft,
00:24:08 --> 00:24:11 but you validate first. Before you send your
00:24:11 --> 00:24:13 commentaries going to executive leadership, you
00:24:13 --> 00:24:15 let AI draft and then you validate it first.
00:24:16 --> 00:24:18 Can you speak to the importance of doing that
00:24:18 --> 00:24:22 even as AI gets better and better at what it
00:24:22 --> 00:24:27 does? Yeah, I can. And I think in the end, the
00:24:27 --> 00:24:30 accountability for finance stops with you as
00:24:30 --> 00:24:33 the CFO, right? So you can't come into a board
00:24:33 --> 00:24:36 meeting and say, oops, the number was wrong because
00:24:36 --> 00:24:41 of Claude. or because of Copilot or because of
00:24:41 --> 00:24:46 any other tool, the accountability is there with
00:24:46 --> 00:24:48 you. And sometimes the challenge with AI, we
00:24:48 --> 00:24:51 take a little bit of shortcuts, but when it comes
00:24:51 --> 00:24:55 to actually paying money. out to employees or
00:24:55 --> 00:24:58 even paying invoices for that matter, you want
00:24:58 --> 00:25:02 to make sure that it is right. And it's similar
00:25:02 --> 00:25:06 to approving an invoice. If you set up POs, yeah,
00:25:07 --> 00:25:10 you can fully automate it, all right, but replacing
00:25:10 --> 00:25:13 the full approval of big spend, it's problematic.
00:25:13 --> 00:25:16 sometimes because the box stops. And if you're
00:25:16 --> 00:25:21 sending a message and into a board forecast,
00:25:21 --> 00:25:24 you kind of need to know what it is that you're
00:25:24 --> 00:25:26 sending. It's the same as going into a board
00:25:26 --> 00:25:28 meeting and there's 40 slides and you've had
00:25:28 --> 00:25:31 your team prepare them. And you've basically
00:25:31 --> 00:25:33 looked at one slide before going into that meeting.
00:25:34 --> 00:25:37 That doesn't work. You still need to own the
00:25:37 --> 00:25:40 message you need to own what's there. So regardless
00:25:40 --> 00:25:42 who prepared them, if it was AI, if it was your
00:25:42 --> 00:25:45 team, you need... you represented, right? So
00:25:45 --> 00:25:49 I think for those reasons, you want a human in
00:25:49 --> 00:25:52 the loop, if we use that expression, to validate
00:25:52 --> 00:25:56 some of those important messages. Yeah, it definitely
00:25:56 --> 00:25:58 won't work because you always have that one or
00:25:58 --> 00:26:00 two board members that will be like, on slide
00:26:00 --> 00:26:06 23, on the third bullet. Of course, that will
00:26:06 --> 00:26:09 still happen. I mean, I remember also part of
00:26:09 --> 00:26:11 my career. I remember going into one of those
00:26:11 --> 00:26:14 big business reviews for the budget, and it was
00:26:14 --> 00:26:16 400 slides or whatever that they'd received.
00:26:16 --> 00:26:19 And then it was exactly on slide 150. The number
00:26:19 --> 00:26:22 is wrong. Yeah, you know, we all make mistakes.
00:26:23 --> 00:26:26 Yes, we do. But some commas and decimals, you
00:26:26 --> 00:26:28 know, they can always be wrong. The core messaging,
00:26:28 --> 00:26:30 though, if you haven't read them through, you
00:26:30 --> 00:26:33 kind of need to get that right. And so if you
00:26:33 --> 00:26:36 let AI do the work for you and you haven't been
00:26:36 --> 00:26:39 through it. You haven't done your homework in
00:26:39 --> 00:26:43 reality. True. Now AI isn't cheap, right? So
00:26:43 --> 00:26:46 AI isn't cheap. A lot of CFOs are getting hit
00:26:46 --> 00:26:49 by big invoices for AI token usage. How do you
00:26:49 --> 00:26:52 make sure that the cost of using AI does not
00:26:52 --> 00:26:55 get out of hand? Yes, and we have also seen that
00:26:55 --> 00:26:59 this is increasing and we've used multiple tools.
00:26:59 --> 00:27:02 So we've done a couple of things. Now we made
00:27:02 --> 00:27:05 some choices around what application, I'm not
00:27:05 --> 00:27:08 going to name the application, but we're going
00:27:08 --> 00:27:12 for one for most of the people. And that's also,
00:27:12 --> 00:27:14 I think it's helpful for people because again,
00:27:14 --> 00:27:16 when I came into this organization where we're
00:27:16 --> 00:27:18 using multiples, people didn't really know where
00:27:18 --> 00:27:21 to start. And so let's not get confused. This
00:27:21 --> 00:27:23 is the one we're going to pick as an organization
00:27:23 --> 00:27:24 and we're going to run with that. until further
00:27:24 --> 00:27:27 notice. But back to the costing. So in our case,
00:27:27 --> 00:27:31 we have a spend limit for every individual, which
00:27:31 --> 00:27:34 is up until that limit, you can essentially use
00:27:34 --> 00:27:38 tokens. So first, this is the seed -based costing.
00:27:38 --> 00:27:41 And then that's, let's say, one for a certain
00:27:41 --> 00:27:44 dollar. And then you have the limits above. Let's
00:27:44 --> 00:27:48 say for the sake of usage, say $50. And up until
00:27:48 --> 00:27:52 that, you basically can use the tokens and you
00:27:52 --> 00:27:55 can override. But once you come past a certain
00:27:55 --> 00:27:58 threshold, let's say $50, then only very few
00:27:58 --> 00:28:01 people can approve the additional token usage
00:28:01 --> 00:28:04 in the organization. So that's kind of a way
00:28:04 --> 00:28:07 to track that. And on a regular basis, reviewing
00:28:07 --> 00:28:10 also those, are there some individuals actually,
00:28:11 --> 00:28:13 maybe somebody coding and engineering, well,
00:28:14 --> 00:28:16 $50 a month isn't gonna be enough. We need to
00:28:16 --> 00:28:18 increase that limit, but you do it deliberately.
00:28:19 --> 00:28:22 And then I would say, you know, with AI, you
00:28:22 --> 00:28:25 need to experiment, but there are different LLMs
00:28:25 --> 00:28:28 that you can use. I mean, I come across this
00:28:28 --> 00:28:30 every day. Oh, for some reason, I've used this
00:28:30 --> 00:28:33 higher level LLM for tasks. Maybe I don't need
00:28:33 --> 00:28:36 it. So I hit my token ceiling. And then you go
00:28:36 --> 00:28:40 back and change that LLM. And maybe, you know,
00:28:40 --> 00:28:42 one of the most frustrating things is let's say
00:28:42 --> 00:28:44 that you're preparing a presentation for tomorrow.
00:28:44 --> 00:28:47 And then all of a sudden you use your tokens
00:28:47 --> 00:28:49 and it's in the evening. You can't reach in them
00:28:49 --> 00:28:52 on and you get frustrated and you look at maybe
00:28:52 --> 00:28:55 there are other AI tools I can use. But sometimes
00:28:55 --> 00:28:58 when you hit the ceiling, it's also maybe time
00:28:58 --> 00:29:02 for reflection. Perhaps you can wait, you know,
00:29:03 --> 00:29:04 30 minutes or an hour. There are other things
00:29:04 --> 00:29:07 you can do and actually maybe think about what
00:29:07 --> 00:29:09 it is that I need from the AI tool. So sometimes
00:29:09 --> 00:29:12 it can actually, I found, be beneficial that
00:29:12 --> 00:29:15 you hit the ceiling. I said, okay, are there
00:29:15 --> 00:29:19 any other ways I can solve this task? Maybe change
00:29:19 --> 00:29:23 the LMM. Maybe it isn't just by prompting AI
00:29:23 --> 00:29:27 another 15 questions to get to my answer. We
00:29:27 --> 00:29:31 do a combination of things. Basically trying
00:29:31 --> 00:29:34 to streamline the AI tools that we do use, setting
00:29:34 --> 00:29:38 limits and also token levels and monitoring them
00:29:38 --> 00:29:41 closely with a controlled approval path for them.
00:29:41 --> 00:29:46 Makes sense, because that cost is probably going
00:29:46 --> 00:29:48 to get out of hands for many people, especially
00:29:48 --> 00:29:50 as you said, if you don't hit your limit, you'll
00:29:50 --> 00:29:52 keep going, going, going, going. It's almost
00:29:52 --> 00:29:55 addictive like me on Netflix. Like you start
00:29:55 --> 00:29:57 watching, watching, watching. You just want more
00:29:57 --> 00:30:00 and more and more. And, you know, it's like in
00:30:00 --> 00:30:03 the old days, you were using your mobile phone
00:30:03 --> 00:30:04 and roaming and traveling across the Atlantic.
00:30:04 --> 00:30:06 And all of a sudden you got this massive phone
00:30:06 --> 00:30:10 bill. How did that happen? Well, you know, or
00:30:10 --> 00:30:14 using, as you say, data using Netflix or any
00:30:14 --> 00:30:17 other streaming to oops, my data is gone. So
00:30:17 --> 00:30:19 you just need to be a little bit considerate
00:30:19 --> 00:30:24 and planned in that's nothing strange. Yep, very
00:30:24 --> 00:30:28 true. So how do you balance like experimentation
00:30:28 --> 00:30:32 like getting people to try it. I know you mentioned
00:30:32 --> 00:30:34 the limit, but how do you balance experimenting
00:30:34 --> 00:30:37 with AI versus having the right governance versus
00:30:37 --> 00:30:40 also the need to demonstrate value? Because I'm
00:30:40 --> 00:30:42 giving you a limit every day or every month,
00:30:42 --> 00:30:44 and you are reaching the limit. Like, what are
00:30:44 --> 00:30:47 you using it for? How is it useful for the organization?
00:30:47 --> 00:30:50 Very, very good. Very good question. Because
00:30:50 --> 00:30:55 in the end, people need to be curious, need to
00:30:55 --> 00:30:58 experiment with the tool to drive the value.
00:30:58 --> 00:31:02 But I would argue you can try and test also with
00:31:02 --> 00:31:05 different LLM models. It doesn't have to be the
00:31:05 --> 00:31:08 kind of top spec models. So there is actually
00:31:08 --> 00:31:10 a way that you can handle that within existing
00:31:10 --> 00:31:13 limits. But then when you kind of go beyond,
00:31:13 --> 00:31:17 and if it's going to be a real big usage, then
00:31:17 --> 00:31:19 I would think like in any other scenario in a
00:31:19 --> 00:31:22 company, It's an investment you make of sorts,
00:31:22 --> 00:31:24 right? So you need to demonstrate what's the
00:31:24 --> 00:31:27 business impact, what's the business case for
00:31:27 --> 00:31:32 having a huge increase in tokens in one part
00:31:32 --> 00:31:33 of the organization or a function, for example.
00:31:34 --> 00:31:37 And if you can demonstrate through the use of
00:31:37 --> 00:31:39 a business case, okay, this is why we're gonna
00:31:39 --> 00:31:43 have need to use these level of tokens for six
00:31:43 --> 00:31:45 months or whatever. It's because we're running
00:31:45 --> 00:31:47 this project. that will get these benefits. So
00:31:47 --> 00:31:51 it's kind of doing it in any other way you would
00:31:51 --> 00:31:54 make any other investment in reality. And of
00:31:54 --> 00:31:57 course, there's also the case of, you were back
00:31:57 --> 00:32:01 to Netflix, how many people are using their own
00:32:01 --> 00:32:04 mobile phone usage or AI for personal use, for
00:32:04 --> 00:32:07 example, designing, making drawings of their
00:32:07 --> 00:32:11 house project with Claude at home. driving tokens.
00:32:12 --> 00:32:14 And you know, that's the same as any other thing
00:32:14 --> 00:32:17 that, you know, you bring your PC at home, you
00:32:17 --> 00:32:19 bring your phone at home, you still have to act
00:32:19 --> 00:32:23 responsibly with this company property. And the
00:32:23 --> 00:32:26 same goes for AI. And that's why you also need
00:32:26 --> 00:32:30 to monitor the usage and when it's being used.
00:32:30 --> 00:32:35 I don't like to control society, but as with
00:32:35 --> 00:32:37 any other cost items, you need to kind of be
00:32:37 --> 00:32:41 on top of things. True. True. Now I'm curious
00:32:41 --> 00:32:43 to hear, since you've led finance in several
00:32:43 --> 00:32:46 countries, what would you say changed from one
00:32:46 --> 00:32:48 country to the next, and what stays the same?
00:32:48 --> 00:32:51 So first, what are all the countries? One, and
00:32:51 --> 00:32:53 all the languages you speak. But curious to hear
00:32:53 --> 00:32:55 some things that you notice in one versus another,
00:32:55 --> 00:32:58 and some things that you can say are universal
00:32:58 --> 00:33:01 to every finance team. Yeah, no, that's good.
00:33:01 --> 00:33:05 Yeah, no, to mention a few countries. So I'm
00:33:05 --> 00:33:09 by birth, I'm Swedish. So I've spent part of
00:33:09 --> 00:33:13 my career here. Ireland, I worked for a couple
00:33:13 --> 00:33:16 of years. France, I worked in Belgium, I worked
00:33:16 --> 00:33:20 in the Netherlands. I have worked in Germany,
00:33:21 --> 00:33:24 Switzerland and Russia. So I haven't worked in
00:33:24 --> 00:33:26 your lovely, lovely country. I've traveled there
00:33:26 --> 00:33:29 many times, but never worked there. Okay. So,
00:33:29 --> 00:33:33 you know, what is different and what stays the
00:33:33 --> 00:33:35 same? So obviously, you know, regulations are
00:33:35 --> 00:33:37 going to be different in every country you're
00:33:37 --> 00:33:39 in. Tax systems are going to be different. There
00:33:39 --> 00:33:41 are similarities. You know, there's VAT value
00:33:41 --> 00:33:44 added tax in a lot of parts of Europe. There's
00:33:44 --> 00:33:47 sales tax in the US, GST over in Australia. So
00:33:47 --> 00:33:50 of course, those things will change. I think
00:33:50 --> 00:33:53 also, you know, obviously, the culture, the leadership
00:33:53 --> 00:33:55 style is going to be different. Although I think
00:33:55 --> 00:33:58 in some contexts, I've worked in the same kind
00:33:58 --> 00:34:01 of corporation in different parts of the globe.
00:34:01 --> 00:34:03 And then you kind of might have leadership traits
00:34:03 --> 00:34:07 from the corporation as such. But otherwise that
00:34:07 --> 00:34:09 change. I think also the way you kind of look
00:34:09 --> 00:34:12 at risk and assess risk might be a little bit
00:34:12 --> 00:34:14 different. The risk appetite might be different.
00:34:15 --> 00:34:19 Now, I think what doesn't change is I think what
00:34:19 --> 00:34:23 true good finance is. An organization with high
00:34:23 --> 00:34:26 integrity. I think that goes across the board.
00:34:27 --> 00:34:29 An organization that partners with the rest of
00:34:29 --> 00:34:34 the organization. An organization that understands
00:34:34 --> 00:34:36 the business and contributes to the business.
00:34:37 --> 00:34:40 That doesn't change. And an organization that
00:34:40 --> 00:34:44 is transparent with its assumptions. Doesn't
00:34:44 --> 00:34:48 change either. regardless of what culture and
00:34:48 --> 00:34:51 country you work in. So the true fundamentals
00:34:51 --> 00:34:56 of finance stays the same in my view. So the
00:34:56 --> 00:34:58 more things change, the more they stay the same
00:34:58 --> 00:35:02 in reality. And I think having worked in all
00:35:02 --> 00:35:04 these different countries, people always say,
00:35:04 --> 00:35:07 what's changed from all of these? And I think
00:35:07 --> 00:35:11 sometimes you stay true to finance and what finance
00:35:11 --> 00:35:14 is good at, and you deliver that in different
00:35:14 --> 00:35:18 countries. And that's why I say some things definitely
00:35:18 --> 00:35:21 don't change. I think the finest professionals
00:35:21 --> 00:35:24 that I've met in these countries, they have all
00:35:24 --> 00:35:26 those characteristics that I've just listed.
00:35:26 --> 00:35:29 And do you feel like we also all have the same
00:35:29 --> 00:35:34 struggles, like maybe lack of capacity or resources
00:35:34 --> 00:35:39 or time? Absolutely. We're always short -staffed.
00:35:40 --> 00:35:43 We're always struggling with poor data, struggling
00:35:43 --> 00:35:46 how to work with a business partner, how to convince
00:35:46 --> 00:35:50 the business partner of a certain message, deadlines
00:35:50 --> 00:35:52 being short, month -end close is going to be
00:35:52 --> 00:35:56 tighter and tighter, forecasting budgets. Yes,
00:35:56 --> 00:36:00 all of that, the same. Oh, yeah, yeah, that's
00:36:00 --> 00:36:02 depressing. Do you think AI is going to help
00:36:02 --> 00:36:06 with that part? Is there hope? No, no. So if,
00:36:06 --> 00:36:08 you know, if we have worked, you know, I remember
00:36:08 --> 00:36:10 starting my career, maybe we closed on day 15,
00:36:10 --> 00:36:15 day 20, the day after the month after. And then,
00:36:15 --> 00:36:18 you know, we successfully brought it down. I
00:36:18 --> 00:36:20 guess you can close before day one. Maybe you
00:36:20 --> 00:36:22 can close at minus three. It all has consequences.
00:36:23 --> 00:36:27 So, yeah, I mean, I think AI will help for us
00:36:27 --> 00:36:31 to close a little bit earlier. Will AI help to?
00:36:31 --> 00:36:34 for us to do more forecasting? Yeah, potentially,
00:36:34 --> 00:36:36 but you also need to keep track of all the forecast
00:36:36 --> 00:36:39 you do. Maybe I can do that for us. Will it remove
00:36:39 --> 00:36:42 the need for a budget? I personally don't think
00:36:42 --> 00:36:45 so. I think the budget is setting targets. We're
00:36:45 --> 00:36:48 always going to need to set targets for an organization,
00:36:48 --> 00:36:52 maybe more rapidly in the future than in the
00:36:52 --> 00:36:54 past. But yeah, it will certainly help, but it
00:36:54 --> 00:36:57 won't fundamentally remove. some of the tasks
00:36:57 --> 00:37:01 we in finance, we will still work to set governance
00:37:01 --> 00:37:04 structures work to challenge the numbers being,
00:37:04 --> 00:37:07 you know, asking for, you know, what's the white
00:37:07 --> 00:37:10 elephant in the room, you know, being being provocative,
00:37:10 --> 00:37:14 and also being the one that's very transparent
00:37:14 --> 00:37:17 with numbers and owning that message. I don't
00:37:17 --> 00:37:19 think any of that is is is going going away,
00:37:20 --> 00:37:22 although we might use slightly different tools.
00:37:23 --> 00:37:26 Okay, so what would you say is one lesson that
00:37:26 --> 00:37:29 you learn working abroad that you still carry
00:37:29 --> 00:37:32 with you till this day as a leader? Yeah, so
00:37:32 --> 00:37:36 I think you have to be cognizant and I mentioned
00:37:36 --> 00:37:39 that their cultures are different. It's very
00:37:39 --> 00:37:42 presumptuous to think that you coming into that
00:37:42 --> 00:37:46 culture in that country that you know more than
00:37:46 --> 00:37:48 them how that culture works. So I think that
00:37:48 --> 00:37:52 the One of the core lessons I brought with me
00:37:52 --> 00:37:55 from that is if you want to make a change and
00:37:55 --> 00:37:58 that goes the same if you kind of go into a different
00:37:58 --> 00:38:00 country or you work maybe in a different industry
00:38:00 --> 00:38:04 that you haven't worked before. Be cognizant
00:38:04 --> 00:38:06 of people being there kind of knowing how it's
00:38:06 --> 00:38:10 been done before. Don't immediately change before
00:38:10 --> 00:38:13 kind of bouncing that idea and asking people
00:38:13 --> 00:38:16 I'm not saying that you're oh if I'm gonna reduce
00:38:16 --> 00:38:18 people I'm gonna ask people what they think about
00:38:18 --> 00:38:20 it that's not what I'm saying but that that you
00:38:20 --> 00:38:23 know how is it done in this in this particular
00:38:23 --> 00:38:27 environment be cognizant of that that's certainly
00:38:27 --> 00:38:31 a lesson that that I will bring with me for all
00:38:31 --> 00:38:35 times and be be humble be humble that you're
00:38:35 --> 00:38:38 you're not an expert and you don't pretend to
00:38:38 --> 00:38:42 be they know it all in that cultural context
00:38:42 --> 00:38:46 either. Nice. So some lessons. That's awesome.
00:38:47 --> 00:38:50 What would you say is an advice you usually give
00:38:50 --> 00:38:53 people, especially if there was a CFO, whether
00:38:53 --> 00:38:55 you're just starting with AI or just taking on
00:38:55 --> 00:38:58 a new leadership role? What would be your advice
00:38:58 --> 00:39:02 to them? So I think I love to say be humble because
00:39:02 --> 00:39:07 I think that's important in any context. The
00:39:07 --> 00:39:11 second thing is Be curious. I think with every
00:39:11 --> 00:39:14 role I have taken on, regardless of what's been,
00:39:14 --> 00:39:17 I have been so curious and excited about learning
00:39:17 --> 00:39:20 these new things. Whether that is kind of going
00:39:20 --> 00:39:25 into setting up a tax manual or whether that's
00:39:25 --> 00:39:29 setting up a new procurement process or a new
00:39:29 --> 00:39:32 forecasting process or how we partner with the
00:39:32 --> 00:39:35 business. So I think going into the mindset of
00:39:35 --> 00:39:39 being humble. You don't know it all, but also
00:39:39 --> 00:39:41 with the mindset of trying to learn it and be
00:39:41 --> 00:39:44 really curious about learning the environment,
00:39:44 --> 00:39:48 but fundamentally learning from the people that's
00:39:48 --> 00:39:51 there and being curious about the people. I mean,
00:39:52 --> 00:39:54 in the end, every organization, finance including,
00:39:55 --> 00:39:58 is about the people that's there. So it starts
00:39:58 --> 00:40:01 with being interested. interested in the people
00:40:01 --> 00:40:04 that are around you and that together with you
00:40:04 --> 00:40:08 will deliver the work. And when it comes to AI,
00:40:09 --> 00:40:14 I think both of those traits are the same. You
00:40:14 --> 00:40:17 kind of cannot expect things to change and everybody
00:40:17 --> 00:40:20 to embrace AI if you don't do it yourself. So
00:40:20 --> 00:40:24 you have to roll up, popular expression, roll
00:40:24 --> 00:40:28 up your sleeves and get stuck in there, but you
00:40:28 --> 00:40:32 do. And in the context of being humble, you also
00:40:32 --> 00:40:34 have to be humble about, you know, you're not
00:40:34 --> 00:40:37 an expert. And also if you want people to come
00:40:37 --> 00:40:39 with you on the journey, and I believe it is
00:40:39 --> 00:40:42 important people are on the journey, showcase
00:40:42 --> 00:40:47 that you yourself are learning and here are my
00:40:47 --> 00:40:50 flaws. And I think it's important to be inclusive.
00:40:51 --> 00:40:54 I don't like the expression of saying be on the
00:40:54 --> 00:40:57 bus or be off the bus. I think that's deeply
00:40:59 --> 00:41:02 putting people away. I think with AI, it's important
00:41:02 --> 00:41:05 to be inclusive. I do fundamentally believe that
00:41:05 --> 00:41:08 people can learn this skill as well. So make
00:41:08 --> 00:41:11 sure that you give people the opportunity and
00:41:11 --> 00:41:13 believe that they can do it. So being inclusive
00:41:13 --> 00:41:16 when it comes to AI is one of the messages I
00:41:16 --> 00:41:18 would really recommend people to think about.
00:41:18 --> 00:41:22 when they're stepping into a role of CFO or in
00:41:22 --> 00:41:26 this modern world of AI. Yes, thank you so much
00:41:26 --> 00:41:29 for sharing my last question for you. What is
00:41:29 --> 00:41:31 your favorite thing to do outside of work? You
00:41:31 --> 00:41:36 know, I have a great passion for wine and particularly
00:41:37 --> 00:41:41 Red wine so, you know, I live up north here in
00:41:41 --> 00:41:44 Sweden and would you would you think that we
00:41:44 --> 00:41:48 produce wines? Not so much, right? Thinking of
00:41:48 --> 00:41:51 Napa Valley and maybe France and Italy but but
00:41:51 --> 00:41:54 actually I do grow some vines and I've made wine
00:41:54 --> 00:41:59 and not so great quality, but I love, that's
00:41:59 --> 00:42:03 my big passion. And at least for now, and has
00:42:03 --> 00:42:05 been for a few years, but I'm also a little bit
00:42:05 --> 00:42:08 of similar, I get to know different things and
00:42:08 --> 00:42:11 I get passionate about that. But for the moment,
00:42:11 --> 00:42:15 and has been for a few years now, wine in all
00:42:15 --> 00:42:18 its different ways of making it, and of course,
00:42:19 --> 00:42:22 drinking it. So that's my passion outside of
00:42:22 --> 00:42:25 work. That is amazing. How do you keep the vine
00:42:25 --> 00:42:29 in the winters of Sweden? They do survive, would
00:42:29 --> 00:42:32 you believe, but there's different varieties
00:42:32 --> 00:42:34 that you need to use. You can't use your Pinot
00:42:34 --> 00:42:37 Noir and your Cabernet Sauvignon. You have to
00:42:37 --> 00:42:43 use a kind of local varieties of vine. But then
00:42:43 --> 00:42:46 you kind of work. So the quality, it's easier
00:42:46 --> 00:42:49 to make sparkling wine quality up here, but even
00:42:49 --> 00:42:53 red wine is possible. Thank you. Thank you so
00:42:53 --> 00:42:56 much for sharing. I guess for us in the US, we
00:42:56 --> 00:42:57 got to learn more about Sweden because we had
00:42:57 --> 00:43:01 Zlatan here for the World Cup. Oh, yes, indeed.
00:43:01 --> 00:43:04 Indeed. That's right. That's right. We love it.
00:43:04 --> 00:43:08 We love Zlatan. We love Zlatan. Yes. Well, thank
00:43:08 --> 00:43:10 you. Thank you so much for being on the show.
00:43:10 --> 00:43:13 It was super, super helpful to have your perspective,
00:43:13 --> 00:43:17 especially you being in the midst of what AI
00:43:17 --> 00:43:19 is bringing to finance. So thank you so much
00:43:19 --> 00:43:22 for being on the show. Thanks for inviting me.
00:43:22 --> 00:43:24 It was lovely to share some perspectives having
00:43:24 --> 00:43:28 a great dialogue with you. Thank you. That's
00:43:28 --> 00:43:31 it for today's episode of the Diary of a CFO
00:43:31 --> 00:43:34 Podcast. If you got something from this conversation,
00:43:34 --> 00:43:36 share it with someone in your world who will
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