Why AI is a Game-Changer for Finance Professionals - Insights from Christian Martinez
In this episode of the Future Finance Show, hosts Paul Barnhurst and Glenn Hopper sit down with Christian Martinez, a finance transformation expert and AI enthusiast, to explore the intersection of finance, artificial intelligence, and data analytics. The discussion covers the growing importance of AI and Python in finance, the benefits of leveraging dashboards for financial storytelling, and how professionals can enhance their skill sets to stay ahead in a rapidly evolving industry. Christian shares his journey, the practical applications of AI in FP&A, and offers insights into how Python, integrated with Excel, is revolutionizing finance functions.
Christian Martinez is a Finance Transformation Senior Manager at Kraft Heinz, with extensive experience across Europe, Australia, and South America. Known for his expertise in combining finance, Python, AI, and machine learning, Christian is passionate about teaching others how to harness these tools to improve financial analysis and decision-making. He recently launched a newsletter, "AI for FP&A and Finance," where he shares practical AI applications tailored for finance professionals.
In this episode, you will learn:
The critical role of AI and Python in transforming financial planning and analysis (FP&A).
How to leverage dashboards like Tableau and Power BI for effective financial storytelling.
The evolving landscape of AI tools in finance and the growing need for data literacy.
Practical tips on integrating Python with Excel to enhance financial operations.
The importance of continuous learning and staying updated with the latest technology trends in finance.
Follow Christian:
LinkedIn:https://www.linkedin.com/in/christianmartinezthefinancialfox/
Website: https://linktr.ee/Thefinancialfox
Blog: https://christianmartinezfinancialfox.medium.com/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://qflow.ai/future-finance
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
In Today’s Episode:
[01:51] - Introduction and context setting for discussing the impact of AI on finance
[08:01] - Introduction to Christian Martinez, highlighting his unique expertise in finance, Python, and AI.
[09:12] - Christian’s newsletter and practical AI tools for finance professionals
[15:39] - The Power of dashboards in financial analysis
[25:05] - A discussion on the value of coding skills in finance
[29:38] - Python in Excel: A game changer
[37:51] - Getting personal: Christian’s hidden talents
[41:52] - Final remarks from Christian and closing thoughts
Full Show Transcript
[00:00:00] Host 2: Glenn Hopper: If leaders are to use algorithms, they must understand the data upon which their decisions are based and the logic on which they are built.
[00:00:08] Host 1: Paul Barnhurst: Any conversation around technology today has to include generative AI.
[00:00:13] Guest: Christian Martinez: Artificial intelligence can be very utilized and leveraged in FP&A, and I started to have a lot of different articles, like all over the place in my medium, in different blogs and so on. I have to say that I think that these days most people definitely know about it, but one of the things that I really think is very useful, it's a good dashboard that can tell a financial story.
[00:00:37] Host 1: Paul Barnhurst: So I would think it's much higher in small to midsize companies than it is enterprise people that have spent their career in enterprise. So I think there's a little bit of industry segmentation. You know, if you think about.
[00:00:48] Host 2: Glenn Hopper: Tableau and Power BI, there's sort of a drag and drop component to it. So you may not you know, you can just learn the drag and drop without really understanding. If you don't know anything about Python or anything about coding, it's going to be hard for you to even get a generative AI to write code for you that you can run in any kind of environment.
[00:01:05] Host 1: Paul Barnhurst: Welcome to the Future Finance show, where we talk about.
[00:01:09] Robotic Intro: Everything from business analytics to superintelligence and everything in between.
[00:01:22] Host 1: Paul Barnhurst: Future finance is brought to you by Qflow.AI, the strategic finance platform solving the toughest part of planning and analysis. B2b revenue aligned sales, marketing and finance seamlessly speed up decision making and lock in accountability with Qflow.AI
[00:01:51] Host 2: Glenn Hopper: In this week's episode, we welcome Christian Martinez. Christian is one of those rare unicorns who understands finance and Python code and artificial intelligence and machine learning with equal skill. Also in this episode, Paul talks about the value of learning AI, and I talk about OpenAI's steps towards artificial general intelligence. Thanks for joining and let's dive on in. All of the great endeavors in human history started with the decision from early voyages, across vast and unknown oceans, to the discovery of the double helix structure of DNA. Our choices have defined not only the world in which we live, but our humanity itself. For leaders, bringing a vision to fruition requires myriad decisions, big and small, about people, projects and principles intertwining to create a holistic outcome. Great leaders rise above baser instincts, emotional responses and distractions. They draw upon all of the skills, tools and counsel at their disposal. This is how great decisions are made. While presidents have cabinets, CEOs have boards, and Macbeth consulted witches. Modern leaders are increasingly incorporating data and analytics to guide decisions. With the help of data science, they are able to better discern signals from a vast ocean of noise. However, to lead effectively in the age of AI, managers must be able to understand and trust the source of that signal, lest they be fooled by a digital fatamorgana. Magellan sailed around the world using data from sextants and compasses, but it was the human drive for discovery that propelled that voyage forward. Conversely, Macbeth made tragic choices based on his strange intelligence.
[00:03:47] Host 2: Glenn Hopper: Whether the source be witches or robots, leaders must be able to discern the nature and validity of the data they are provided. There is a tendency to believe AI driven algorithms are more rational than humans, but math is inherently no more rational than a toaster. If leaders are to use algorithms, they must understand the data upon which their decisions are based and the logic on which they are built. Just as they would with data from any source, including their human advisors. For if they be Macbeth's instruments of darkness that tell us truths, we must know this strange intelligence to which we owe. Leaders don't need a PhD in mathematics to use AI any more than they require a doctorate in psychology to work with their human counterparts. Yet a general understanding of each goes a long way to maximizing both. From backpropagation to rumination, effective leaders must draw upon the totality of the tools available to them, yet cede authority to none. Like any great technological advancement, AI brings great promise but also great responsibility. Algorithms will continue to improve and become increasingly valuable. But just as we would not completely surrender our shopping decisions to a website's product recommendation engine, we should not abdicate our authority nor relinquish our control to them, no matter how specific, valuable, or alluring the data. Great leadership comes down to the human factor. Leaders must digest and assess AI inputs, but then chart their journeys using their insight, instinct, and humanity.
[00:05:28] Host 1: Paul Barnhurst: In this week's episode of You Know Your Job Is Safe For now, I'm not going to talk about the silly or ridiculous things that AI has said. How hallucinate, how many of the answers can be less than ideal and make us feel very safe in our roles? What I want to talk about is what we can do to make sure we're safe in our roles. Because yes, AI makes mistakes. Yes, AI is not there. We haven't hit AGI, artificial general intelligence. It's ChatGPT laid out. There are five different levels and right now we're on level one, the conversational AI. We have a long ways to go to get there. In the meantime, what can we do as finance professionals to be prepared? First and foremost, find out how you can implement technology and AI into your workflows, whether that be using it to help write letters. An example I know of somebody early on that used AI to help write a collection letter, and they collected over 100,000 from a customer. I know Christian Martinez has shown many ways you can use AI and Python to enhance your analysis. Seeing people use AI with VBA. I've used it in my work. Oh, I need a quick script to do this or do that. I ask AI to write that VBA have it help you write that formula. Analyze data. Another great one from Jim O'Neill in a prior episode is he talked about how it can be the contrarian. Ask it for all the risks and all the reasons you shouldn't do something. All the counter reasons. So the more specific you can get with AI, the better the answers you can get from it.
[00:07:12] Host 1: Paul Barnhurst: But the reality is you need to learn how to start using it so you can be more effective. Many people do not impact that. Ai will take away a lot of jobs in finance, but one thing it could definitely do is companies will start looking for a productivity tax, almost an efficiency like, hey, we're not going to give you this many employees because we expect you to be 10 or 15% more efficient. You may delay hiring. You may have to justify every headcount and say, why can't I do it? The better you are at using it, I. The more efficient you are, the easier it is to maintain your role because you're being very productive. So this week the focus is on being better with AI not talking about the shortcomings of AI. Welcome to another episode of Future Finance. I am your co-host, Paul Barnhurst, and this week we have with us Glenn Hopper and Christian Martinez. I'm going to give a little bit of an introduction for Christian, and then we'll welcome to the show here. Christian comes to us from the Netherlands. He currently works as a finance transformation senior manager at Kraft Heinz. He has worked on several different continents during his career, including Europe, Australia and South America. He's well traveled and he's quite the expert. He loves teaching finance and FAA people how to use AI and data analytics. He has several courses he has developed on those subjects helping people be better with AI. He has his own newsletter that he recently launched called AI for FP&A and finance. Christian, welcome to the show.
[00:08:53] Guest: Christian Martinez: Thanks, Paul. Very glad to be here.
[00:08:55] Host 1: Paul Barnhurst: Yeah, excited to have you. I know we had the opportunity to chat. Previously on FP&A today, several months back, but we're really looking forward to having a little bit of a different conversation today. So again, very excited for this. Let's jump into our first question we have for you. You recently started your newsletter, as I mentioned in the intro there to help FP&A people learn AI. What made you decide to start a newsletter and what's you know, what's going to be in those newsletters? Talk a little bit about that.
[00:09:26] Guest: Christian Martinez: Definitely. So I think that artificial intelligence can be very utilized and leveraged in FP&A. And I started to have a lot of different articles like all over the place, my medium in different blogs and so on. So I decided to put everything in one place, , in the newsletter and try to communicate on the things that are most passionate about that. It's artificial intelligence in finance and infinite in a constant basis.
[00:09:54] Host 1: Paul Barnhurst: Got it. And so you kind of put everything together so you could see it. And what's really the the focus? I know it's AI, but is there a certain technical skills. Is it prompting a little bit of everything? What are you hoping to people kind of take away from your newsletter? What will they get if they read it?
[00:10:10] Guest: Christian Martinez: Yeah, definitely. So the main focus is that after they read every, let's say, like every newsletter, they will have something very practical that they can fully utilize. For example, last couple of weeks ago, I put this profit algorithm that is an algorithm developed by Facebook. And I put the full code, the full dataset, and a couple of like videos and prompts so that they if they want, they can utilize their sales data in that algorithm in like ten minutes. So that's the the main aim and the main focus.
[00:10:42] Host 1: Paul Barnhurst: That's amazing to think. I mean, think 2 or 3 years ago, trying to get somebody to use an algorithm like that in ten minutes on their data. I'm going to have to go check that one out, because I've had it on my list to play with profit with some data. And I, you know, I know a little bit about it. And I've talked to people I know someone that helped develop that algorithm, worked a little bit, worked in finance at the time. It was developed at, , Facebook. And so that that's really cool. That's that's just amazing.
[00:11:10] Host 2: Glenn Hopper: Yeah.
[00:11:10] Host 1: Paul Barnhurst: And I love Glenn's face. I can tell he's geeking out over this one.
[00:11:14] Host 2: Glenn Hopper: I am, and I you know, I always love talking to Christian because Christian, I feel like you and I, you know, early on in this, , your coding chops are way better than mine. But I've always been a a kind of a half assed coder out there, you know, just doing the citizen development and stuff. But I feel like, you know, we've been connected a while, and you and I in the, you know, pre generative AI, we were out there espousing how great, you know, machine learning is to be used in finance and the power of big data and all these correlations you could find and building models. And then as generative AI was coming along, I feel like you and I paddled out on our surfboards, you know, out way ahead of everyone else, looking around for all the great whites and everything. And then, you know, the generative AI wave started coming, and you and I turned and started paddling along the wave. And as as we go along our path, I feel like every now and then I'm looking over and saying, oh, there's Christian right there, doing the very similar things to what I am. And you're usually surfing just a bit ahead of me, but I really appreciate the work you're doing. And as someone, one of the questions that I get a lot that I thought would be great to pose to you, you know, because we've talked about, let's call it traditional machine learning, , for a while, or traditional AI, you know, using machine learning algorithms to kind of supercharge our forecasts. But now generative AI has come along. And I remember the early days. , probably, you know, ChatGPT 3.5, where, you know, significantly better than three. , but still, you know, far behind where we are today. And I remember some of the first. Stuff I tried to do. Paul, you and I talked about it, , when I tried to build it, I tried to get back.
[00:12:53] Host 1: Paul Barnhurst: When I hosted your show.
[00:12:55] Host 2: Glenn Hopper: That's right. Yeah. It's a it's a strange family world here where we have a lot of crossover. Yeah, but I, you know, I tried to get, , 3.5 to write some Python code for me, and it was pretty good, but it wasn't good at QC in the code back then. And it was, you know, you had to kind of know Python a little bit to be able to execute the code. But it's gotten so much better so fast and outside of the coding. Or maybe it is just the coding, but as you work in and teach how to use generative AI with finance, I mean, what is surprised you the most about the capabilities of generative AI and how and how it's being used or could be applied in FP&A?
[00:13:36] Guest: Christian Martinez: Yeah, very good question. So I think the main one to enforce is the code, , because as you were mentioning before, like finance professionals needed to have like six months, one year of training to be able to know everything about Python, even downloaded Python in their computers and so on, and then start using it. And now with JNI, like, I have seen a lot of different people in our courses that in like ten, 15 minutes, 30 minutes, they are already coding their machine learning algorithms, but using like generative AI, which is like fantastic. I feel it's like really democratizing the access of all of these bigger technologies for every person now.
[00:14:19] Host 2: Glenn Hopper: Are you hearing feedback from people, like after they take one of your courses and after you talk to them, are you getting feedback? Are you getting good stories of how they're actually using what they learned from you in production?
[00:14:31] Guest: Christian Martinez: Yeah, because one of the very good things that we do in our course, like both Nicholas and I, is that at the end of the course, we also tell our people on how to continue like the next steps. Right? So either they can, , message us or email us and so on. And then that's where I get a little bit of how they are using it. Or also because has this great called the AI Finance Club. And a lot of people from the courses, they join the club and then I'm interacting with them afterwards, like on a constant basis and really knowing how they have been exploring and utilizing everything that we teach. And because one of the other things is that, as you were mentioning then, like this environment, it changes very, very quickly. Right? So what we were teaching on the first cohort, it's very different to what we can teach now. And obviously we cannot go with everyone and say like, okay, now all of this happens. So this is like the new thing and so on. But with these communities, we can really share the knowledge of everything that is like constantly changing.
[00:15:35] Host 1: Paul Barnhurst: Love. That's very exciting. I want to step back. Right. We talked a little bit about Python machine learning. Obviously, it feels like any conversation around technology today has to include generative AI, But a lot of the work you do is in the data analytics field, not just finance, but data analytics. So what are some of the other technologies that have you excited that we should be talking about? Obviously, AI is the big one we all hear about, but what else is out there that you're like, you know, this is really exciting and I wish more people knew about it.
[00:16:08] Guest: Christian Martinez: Yeah. So I have to say that I think that these days most people in the family know about it. But one of the things that I really think is very useful, it's a good dashboard that can tell a financial story. So either if people decide to use Tableau or Power BI, this dashboarding tools, I feel that they can improve the way we do FP&A work a lot. And that's because the typical API for like type of work, it's either month end closing, and then you're preparing a couple of reports, sending to them and then analyzing the data and then exploring like the wise that data is that and so on, the variance analysis and so on. And with a good dashboard, you can do all of that very quickly so that then you can focus more of your time on business, partnering and talking to the business ongoing really either if you are let's say manufacturing finance, you go to a factory, you are in sales finance, go to the stores, and so on and so on. So it's I think one of the things that, again, many people know these days, but I'm still very passionate about love it.
[00:17:13] Host 1: Paul Barnhurst: I'm curious, do you have a favorite analytics tool. Do you like do you prefer power BI, Tableau, looker, Domo. Do you have one that if you could pick that you like to use?
[00:17:23] Guest: Christian Martinez: So I have to say that both power BI and Tableau, those are my favorite ones. I do think that they can do very, very good job. Both of the two. They have pros and cons in of course in both environments. , but yeah, those are my favorite ones for my intelligence, like business intelligence.
[00:17:43] Host 1: Paul Barnhurst: And those are the two biggest out there. Right. And so that doesn't surprise me. And you would expect them to do a lot of similar things. Some of it comes down to preference.
[00:17:51] Guest: Christian Martinez: Yeah. You know.
[00:17:52] Host 2: Glenn Hopper: I, , years ago when I really started getting into analytics, I had to I felt like I had a choice. I guess I could have done both, but I, I picked Tableau over power BI because I felt like it. The entry cost was lower. It was easier to do stuff in Tableau. But as the years have gone by, and especially now as Microsoft has really ramping up its AI presence with its partnership with OpenAI and everything they're doing with Copilot and some of the built in functionality of power BI, I wish I'd spent more time when I was actually more hands on learning power BI, but who would have seen how far Microsoft would have come since then? But, , you know, this is a question for both of you, and and it's going to lead into my my next prepared question here. Maybe not for management, you know, knowing how to get into the guts of things. But as far as front line FP&A people right now, what percentage of FP&A professionals do you think have either Tableau or Power BI or whatever it is, have that sort of dashboarding and, and storytelling and, , experience beyond Excel and able to create the is that most front line people have that skill set. Now.
[00:19:06] Guest: Christian Martinez: I would guess that if you are talking more about like the finance analyst, like the people actually doing analysis and so on, I would say right now, either from 50 to 60%, , they would have that skill or on their list. They're trying to get that skill. Does that make sense? , I don't know if you have a similar view. Yeah, I.
[00:19:25] Host 1: Paul Barnhurst: Was going to go a little lower, but I wasn't that different. I the number that just popped in my head is probably 30, 40% have it. If you're saying who's trying to get it, there's probably another 1015 that are starting to develop it, which gets to a similar place that Christian's at. I, I'm I guess I'm a little more pessimistic, maybe, but it's it's somewhere in that 30 to little over 50 plus. I think some of it also depends on company size. Right. Big, huge companies have an entire team that's managing by. So a lot of times the FP&A person never has to get into it. So if you only worked for big companies, you may have only pulled reports and learned, you know, basically nothing. Where if you work for a midsize company, you may have the BI team reporting to you. You may have chose the BI team, you know, a small to midsize company and being involved in the entire process. So I would think it's much higher in small to midsize companies than it is enterprise people that have spent their career in enterprise. So I think there's a little bit of industry segmentation.
[00:20:26] Host 1: Paul Barnhurst: Would you agree? I mean, you work at a big company at Kraft. Would you say the numbers are really high? They're on strong buy skills, or you say mostly FP&A people, probably a little lower because they have people who can do a lot of it for them. Ever feel like your go to market teams and finance speak different languages? This misalignment is a breeding ground for failure, impairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Mcfloat. I the strategic finance platform purpose built to solve the toughest part of planning and analysis. Be to be revenue quo, quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stats, make segmented scenario planning a breeze, and closes the planning loop, create air tight alignment, improve decision latency, and ensure accountability across the teams.
[00:21:46] Guest: Christian Martinez: So one of the things that are really about. Worker friends is let's say that learning and development, , ecosystem and environment. So back when I started around six years ago, there was this huge wave of everyone learning data literacy. A little bit of like, , like Tableau, like dashboarding things, a little bit of automation and so on. So I think we have like way more people than the average, let's say big company people that know how to use like, dashboarding tools.
[00:22:20] Host 1: Paul Barnhurst: That's great. I mean, that's fabulous to have at a big company. And I think that's awesome that you have that kind of investment.
[00:22:26] Host 2: Glenn Hopper: You know, to me. And it's. To my mind in a leadership position that you understand, you know enough data science to be dangerous enough about kind of what goes on under the hood with power BI or Tableau or any of the other tools out there, and then a little bit more about what the BI people are doing. Because if you're trying to organize the the chessboard and know how to have your teams work together, like understanding how all the components fit together, it helps you sort of allocate resources and know what you can get and what you can't. And I think I've been my whole career has been with SMB companies. And to my mind, and maybe because I didn't have the luxury of resources, to my mind, it's always been very important for the the FP&A folks to have that BI skill set. And, you know, if you think about Tableau and Power BI, there's sort of a drag and drop component to it. So you may not, you know, you can just learn the drag and drop without really understanding, without really understanding what's going on under the hood.
[00:23:45] Host 1: Paul Barnhurst: You don't have to learn a data model to build a basic report as an example, right? You can drag and drop the report and have some of the Dax built for you. If it's basic enough without having to know anything about the actual underlying star schema model, that's going.
[00:23:59] Host 2: Glenn Hopper: Yeah, exactly. And I but to my mind, and I get a lot of pushback on this. So maybe I'm wrong, but it's not going to stop me from keeping to answer this question. So Christian, I this is a really important question for you because I know how deep your skills are here. So I've long believed that FP&A professionals, they need to expand their skill set to include some basic programing. And I think to your earlier point, that's easier now because you can, you know, if you don't know anything about Python or anything about coding, it's going to be hard for you to even get a generative AI to write code for you that you can run in any kind of environment. So and maybe, you know, I would say at a bare minimum, I think we should be able to write SQL queries, but that's probably that, that may be it's table stakes. Now, I could even make the argument for learning Python as well, because you can. You get so much more flexibility if you actually have a coding language and you're not sort of restricted by the drag and drop and by one environment, and you can really dig deeper into the data when you can fine tune the programing and whether you're setting parameters or whatever it is that you're doing, you know, you have that flexibility to really customize what you're doing. So Christian, with your skill set and knowing what it took for you to kind of develop that. I mean, what are your thoughts on this? Should all finance folks be able to code?
[00:25:14] Guest: Christian Martinez: So I definitely believe that all finance professionals need to have that analytic skills. I do think that the majority of people would be very like, will have a lot of benefits if they know how to code either SQL or either Python. And I also believe that many finance professionals would have a lot of value if they know, let's say, how to build a dashboard like from scratch. But I think in general it's more like data analytic skills because it really depends as well on your level of your analyst, if you are a manager, if you are the CFO, and so on. But having those right type of data analytics skills for for your position, That is key. So maybe not everyone needs to to learn how to code. I do let's say suggest many people to learn how to code. But again it really depends on their level as well the organization. But that analytic skills that one for sure. Yes.
[00:26:09] Host 2: Glenn Hopper: Do you think that, , the job the marketplace, , for hiring, if you have those skills, I mean, it would have to put you at a premium, right? Have you seen any indication of that?
[00:26:19] Guest: Christian Martinez: Yeah. So I have seen it before like for maybe like six years now and so on, that people with those skills on top of other very important skills in finance then as you mentioned, like they are in a on a premium.
[00:26:34] Host 1: Paul Barnhurst: And I'll share a little bit of my thoughts here because I think I'm similar to both of you, maybe a little different. I've always said FP&A not maybe all finance should at least learn SQL. Being able to pull the data. I did report writing for two years. I did a master of Science and Information Management. It worked on a software thing, so I learned a lot on the data side. Never learned to code. Yes, I can do some code. Sure. Can I do a little bit of VBA? I've always wanted to learn Python but never done it and I'm still going. One of these days I'm going to get there even though I don't use it and what I do for business today, So I think at a minimum, you need to be really data literate. As we were talking about. I think books like this are great. Jordan Goldmeier becoming a data head, where the whole book is to help you be able to speak the data language without being a technical person at a minimum, as a finance professional, you have to understand that data language and be able to have those conversations. Do you have to know how to do it all? No. But will it benefit you? I think without a doubt. I think we're all on the same page, that it's when you have an employee that knows how to do that stuff. Can you trust them with more things? For sure. And is I making that easier? Of course it is. But I would argue and I'd love your thoughts on this Christian. I'd say anyone who knows some basics, knows a little bit of the coding, knows SQL, knows Excel, really, whatever it may be. The more technical skills they have, the more they can get out of I and someone who doesn't know anything. Would you agree, Christian?
[00:28:00] Guest: Christian Martinez: Yep, totally. And I have one, , very good example as well. So it's not with AI per se. It's actually with dashboards. But back on the day when I was, working in Australia, I had a manager that I was telling him like, oh, look, there's this thing called Tableau. , we can build dashboards, we can do this, we can do that. And then he starting slowly to to trust me and tell me, like, okay. Yeah, try it on. Okay. Bring me the things and then see if we can, add value to your company and so on. I think, like, maybe like six months to one year passed. And then at one stage he had to take one of these courses of Tableau Baltimore for managers. So as you mentioned, not to build a dashboard, but to learn everything behind and so on. And I think it was already quite late, but he called me and I was still available, and he was telling me, like, all right, Christian, now I really understand what I can ask you to do. And my mind go like super, super quickly. It's like, now we can really build up very, very interesting things. And I think a matter of like two weeks after that, we build up so many more things than the previous six months, , where he was still not trusting me 100% on. Okay, this is a very good technology. So I think this is exactly the same thing now with AI. So like if people understand this part of like more like data literacy, that analytics how data behaves, they will be so much better and really getting the most out of any type of AI.
[00:29:38] Host 1: Paul Barnhurst: All right. So we've talked quite a bit about Python. I'm still going to go ahead and ask a little bit. You just released a course Python for finance professionals. What was the thinking behind that course. We've talked about how AI can do some. They can learn more. But why Python? Why not R or SQL? Or what was the reasoning for choosing Python as your kind of next finance course for people?
[00:30:02] Guest: Christian Martinez: Yeah. So actually the full name of that course is Python in Excel for finance.
[00:30:09] Host 1: Paul Barnhurst: Oh it is in Excel okay.
[00:30:11] Guest: Christian Martinez: And that's the main reason because Microsoft released this , last year that they were going to be integrating Python completely natively in Excel. So Microsoft is also owning LinkedIn. And then LinkedIn reached out to me to basically create this course of combining like let's say final skills, but also Python and then also Excel. So that's the first course that I have already released in the platform, and I'm already working on a second one, which is a little bit more deeper and advanced. And actually it's about using more machine learning still in Python in Excel.
[00:30:50] Host 1: Paul Barnhurst: I'm going to have to take those. I didn't realize it was linked in. I know George Maltz done some as well. He's another Python in Excel. I finally got it in my beta version of Excel and played with it the other day. It's fun.
[00:31:02] Guest: Christian Martinez: It's really good. It's really good. And then one of the I think most beneficial things is that it's in Excel. Right. So a lot of finance people are people. They love Excel. So then Python instead of like taking them out of Excel and going somewhere else, now they can leverage the power of this program language natively in the solution that they like to use.
[00:31:25] Host 1: Paul Barnhurst: Now I've played with a little bit. Have you had an opportunity yet, Glenn? Have you played with Python in Excel at all?
[00:31:29] Host 2: Glenn Hopper: Really haven't. , and I'm, I mean, I saw and it's been out long enough that I don't even have an excuse at this point, other than I just have to sort of pick my paths to go down, so. Oh, I get it. I'm excited to see that course, though. And, the, , did you go on site to do the recording?
[00:31:47] Guest: Christian Martinez: No. So this one, they sent me the the recording kit. If you get.
[00:31:50] Host 2: Glenn Hopper: An opportunity to go out there, it's beautiful part of the country right there. , just, you know, just south of San Jose. I guess that. Is that.
[00:31:58] Host 1: Paul Barnhurst: Santa Clara? Yeah. I've heard it's a really good campus to go to.
[00:32:02] Guest: Christian Martinez: , I'll definitely go. It's a bit farther from me. Obviously. From the Netherlands.
[00:32:05] Host 2: Glenn Hopper: Yeah, yeah.
[00:32:07] Guest: Christian Martinez: Yeah, I'll be keen to to go. Yeah.
[00:32:12] Host 2: Glenn Hopper: We've talked, we've really geeked out and gone way deeper in Python probably than we have in, , in previous courses or in previous interviews rather. But I want to get back to something that a lot of people can relate to. And this is my other hot take. So, Christian, I'm really curious to hear your response on this. So like, you like like all of us, I'm out talking about AI all the time, and one of the most common questions I get is, well, could you just give me a prompt library? Could you just give me some prompts that I can use to interact with generative AI? And that always drives me crazy because to me, people, it means people are not thinking of generative AI the right way. It's not like a software where you push a button and, you know, pull a lever and get a result. It's rather than prompt engineering. And I do think I've said it. I probably did one of my monologues on this. I think in a few years, prompt engineering, if it's not already is, is going to be like saying I'm really good at googling stuff. I mean, it's just going to be it's not going to matter. But what does matter is understanding what these tools can do and how to interact with them, and how to be specific in your questions and all that. So I'm sure, Christian, you get it all the all the time. But what are your thoughts on prompt engineering? And if somebody is asking you for guidance or for you to make some canned prompts for them to use, I mean, what are your thoughts on it and how do you sort of handle it from your perspective?
[00:33:38] Guest: Christian Martinez: Yeah, no, definitely. Very good question. So I have two points on that. Well, first one is that one of the I think the second slide that we show in our course of advance LGBT with Nicholas, it's a picture of someone fishing and we do the whole metaphor of, , teaching you how to fish instead of, like, giving you fish. Right? And I think that's one of the things that I most like about the course like that, let's say structure that we don't give people exactly just this prompt and then that will work. And that's it, that we have 100 prompts, , but instead we do give them a bit of guidance at first, like, okay, need to be the structure of the problem. You need to add this. If you don't add this, then this happens. If you add this, this happens, and so on. But essentially we try to teach them how to think differently about handling these tools. And that's I think the most important because even though we put a lot of different, , finance use cases like cohort analysis, like heatmaps, PBM analysis, different automations, and so on.
[00:34:48] Guest: Christian Martinez: We teach all of that, but on top we really teach them on how with their own data, after the course, they can really, utilize the framework and then build the prompt by themselves. That's great. Yeah. The first thing, the second thing is that even though we do that in the course, which is obviously the more comprehensive one that we have, and we spend a lot of time with the people in there, we also have some documentation on like prompt libraries. And I started not like a prompt library itself, but more like interactivity. You can share the link of an entire conversation. So I started building these full conversations of like ten different questions back and forth and so on, but for specific use cases on a, on controllership, on tax and so on, so that people that don't want that further to, you know, take a full course or go very deep into a rabbit hole or something like that, they can go in there and more or less see what is available and what is possible.
[00:35:50] Host 2: Glenn Hopper: That makes sense. And it's almost I feel like, you know, you're almost structuring that like a chain of thought prompting that. It's like, look, you can't ask. You can't eat the whole elephant in one bite. Let me just show you these are the sequential prompts that you do. And maybe after somebody does that a couple of times, it clicks with them and they can, you know, take off the training wheels and go off and do it on their own.
[00:36:12] Host 1: Paul Barnhurst: I think it's very similar to learning Excel or other things. And what I mean by that is like later today I'm doing a Excel webinar where I'm talking about lookup methods in Excel. How many people just learn Vlookup and never go any further? But we all know if you understand the why and the different methods, you can get a lot more analysis out of your data. You can often be quicker, do it in better ways, and I think it's the same with AI. If all you do is say give me the prompts, you might get to the third grade level, but if you start to understand the why behind it and experiment, and also in your sixth, your eighth, your college, whatever, you're becoming advanced because you're getting the why behind it. Not just, you know, oh, I do it this way because, well, that's what I learned and it always makes sense. All right. Well, we should move on into our last section here. This is where we get to know a little bit about you. So I'll lay out the ground rules and how this will work. We have a list of questions. The list we believe this week came from haiku Claude. We asked it 25 questions to come up with, to ask a podcast guest to get to know them. So we have two ways for my question. Then Glenn will walk you through how he's going to do it. I have two ways you can pick a number between 1 and 25, or the random number generator can pick a number between 1 and 25. And that's the question we'll ask you. Do you have a preference? Yep.
[00:37:36] Guest: Christian Martinez: Maybe that's what the random number generator.
[00:37:39] Host 1: Paul Barnhurst: All right. Here we go. Let me generate 23. Let's see what 23 has to say. All right. This is a pretty good one. What's your hidden talent or something unique about you that we don't know?
[00:37:57] Guest: Christian Martinez: That's a very good one for sure. Trying to think more on things that I think many people don't know. It's not a hidden talent, but I would go with that part. So I was actually doing a presentation on it earlier today. But one of the things, about me is that right now I live with my girlfriend and my two pets. So one cat called Simba and one dog called Nala. And then I have always loved dogs, but until I met my girlfriend around like eight years ago, I had never even touched a cat. And then since then I started like slowly, , interacting more with cats. Until last year, we decided to to get one. And then now the family is bigger because we just got a dog. So again, not for sure. But things that no that's unique.
[00:38:51] Host 1: Paul Barnhurst: I like it that that qualifies. We'll give you that one.
[00:38:54] Host 2: Glenn Hopper: So with these questions, my idea I'm waiting for the full, , multimodal, , ChatGPT for the voice and everything. And we're just going to have ChatGPT or maybe advanced Siri after the Apple partnership. We're just going to have the bots ask you the questions, and that way we can't be held accountable for asking stupid questions at this point. But until then, we've been bouncing around. Gemini is the worst. I'm just I'm dog cussing. I'm not a Google stock owner, but I've been dog cussing Gemini for a while. We'll see. You know, maybe they'll they'll catch back up, but, for a while. Chatgpt 40I thought was doing the best job at at coming up with the questions. And now we're using Claude Haiku here, but the, , the new, , you know, the 3.5 from anthropic is really, really good. And then I'm getting my model names mixed up. But the version three is that sonnet, I guess, does an incredible job writing. But anyway, long story short, we're we're still, , doing a bake off between the, , the different large language models. And we've got haiku this week and instead of doing the random number generator, what I do, I don't know what it's going to take in context out of this. And I've not seen any rhyme or reason, but we'll load, we'll have it come up with the questions, and then I'll load your bio and the questions that we asked you, and then I'll have it come up with the random questions here. So the, , what I'm going to come up with or what haiku is going to come up with for you is what is and this one's kind of boring. I just talked it up. It's fine. It's it's fine. We're going to learn something new. What is your favorite way to start the day? Perfect. Whether it's a work day, weekend day, you get up. How are you? How are you greeting the day? If you can do everything right.
[00:40:46] Guest: Christian Martinez: I would say I'm very I'm very structured person. So if you ask me that question, I think one month ago, I'll say just like wake up and then start my coffee, like my espresso machine, and from then on my my laptop and start looking at different things. I have a few different websites, like I look every every morning and so on. But nowadays, since my dog is like, I just got her, so she's a puppy, I start my day with her. So basically doing all of this for minutes. Then I do the coffee and then, , I start my computer.
[00:41:27] Host 2: Glenn Hopper: I think puppies are a good way that calms you down. Releases the the chemicals and everything. It's probably a great way to start the day and, , put you in a calm space. If the puppies if the puppies behaved otherwise, if it's chewed up something overnight, you have a whole other list of problems.
[00:41:43] Host 1: Paul Barnhurst: And speaking of puppies, that's probably a good spot to end on, right? A nice calm everybody can think about their pet that they love and how it can help calm them down. So Christian, we really appreciate you joining us. We enjoyed chatting. You know we got to get deep a little bit in Python. Just all the amazing things you're doing. And I just want to close by saying thank you for all you're doing for the finance community. I think the work you do is great. We need people out there like you that can help lead the way and help us realize that there's a lot more we can do with technology if we're just willing to invest a little bit of time for. So thanks for coming on the show, Christian.
[00:42:17] Host 2: Glenn Hopper: Yeah. Thanks, Christian.
[00:42:19] Guest: Christian Martinez: Thank you so much for having me.
[00:42:21] Host 1: Paul Barnhurst: Thanks for listening to the Future Finance Show and thanks to our sponsor, QFlow.AI. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.