[FP&A와 AI: 기대와 현실에 대한 전문가 패널 토론]-[AI and FP&A: Hype vs Reality]

This special episode is taken from the online event FPNA con by DataReels.

I hosted an absolutely fascinating panel of experts called AI andFPNA, hype versusreality.

We brought together AI and FPNAexperts, NicholasBoucher, ChristianMartinez, and GabriellaGutierrez.

It was an incredible discussion with so many great questions from theaudience, some interestingpolls, and practical experiences about applying AI andFPNA.

I loved hosting thispanel, and I know you're going to find a lot of value in thisbroadcast.

We talked about some practical applications ways you can use AI today inFPNA, and our projections for the future of AI andFinance, and some guidance on what you can do today to learn how to use these new AItools.

So let's divein.Today, we're going to dive into real-world applications of artificialintelligence, and try to separate Fact fromFiction.

Fact not all Fact fromFiction, just when it comes to the state of AItoday.

We'll look at the present and future of AI and financial planning andanalysis.

I'm GlennHopper, the host of FPNAtoday, and the head of AI Research and Development at an eventous advisorygroup.

I was a startup CFO for 15 years and came up through the FPNAranks, so I genuinely love all thisstuff.

I'm also an author and lecturer on AI and its applications and finance andaccounting.

And I'm proud today to host my esteemed panel of Finance and AIexperts.

We've got NicholasBoucher, ChristianMartinez, and GabriellaGutierrez.

Gabriella is a financeleader, a keynotespeaker, and an advocate for financial innovation with 10 years of industryexperience.

She focuses on strategic financialmanagement, drivinggrowth, and optimizing businessperformance.

She is currently transitioning from local CFO at Teeds to founding her own startup calledTabs, which aims to solve some of the biggest problems in finance today usingAI.

Christian is the global finance analytics manager for KraftHeinz, not theCEO, despite our bestefforts.

And he's also an AI for financeinstructor.

He has over eight years of experience in multinational companies across internationalmarkets, includingAustralia,Mexico, and theNetherlands.

He teaches Microsoft co-pilot Python and Advanced Chat GPT forfinance.

Christian was named among the 30 under 30 and accounting and finance industry in Australia in2021.

He also secured the International Rising Star and Finance Award in2023, and the EMEA Data Democratizer Award in2022.

Outside of finance andAI, he's a marathon finisher and a world traveler having explored over 70countries.

Nicholas is a recognized thought leader on finance topics like AI forfinance,FPNA, andcontrolling.

Nicholas is also the founder of the AI FinanceClub.

And internationalcompanies, includingMercedes-Benz,Chanel, HugoBoss, andKPMG, trust Nicholas to train theirteams.

He has more than 15 years of experience working in senior financeroles.

He speaks three languages and has lived in five differentcountries.

Okay, before we getstarted, I think we're going to jump into thepoll.

And while we're answering thepoll, the first poll questionis, have you used AI in your workpreviously?

In thisquestion, we added at the lastminute, but we thought it was a good idea to get kind of a level set of where weare.

Christian, you want to take a guess on what our percentage of people who've used AI and their work previously is going tobe?

Yeah,definitely. I'll say30% ofyou.

That's what I've been seeing in thepast.

Yeah, have you seenit? I've seenit.

And this is Nicholas and Gabriella for youtoo.

I have seen this number come up as time goeson.

Every month it seems like there's a slightly highernumber.

Nicholas andGabriella, you want to take a guess where we're going to be on who's usedit?

Maybe40%. Let's say40,50.

If people will say50, I would be verysurprised.

But they we use also AI for many different use cases that we don't evennotice.

Soyeah, if we wouldn't include them then50.

I think in myexperience, I do a little game when I train people onAI, especially inperson.

And I put people on thewall.

So like imagine a line of 20 people and ask them to step one foot ahead each time they will sayyes.

And the first question I askis, did you ever use JGPT orCopilot?

And then you have already only80% of people whostep.

So you have already20,30% who didn't move onestep.

So they never useit. And that's interesting after to askwhy.

Then if you ask when you will use it for work yes orno, then you will have like half of the people willstep.

But then after when youask, are you using it every day or everyweek?

Then you'll have 10 and20% only of people infront.

And that's interesting to see after like physically the map of the people and to ask them why they are using it a lot or why they are not usingit.

Allright. Let's say let's see the results here where we cameout.

Wow47%yes. So this this number is coming up everymonth.

It's and also maybe people are self-selecting by joining this webinartoo.

If you're joining the AI and FPNA you might be you know kind of self identifying there out of thegates.

Youknow, so we had the poll to see where where everybodyis.

How many people have used it and are using it in their dailyworkflows.

But because you guys are out there at theforefront, youknow, working withteams.

How are you guys seeing finance teams using AI and financetoday.

And then I guess as a follow up tothat, how do you see them using it in thefuture.

And I guess Nicholas let's let's start with you on thisone.

So the thing is you have like Gabriel says a lot of people were using AI before the AI washype.

So it was like banking was like big companies with a lot of frauddetection.

But now that we all have since 1.5 year chat GPT and copilot and Gemini actually it's open toeverybody.

And the first thing what people do is they writeemails.

But after they have like played around and wroteemails, they get frustrated because they don't know how to use it and they use it likeGoogle.

Like that's what I call like the Googlemethod.

And if you use it likeGoogle, then you will only get something supergeneric.

And you need to really change your I will say your framework because you have not Google in front ofyou, but you have like a mini assistant or intelligent trainee which has read all of the Wikipedia articles and which can process much more data than what you canthink.

And you need to ask concretequestions.

You need to bespecific.

And an example I always do is when you want to write a Dunningletters, then if you just say Dunningletters, youget,oh, here are the 10 ways to recover yourmoney.

But if youask, can you draft for me a Dunningletter?

You get already a gooddraft.

But then after sheexplained, Mike Ryan neverpaid.

So how can I really have something to move theneedle?

Then he willwrite, like a lawyer who is exactly the legalactions.

And like in really oneminute, if you tweak yourprompt, you get from having something supergeneric.

And that's where90% of the people stop to having something super concrete that even a CFO will have a hard time to write by himself or bythemselves.

How aboutyou,Gabriella?

I would totally agree with Nicholas and that I have seen most of the use cases of people using any language model to send emails to reply tothings.

But I have also seen for some companies that they are using language models to writepolicies.

And even policies about usingAI.

So it's a bitfunny. Like youwrite, you ask the languagemodel, please write me a policy of our employees of how to useAI.

But forthem, it was a really nice use case because theyeven, or the model came up with more edge cases that they wouldn't have thoughtbefore.

And then they were able to implementit.

I think also what is important is to always have the human in theloop.

So someone like reading about policy and making sure that it fits to thecompany.

And aswell, like the feasibility to really prompt again and to ask forchanges, it's quiteamazing.

Imean, also another usecase, I would say like predictive analytics and so on that you could just put your data and ask forquestions.

Obviously that comes a bit into the privacytopic, but I think we can discuss that in laterdetails.

Yeah,yeah. Andactually,Christian, this moves over toyou.

Let's, I want to introduce something that maybe a lot of people haven't seen and you might be a good one to introducethis.

So thinking about the ways that a lot ofdepartments, youknow, marketing copy is onething.

But when you're actually like Gabriella referenced using using it for dataanalysis, that's something completelydifferent.

So I know you've done alot,including, youknow, writing Python and using the data analysistool.

Can you talk specifically to use of that tool forFPNA?

Yeah,definitely. I would say that one of the more exciting use cases of participation tell you from me inFPNA, it's actually these like machine learning and predictive analyticsmodels.

So as you're like 10 yearsago, when I started selling a kind of masters in the assignedfinance, that's where I was like used to learnlike,okay, how to useprograms, and we're just like Python to start doingforecasting, let'ssay, ofsales.

And then you need to really study a lot of time in order to make your first and forecast using machinelearning.

Butnowadays, if a team is leveraging in a correctway, like theseelements, they can basically generate Python code and then implement these machine learning models in order to do forecasting of like sales revenue or even identify financial performancedrivers.

So I think that's one of the barriers that some FPNA teams are breaking rightnow.

And forme, it'svery, veryexciting.

So I thinkalso, youknow, thinking about the data analysis tool and then just the sort of inherent abilities of anLLM.

So a lot of peoplehave, Imean, there's current perceptions and then some misconceptions on AI andFPNA.

And one that I always lead with is LLMs aren't inherently good atmath.

That's not what they're designed for it to domath.

Now,obviously, tools like the data analysistool, it's writing Python under the hood and it is doing math thatway.

But so tome, the biggest issue I have withpeople, or that I want people to understand when I'm firstintroducing, thathey, you can use these LLMs in finance and accountingis, but don't get thestraight, large language model to build you an amortization table because it's not going to actually do the math unless it's actually writingthem, youknow, simple things like calculatingdepreciation.

If you're just asking thelanguage, large language model forresults, that's not what I'mtrusting.

So that's the biggest thing I have is there are ways to dothis.

Like Christiansaid, youknow, writing code or using the data analysistool, but the LLMitself, I'm not going to trust when it's just in line giving me a textualanswer, butget,Gabriela, what do you think are some perceptions and misconceptions and things that people need to understand right now with using AI infinance?

I think like overall that the thinking that you could just put all your data and it will give you all the answers would be kind of a misconception that you could and also if the first answer is not the correct one because the way to how language models work and usually the number of tokens that you would have a token would be it could be a word or it could be a mix of a couple of words and they could bemisinterpretated.

So the straightforward like you are able to request theprompt, the better would be theresult, but also if you include a very largeprompt, it would be a bit more difficult for the model to even understand what is the task todo.

Also, I wouldn't or I would encourage people as well to try more prompting and I think like people that right now they are even specialized in prompting to really get like better results and to get the model to do even more kind of more customized or more preciseanswers.

I will say that would be one of the misconceptions like really giving all the data and having the best answer on the firsttry.

So that's nothappening.

Another misconception is that for example on the data privacy part or like they could train with my data and soon.

So they are many different like companies with many differentterms.

So I would also encourage people to kind of like you have the free version and as well you have the paidversion.

So within the different terms so on you can have like also kind of your owncluster, your owndata.

So like they wouldn't share it unless it is obviously written in their termconditions.

And again like on the third use case as well it's for example that on the predictive analytics or on more machine learning is that you could just pull all the data again and it could give you the perfectforecast.

I think like it needs to be a bit of a trial and error that you play with many different models and then you choose what are like the best that they feed overall for the use cases because it's not a one model fit all but rather it's like going into various specifics and really trying andplaying.

I always call it trial and error until you find theone.

Christian what do youthink?

Yeah I would say the two things are like one of the main misconceptions that I have been seeing people having is that LLAMs it's just the only artificial intelligence application that they can use inFPNA.

So there are like so many more applications of AI like as Robert Loves mentioned like machine learning models and techniques for example there are so much more powerful for finance andFPNA.

So it's not just like you will go to a chat GPT and then you ask questions about your financials and so on like it's more again like you can use chat GPT to generate Python code that create some financials either visualization forecasts and so on in anotherenvironment.

So it can be like Google call up it could be your notebooks become something else as a sure from Microsoft and then I think that's the more powerful use case of let's say like AI inFPNA.

Yeah it's funny because you know for years dorks like us have been you know using machine learning for classification and regression and fraud detection you know all the uses that they've been to it but the the barrier to entry on all that is well you have to know Python or you have to you know you have to be able to code and you have to have sort of the data scienceapproach.

So now with generative AI this is a lot of people's first experience with AI so it's there's an attentation to put all AI in the box of just what these generative modelsare.

So I mean the good news is it's actually you know it's kind of democratizing AI that it's making it accessible to more people but the danger in that is that if you don't understand how the model works or what to expect out of it that you know you're if you don't know how you're using it you've got a very powerful tool that could could give some some badresults.

Nicholas what are you what are you saying what do youthink?

So it wasn't interesting because a lot of people say AI it was like I think 20-30 years ago when computer arrived and people are saying or computer are going to do our work and when you think about that is not the computer who is going to do the work it's maybe a special program is going to do a special task that before was doing by the human and AI is the same you cannot say AI is going to replace AI is going to take all over our job is more like okay if a tool using some AI techniques or OCR and LP machine learning has access to a part of your data and can on top right so create then it can do a part of the work and if it doesn't have access to all of this then you and AI is going to do a part of your work faster and better and so learning about what means AI what Christian explained in Gabriela and US as well what you explain so learning what it can do and then learning what you have what is your current setup and see how you can connect and the more you will have a connection like today OpenI just on last week they open the connection with Microsoft and Google Drive but it's still you have to drop manually and to decide yourself but companies right now start to have integration within their own company where humans don't have to connect things they just say I want to see the sales of last months and then they get a report in front of them but you first need that somebody connect that and people need to figure that out that is not AI who is going to do all of this is a set of AI and a specific program using AI and that is like a big misconception that you need to train and educate people and once they understand that then they know what is possible today and they also have kind of an idea where they need to go and also when they are going to select tools what are the criteria to select those tools fpna today is brought to you by data rails the world's number one fpna solution data rails is the artificial intelligence powered financial planning and analysis platform built for Excel users that's right you can stay in Excel but instead of facing hell for every budget month and close or forecast you can enjoy a paradise of data consolidation advanced visualization reporting and AI capabilities plus game changing insights giving you instant answers and your story created in seconds find out why more than a thousand finance teams use data rails to uncover their company's real story don't replace Excel embrace Excel learn more at data rails dot com so i'm going to throw up the poll right now and uh for those who are listening only the poll question is what is the biggest barrier to AI adoption in your organization's fpna processes and our choices here are lack of understanding or awareness data privacy and security concerns inaccurate data budget constraints lack of skilled personnel or other i think that google's latest gaff from last week or the week before where uh you know it was tell it the their AI search and enhanced search whatever they're calling it now was telling people to eat one small rocket day to put cheese on or to put glue on pizza to keep cheese from sliding off and whatever that absurd things that the AI was kicking out and Nicholas to your point that that human in the loop is so important right now and i understand there's sort of this scientific idea or this science fiction rather idea of the AI that's going to replace us but as we're seeing these these kinds of issues that human in the loop stays so important but if you are you know if you type the wrong numbers into a calculator it's not going to be the right result or if you do if you do your order of operations wrong you know so this is a tool that we have to know how to use we as users have to understand how to use it and when to trust it and when not to so i think you know a lot of this lack of trust right now is well we're not ready to hand over the reins to AI to run our fpna department but they're if you're using fpna or if you're using a i and fpna it is giving you a leg up on people who aren't so it's you know it's not AI versus people it's people using AI versus people who don't so um okay i've rambled on long enough let's see the uh the poll response here so biggest one yep to the point there lack of understanding or awareness so people joining stuff like this that step one just to get kind of the broad overview but i think what i'd like to do um before the show's over is talk about uh you know where some people can learn more about AI and how to use it and i know uh everyone on this panel has a a lot of great resources for that um data privacy and security very big and and Gabriella hit on this a little bit with always tell people don't just don't go to the publicly available interface you know chat gpt 40 free for everyone now if it's if they've rolled it all the way out but when it's free for everyone it's kind of like google you know when you're not paying for something we're getting something from you and that's something from you as the data so all of your data is being used to train the models so there are there's chat gpt for enterprise there's closed uh environments for all these and that's uh if anyone is going to use proprietary information that's where to go with it so lack of understanding data privacy and security inaccurate data that's you know that's that trust era and we can uh talking a little bit about how we can trust more budget constraints i get um a little bit a lot of these now the pricing is is um is coming down uh another big one is lack of skilled personnel and that is you know we're people who are trying to implement AI today we're all kind of at that bleeding edge where we're out over our skis a little bit with this um so that it's up to the rest of us to to kind of reskill and upskill to understand how to use these tools so very interesting poll results there so let's talk about though for people who are successfully using AI and fbna today because i think everybody wants to know yeah it sounds great i get it i got it to write a limerick for me it was great at that it was funny um but how can i actually turn this into my professional workflow so i do want to talk a little bit about um what your you guys experience are of successfully implementing AI and fbna and i i know christian i think you've got a good a good story on this i'd love for you to lead us off if you can yeah definitely so what i have been saying on um let's say things that successfully implement AI is as someone was commenting on the chat that training incorporate environment so they really need to be trained on how to use AI properly and then there are like so many use cases for example the very i guess like simple one but powerful it's just for forecasting a lot of us in fbna we need to do forecast every month sometimes we're like pre months as well every year and so on you can improve your forecast so much more if you use artificial intelligence either to do like a let's say clustering method there is a machine learning technique in order to segment let's say of your different like business units your customers and so on and then implement other algorithm in order to actually predict the sales that you're going to have the revenue identifying the top financial drivers and so on those are i think the main use cases that people can have let's say in forecasting then the second one is just around saving time so a lot of us especially also in fbna we need to either to consolidate files to merge files some mappings like you need to be like the manually and so on you can use as well AI tools in order to do that for you so that part of like saving time is the second one so I've been trying you know like all you guys I've been trying to push kind of the limits of what generative AI can do and when 40 came out I was pretty blown away speaking of forecasting I was doing scenario analysis just straight in 40 so I had my you know three years of quarterly financial information and in the past I've gotten the data analyst tool to you know do a simple regression or whatever to just carry the forecast out but with 40 I was actually able to get it to do some more complex forecasting it in one shot did a serema forecast it decisinalized detrended did that full seasonal auto regressive integrated moving average I think I got that right serema forecast which was much more accurate than regression and then I got it to do you know three different scenarios and then I got it to run money Carlos simulations all direct in line so it wasn't like I was getting it to write the Python I was going in another application it was doing this right there so that was the 40 I think kind of quietly went under the radar for me but when I saw it doing that all in one shot accurately I was like okay now we're starting to see some more practical use cases for this Nicholas Gabriella do you have you seen uses that you've kind of been blown away with recently I think it's very similar to what I created a Christian mentioned three years ago actually or maybe four I was working for for a different employer and there we had tons of amount of data like our excel feeds and spreadsheets will collapse because of the amount of data that we had and obviously running that for us or even creating a budget for for different products it was quite a challenge and therefore like we even tried to look for solutions in the market but really we couldn't find the one that would fit for all of us and that's why when we change completely to running like our forecast into like using a call app so it's like a Jupyter notebook badge on Google Drive and then we we were able to automate all of our kind of data ingestion into directly to our call app node will run there our forecast and then we just did a small integration of the consolidated version in one spreadsheet so we would be able to see it okay month by month it would do then like variance analysis and so on so I would say that has been one of the most successful use cases as we we were able to achieve like an accuracy of 99 percent so we're at 12 month period so that was one of the biggest use cases sofar.

How about youNicholas?

I had the chance actually to talk with the OpenAI team and their finance team and what was interesting when I talked with them they explained me that they had to go from a business doing I think I think 20 or 200 million annual revenue in 2022 to two billion in 2023 so if you see the graph is live and their team they could not keep up with the pace of the growth so they had to grow with the members but there was too much work what they did is they hired robots and they didn't hire them they actually created those robots so two type of business cases for these robots one is a bit more what Christian and Gabriella explained so creating Python script that will do the work that normally humans do even though they were they are in OpenAI and they have access to the LLM they will not create that inside the model because for three reasons the first one is not scalable because you cannot like you have token limits and you cannot run a discussion or chat for each of the operations second one you cannot actually audit it so if somebody it comes after you and then look at the work then it's not possible to see what happened is the black box even if you have the code written in the discussion and the third one is still they didn't want all of the data in the model and to be trained because they want they want to put it all of the models or like I will say another company even though it's OpenAI you would don't want maybe all of your data there and so for these three reasons that's why it's not good to do all of your competitions there and the second one is actually a chatbot for all of the questions they receive so for all of the questions they get on so it's more the accounting department but on travelpolicies, reimbursements all of the things or how to categorize expenses they created a chatbot to us where all of the questions and like this they could filter down to only a few questions per day rather than a hundred of questions from all of the new OpenAIemployees.

And creating these chatbots I mean they're obviously you can customize and use the assistance on the back end but I think and I don't want to be a Homer for one one LLM over another but the GPT's from OpenAI that you can build yourself not GPT as a generative they're terrible at naming things at OpenAI so they call them GPT's but these are chatbots that you can build with no coding and these are to your point there these are great tools if you have you know internal documentation assuming you're on the enterprise or team account where you know that your documentation is secure where you can build these GPT's with no coding so you could make your customer service chatbot your FPNA Q&A chatbot or whatever there's I mean and these are the democratization of these tools is that you don't have to be able to write Python to do it these are very automated easy easy tools to make so definitely something if you're using Google if you have a contract with Google or Amazon or Microsoft they already have that as well so GPT is the famous one because we are so used to it but I think Copilot now since last week they have the same user interface where you just chat to create your your own mini assistance and all of the others big ones have that so I will also advise for people who are saying oh but my company doesn't have a contract with OpenAI and until they have one and maybe they will never have one or where do I start first look actually what is your company using if you have a contract with Microsoft Azure and I'm not affinity with them but you can do so much more already just with that or same with Amazon Web Services same with Google so just look at that first and if you don't have the money for it there is a lot of small small alternatives that don't costmuch.

Yeah and I think I mean that's if people so the50% roughly who have not used GPT or not using in their word I always think don't you know don't start just uploading your company's financial data and to one of these LLMs start with just interact with it ask it you know super benign questions just understand sort of the capabilities or I always say take some public company financial data that's already out there and put that in and play around with I think people you know that sort of remember the other poll where it was you know lack of understanding of what's out there I think the first thing people can do is just start interacting with it don't worry about solving your work problems right now just understand what's working and I think another part of that is you have to also kind of learn a little bit I'm not saying you've got to become a developer or a data scientist but you do need to if you're going to be trusting this tool or interacting with it you do need to understand that it's different than asking a magic eight ball a question and shaking it up and seeing what your answer is you need to understand what is roughly how is the LLM generating this this content and I think it goes back to that what I was saying about the problems with Google in their in their web search you know we forget because it's easy to anthropomorphize these models but it's easy to forget they don't actually have any knowledge of their own they've read you know the entire internet but they're spitting stuff out sort of in a statistical probability way so that's why a human in the loop if you know how to prompt it and get the right information and know how to sift through and know I don't need to be eating rocks or I don't trust this amortization table or whatever it is it still can be a powerful tool and I think you know maybe that's a good point to transition into let's go to our next poll question and this is maybe where we'll drill into some more specifics after this but the next poll question is which FPNA processes do you think you could use AI's help with so we've got budgeting reporting and analysis are the three categories here obviously a million more but we're going to try to keep it into a into a range here with what we as FPNA professionals do Gabriella what do you think what's the low hanging fruit out of these three do you think reporting I will say it on the data clean inside and consolidation there is a huge use case question what do you think my favorite one to be for the analysis part so because it's not just let's say describing what the data is coming like when it would be reported on but AI really can help to improve the financial performance of your company via these more like prescriptive analytics let's say yeah uh necklace yeah I will go with analysis because if you have the possibility to give data that you don't have time or don't have the volume or capacity to analyze you can get the help of AI but what Gabriella says actually what people want is first reporting because they spend so much time on reporting that all of the questions we get is not how can I analyze better my data first thing how can I just make a reporting and then I can work analyzing and the thing is because what I said at the beginning is your AI tools that you are using so if it's a LLM or if you are in copilot for or Excel the thing is it's not connected to anything so reporting is getting your data or actually getting a lot of different data sources to go inside one framework either your management reporting or your legal reporting and this has a model that and the data needs to fit inside it AI cannot do the integration for you because it doesn't have access maybe like in the future it will be able to write the code to do all of the APIs integrations to put it inside but it needs that somebody let that happen and you need to have like a Python I was like a cluster behind I will do all of these APIs until you have the final output inside Excel or PDFfile.

All right well let's let's take a look at our poll results here and see what everybody said so analysis77% reporting 57 budgeting 53 and it's you know I think that really I would want to just check all three but you know in selecting one Nicholas to your point on reporting I think about all the you know Excel models over the years that I've had where you've got you know you get your GL data at the end of the closed cycle and you got you got your index match or your VLOOKups or whatever you're doing to like pull it back in and like just the the amount of time that just goes in getting the data from raw data into a management report into your legal reporting whatever it is like you're not adding value there you're just you're formatting and it's it's frustrating but if you with AI that's sort of the promise of it is if you can automate that task and then you spend more time in the actual value add of providing some strategic insight to it that's where fpna can really start to add value and I think that this probably is a great transition into the next section that we wanted to talk about which is practical advice and best practices and Nicholas if you could maybe get us started with for our listeners who are you know I think where most people are right now yeah this all sounds cool I don't know where to get started so if if I'm listening to this right now and I'm thinking okay yes I want to start bringing AI into my shop what's the first thing I do what are the steps I follow to start being able to use AI in my in my processes I actually divide that based on what is the current state of your company because a lot of things that we can do with a small company you cannot do at a big company and this person so let's start first so I will say that if you are a small company meaning zero to one thousand even one thousand starts to be big but I had to really have to choose so everything that you can do for small one so I'll repeat myself for small one normally you can do that for medium and large company as well but if you are a large company you have much more means than small one so that's why I segmented this way so for small one I will start by making everybody educated on what is possible to do with the generic tools because there is no need to go and buy something there is no need to bring a consultant you don't have money you want to have a result first so you need to educate your own people on using generative AI tools like chatGPT,copilot, Gemini then you need to look at for example and that's just an example on Power BI there are already AI functions inside the tools you are using today and in Power BI you can ask questions and get a report for you if you ask the question the right way same in copilot for Excel same in PowerPoint so that's an example but this is the way to use AI in FPNA then the third way we talked a lot today about Python but for me it was really 2023 was chat GPT the tool to learn and Gemini and the other ones but L&M's 2024 is Python because this is how you are going to break the barrier of leveraging AI for your business because you cannot count on only external tool and Python is something you can use on your own environment with your own data and that you can do everything with it because you can automate for example you can consolidate 100 files in one file in just one line of code you can create a hitmap analysis for cohort which is really hard to do in Excel in Python is also just one line of code you can do forecasting with machine learning really simple as well like you explain also Glenn and those all of this in your own environment without any problem of confidentiality of data and also scalability problems then if you are a medium company so you start to have enough volume you start to have also money so you can start investing in native tools for example in your accounting or even in FPNA to have those tools that already work with AI algorithm so either OCR to digitalize your processes either NLP to ask questions on your data so you can invest on those tools and on my LinkedIn profile I made a map of the 100 AI finance tools so you can have a look this one wanted to change that but still the same but the third one on automation you can bring automation where for example when you need to collaborate with a lot of departments well instead of you being the person who goes to all of the people analyzing the emails combining all of the output you can automate that with automation automation tools same if you need to send reports to other people either you create or say dashboard that you can have on demand or you automate this and then the third part is where for large companies you can invest money you can bring external teams and build your own stuff so you can start to have your own AI models which are basically99% GPs plus your data and your fine tuning and that's where I've seen companies starting to do that like Eugobas and other companies they already have their own chat GPs they didn't start in finance yet but they start to fill up for example with the onboarding for HR or for IT problems you can ask the questions and you have all of the IT help guide inside your chat GPT then the second one you can create your own forecasting models because if you are that big then you have a lot of historical data you have also probably a lot of macroeconomic dependency because if you are a big company you are probably sitting selling to a lot of the world and the third one like I explained with OpenAI you can create your own chatbots so those are segmenting like this it's the way I will say to start small even if you are like if you don't have even if you are in a big company but you want to start like start like a small company and then go up the ladder and for everybody just quickly if you don't know where to start we did with Christian like AI maturity assessment so I will put the link also inside the chat but you can see here where you are inside your AI readiness and use this questionnaire to do that and I think a big part of AI maturity it you can't even really talk AI maturity until you talk data maturity and I'm sure you can access that out and you're in your assessment as well but it's you know we've been talking about digital transformation for 30 years now and if there are still companies who are kind of lagging on that but you know if there ever has been a time where it's you know you're banging on the drum of get your data house in order it's now because you're not going to be able to use any of this if you've got garbage data then AI won't work for you you know I'm the implementation of AI and I think Gabriela and Christian I want to ask this same question to you but from two different perspectives and Gabriela will start with you I wonder and thinking you know with what you're doing with tabs and with what other um companies are doing out there whether it's data rails or oracle or or whoever like for a lot of businesses if you don't you know going back to that initial survey question of um you know not have an expertise in house I think for a lot of people their first exposure with AI is going to come from the software from the SaaS tools that they're using it's going to be incorporated in that and it's not going to be something that you build in house so if you could tell me about you know what users can expect to start seeing in these AI powered tools or maybe AI enhanced tools maybe it's something they're already using but it's getting new AI functionality I mean how do you think that will uh find its way into people's workflows using uh third party software I think also like already must come in use cases like seeing more uh chat robots that you are able to chat with them and ask them to do like a report or take like I need to clean up this data I can remove to title can you add here and there but I would actually like or my way to of how to really implement AI would be to start with a problem like for example if we really want to automate something or improve something that's a start with the problem and from there we can see if actually AI is the best way to do it because in many use cases it could be that it's a collaboration issue so then we need to completely use a different tools so for people out there I would really like like them to think about what are like the biggest pain points and then like if AI makes sense to take it like for example build it in house to build it like in house AI it requires a lot of like IT work and software development work so it would be a huge investment for a company so usually that's where we will outsource to different softwares and then I would also like for finance as we have this huge um knowledge cap of not knowing how to use Python or to code then I think no code solutions without chat interface that is easy to implement and to use and especially that they care about the data quality I think that's kind of the way to go yeah makes makes perfect sense and Christian I think you and I are our kindred spirits here in in this and I think a lot of my like I never set out I never wanted to be a coder I didn't want to be a developer but I wanted to get stuff done and I knew back of the house if I asked something from IT or the dev shopper whatever we were always kicked to the back of the line and so it's like well if I wanted to get stuff done I'm doing citizen coding I'm running some bizaro database on my laptop instead of on a server I'm doing this citizen coding and I not not that I'm trying to advise anyone on the show to do this but I think maybe I'm going to throw this out on two levels one is the the warning of two companies you need to have an AI policy in place and make it very clear what it is and I'm not saying it needs to be locked down draconian but you need to understand what's out there how the people are going to use it because I think what you're seeing right now is for companies that don't have a policy and don't have a handle on this then you know they may tell their employees nobody's allowed to use AI for anything well a big percent of your workforce is going to become like secret cyborgs who are using it on the side probably in dangerous ways but if you do have a policy and you've given them a playground whether it's an Azure environment or enterprise or whatever you know whatever the environment is there are some things because I do believe that the way AI is not going to be rolled out like dropping in a new ERP system it's going to be rolled out you know give the employees access let them hold democratization let them find use cases for it so if you're in a company right now and you're trying to figure out ways to use AI what's your approach to using these tools at this point yeah definitely so I'll start first with the part of the they're getting stuff done that you mentioned that it's very important so how can let's say people start right like let me show my screen quickly and I prepare something for all of you so I always advise to get started and other mentioned like not with your real data but with data publicly available so you can just go to jack finance to say choose your any company there you can download a really like balance sheet income sales one but I just click here and then download this one and then go to charge up for O and just start with like something very simple access management consultant and analyze this data from an FUN adense I uploaded already there where I downloaded from the public available domain and it will start analyzing so already in here but for people to start seeing it will start generating all of these different like analysis like customerprice, phone time and so on so on so how do you take this with your real data after it does this analysis you just need to ask then for the actual pricing code and so on explain it where you're using it and then get the code and then from there then you go to actual Google code that is kind of like the Google docs of programming it's very reduced you don't need to download anything and you just need to copy the code in there and then right now I uploaded exactly the same data but the idea of this is you can upload in this environment your own data and then create exactly the same graphs and so on so also similar like I did quite quickly in here but for people if you want to follow along and so on I'll also drop down quickly in the chat where I basically give this type of example and all of these other examples to start with this like AI literacy in finance and FUNad.