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And now onto the show Welcome to FPNA Today, I'm your host, Glenn Hopper.
Today we have with us Mike Deon.
Mike is a senior finance leader with over a decade of experience helping businesses from Fortune 100 firms to startups unlock value and streamline operations.
As the founder of f9finance .com, he empowers finance professionals to save time and excel in their careers through automation.
The author of The Financial Storytellers Playbook and The Ultimate Guide to AI Prompting for Finance, Mike blends humor, expertise, and practical advice to simplify complex concepts.
With over 100 automation projects and over 100 ,000 hours of labor saved, his work has become a go -to resource for finance innovation.
When not consulting or advising.
Mike enjoys time with his family cruising and following market trends.
Mike, welcome to the show.
Hey, thanks, Glenn.
I'm so excited to be here today.
Yeah. So I'm going to shift everything and we're just going to talk about cruising for the whole show.
Is that all right? I could.
I could talk about that for hours.
Awesome. I guess we'll get to that at the end.
I think maybe our guests would rather hear about FP and A, because we do have some Hardcore FP and A nerds out there who, I think, would rather maybe sometimes be doing FP and A than cruising.
I could do both all the time.
That's life right there.
Yeah, there you go.
So let's dive right into it.
I want to hear about your career in FP &A from working with both the Fortune 100 companies all the way down to startups.
Very, very different.
FP &A, same concept.
Lot of different information, different ways to approach. So we'll get to that, but I guess maybe walk me through what initially drew you to finance, And you and I have this in common. It sounds like from very early in our career, automation and technology were kind of joined at the hip with finance you've used it through your career.
So walk me through some of that.
RW Absolutely. So when I first started in college, I was a general business major.
I didn't really know at all what I wanted to do with myself.
That wasn't until I took Introduction to Finance.
I remember sitting in our first exam.
I was looking around, and there were actually people crying taking the exam.
And I'm like, this is so easy.
This is awesome. I love this.
What's everyone so upset about?
And actually, afterwards, the professor approached me and said, hey, based on your score, I want you in my finance program.
And from there, the rest was history.
I started my career as an FP &A intern, for a media and entertainment company.
Really from start to finish, I've always just been fascinated by technology and what technology can do, getting the most out of Excel.
And the biggest thing is I'm never happy with the process.
A process is never good enough.
I might stop working on it because I've gotten enough of the value, but I'm just never happy with the process.
Built my career up at that company.
From there, I wanted an opportunity to just take my career in a different direction.
Moved over to the startup world where we went in, no systems. It was literally a brand new company, clean slate.
They'd spent a little bit of money, but didn't even have a QuickBooks set up, all of our systems integrating with HR, putting all the processes in place, and being the person for everything, which honestly really made me appreciate large companies having tax departments and payroll departments and all that because payroll doesn't go out, I'm fixing it.
From there, I jumped back into the media and entertainment industry.
Wanted the opportunity to work a little bit closely with the CFO, so took an opportunity at a studio to work side -by -side with the CFO, doing all the board decks, all the presentations.
And they had just put in their first consolidated planning system and didn't have any processes around that, had the opportunity to really stand that up, automate the forecasting, automate the consolidation process, where they literally used to be manually adding files together and didn't even have them linked.
From there went over to try something new with the telecom industry at Verizon helped stand up a center of excellence.
The great thing about that was they wanted us to try as much tech as possible.
I got my hands on nearly every dashboard program out there.
I got my hands on a lot of the new no -code or low -code coding tools and had a lot of leadership support to just tear everything down and rebuild it.
And then from there decided to jump back to my original passion, media, and entertainment, where again on the cusp of brand new systems, brand new ledger systems, brand new dashboarding systems, and just a lot of processes ready and ripe for innovation that have been done the same way for a long time.
That's back where I am today and just absolutely loving every minute of it.
Got you. In addition to your professional work, your side hustle is this f9finance .com site.
The focus of that is on helping finance professionals learn how to automate and optimize their workflows.
And tell us a little bit about F9 Finance and kind of what inspired you to start it, and what kind of things you focus on there and maybe from the feedback, what are some of the biggest challenges that you're seeing FP &A teams facing today?
Yeah, I think two things really got me going on.
The first was there's just, there's a lack of approachable finance content, especially when you get to automation tools.
It's almost like there's kind of some gatekeeping, either because people You know, want the job security or because people have something to sell you right.
And they make it sound more complicated than it really is.
And even sitting in FPNA teams in large companies, with a lot of resources, people didn't know about these basic tools.
If I go even today and ask 10 people if they know how to use Power Query, which is a free tool built right into Excel.
Maybe one or two of them out of 10 will know how to use Power Query.
And it is a free tool that every FP &A professional could benefit from.
So, that message is just not getting out there, whether it's just from how busy all of our teams are or from people being given the resources or feeling like they know where to go for those things or thinking that they need to go ask for budget when they can get started for free.
For FP &A, especially since COVID, our teams are getting smaller, but business is also getting a lot more volatile.
And I'm seeing the need for dynamic modeling and scenario planning more than ever before.
Our scenarios are getting wider.
FPA's insights on that, you know, the business is is demanding them now.
Whereas when I started my career, some people didn't even like it when we showed up.
We would, you know, kind of send them a report and that was our interaction with them.
And now our operations teams are demanding we have a seat on the table and come to them to tell them what's the range of possibilities and how do I move it to the best case scenario and away from the worst case.
And that is account.
That's only accomplished with a smaller team.
automate. We don't have time to be copying and pasting 20 slides into PowerPoints anymore.
Really you are preaching to the choir here because I think that being in the startup space, and I'm sure it's the same at big companies too.
You never have all the resources that you want, but being in a space where you're really strapped for resources and in my career was in private equity backed companies and the demands and the amount of reporting that they wanted and the speed with which they wanted it.
If you didn't lean into automation, I mean, I just wouldn't have slept.
And kind of my approach was always you know, I will spend 50 hours building something if it saves me two hours a week in perpetuity.
I mean, it's, you know, straight line math to figure out, you know, the ROI for that and one of your one of the things that you've talked about is saving organizations over 100 ,000 hours through automation.
I'm curious your approached automation, and I'll tell you mine and see how this aligns with what you do.
So for me, anytime I've come into a company as a new CFO or even as a consultant coming in, I wanna look at two things.
The human process flow, what people are doing now, and I don't even mean just in finance.
I mean, if we're tracking data, I wanna go all the way to the front.
If we're looking at the CRM, if we've got somebody in the sales funnel as a prospect or leader or whatever, let me gather that information there.
Let's pass that information and carry it through the systems and then look at how the information is being handled at each step along the way till it goes into billing systems and project management systems and all that.
But you start kind of with the process And you see where those bottlenecks and speed bumps are.
And then you know where can how can we automate this pain point here?
And as you do that?
The amazing thing to me was that automation and data go hand -in -hand.
If you want to get the data, the way to get that is to get sort of the automation of the process.
But you know, it doesn't matter what the system is.
You can. That's where your requirements come from.
And I'm wondering when you come in to a new company, and well, maybe there's two ways to look at it.
One is the startup where a system hasn't existed before.
and two is in a company where they're you know people might be used to doing things a certain way, very manual like you said, and in trying to change that to automation.
How do you approach all this?
My first biggest thing is to get quick wins because especially if you're coming in to established processes, and people don't want to change things, you have to get them on board that this is good for them that this is improving their life and it's going to make their day better.
While also kind of addressing that concern in the back of people's heads there will always be the concern is you know, what happens to my job?
Which is why, really emphasize this isn't just about making your day better.
You're going to learn the skills that are transferable if you're the person who can automate things.
If you're the person who can save your leadership money, you're more likely to be the one they're keeping around because you are you're not only adding value in your job, you're adding a value across the whole finance organization, and that approach has really broken down a lot of barriers.
Had a great example from Verizon where I came into a team and supporting actually the CFO's organization as their business partner.
And that is a lot of executives.
I think there were 30 or 40 vice presidents and up.
Everyone wants their own special reporting.
So when I came in, my team was producing 30 individual decks from 30 individual excel files and was the last one out of the office every single month.
Just crazy. This has to stop.
So how do we do this?
Because first of all, I'm going to have a bunch of senior executives who've been here for 20 or 30 years who will want their report.
There's going to be a resistance change.
Then I have the team who's going to have to keep doing those reports well we automate, so how do we keep everybody happy?
So for the team we sat down and literally went through and said this is what your week will look like and I guarantee you I will make you the first team out of the office.
If we do this, it's going to be two months and it's gonna suck.
And I'm not going to lie to you.
It's gonna be painful for two months and then it's gonna be amazing.
Got them on board. Then I went to started at kind of the top of the CFO and said I want to come to your teams. I'd like to move us to an automated solution.
This will be a great product that you're going to be able to go to the CEO and say look at this automation that my team is using and my team is the leader on this.
And second, I'm gonna give your team more capabilities because whereas today they might be getting a three or four page deck, they're going to have the capabilities to drill down to the point of if I click out a variance, I could even see the ledger detail and what's driving it, right?
I'm going to be able to answer more questions than today and everyone will spend less time on this process.
And he said, yep, sign me up.
So we developed this process using clicksense dashboards were able to put this into one single dashboard product that updated entirely automatically.
That dashboard product would push out at month end.
Once we cleared and invalidated the data and got accountants confirmation, the books were closed, would push out automatically to the leaders.
So if they didn't want to go physically into the dashboard, they would get the top sheet just in their email automatically.
And my team was done by 5 PM at month -end instead of 9 or 10 p .m. Everybody was just awestruck at this and these dashboards, then quickly rolled out across kind of the rest of all of the cost -based organizations with a very similar process and similar adoption.
Always a couple of holdouts, a few people were still not big fans of it.
Once you get down to one or two, it's pretty easy to get senior leadership support to say, hey team, we're just going to move forward with the dashboard. That project alone, once it's spread across everybody, I mean that was nearly a thousand hours a month of effort at a company of that size by going to these automated dashboards that one person could maintain.
And then, bonus, it wasn't just about my team not having to do that work, we were spending 90 % of our time helping research variances and make it better for next month taking things that happened and improving our forecasts.
We weren't just sitting around doing nothing, we were working with the business to improve things and make our forecast better.
And that's the real sweet spot because that's what gets you promoted.
A lot of times you're going to find these automation projects by themselves, that's not what's going to move the needle in your career.
coming back and adding $2 ,000 ,000 in revenue, saving a million dollars of cost, making your boss look good, that's what's going to move the needle for you.
So the crazy thing, I know exactly what you're talking about and I'm picturing when you get stuck in this sort of manual workflow, it's like you're on an assembly line and work is just coming at you and you can't step away or step above the assembly line and figure out how to address it.
You're just grabbing the items and doing your work.
And I think people fall into these traps of like, well, the first time we were asked to this report, you know, we just put it together as a PowerPoint or whatever, and the deck just kept growing and growing and the needs.
You know, it just bloated and said, Well, we need this, we need this.
And instead of stopping, backing up and saying, OK, let's find a better way to make this, they just keep adding on to it and you just it becomes more and more work.
And when you're in that mode of just data entry and putting that together, you don't realize that you're not adding any value.
You're just aggregating information that is not it doesn't make you a strategic partner.
It makes you a worker bee, and that's assembly lines get replaced by automation.
But the question is, if that's replaced, then what do we need?
And like you said, it is those strategic thinkers and ones that are providing value.
And I think there's just so many things that we do every day.
If you don't come at them with an automation mindset, then you're just going to do them the way you always have done.
And it's going to be with A .I.
and everything else coming along, it's going to be harder and harder to justify your position when you are an aggregator of data.
And I guess with all that, I mean, we can't go through every aspect of FP &A.
But maybe let's take a couple of examples that people are still doing manual today.
And so, you know, being a data rails podcast, I think forecasting is one that people still are doing plenty of manual work in.
And I'm wondering, maybe just we'll look at forecasting first and maybe a couple other areas, but are there some common pitfalls that you see in manual forecasting and or some examples of ways that companies could use automation to have these quicker turns of forecasts and, you know, whether it's the annual budget or just updating the forecast quarterly or whatever it is, any automation tips or hacks you've seen there?
There's three places where people really get into trouble with a manual forecast that automation is great for.
The first one is just data overwhelm.
There's so much data that they don't know what to do with it and they're spending their time being, as I would say, data janitors, not actually working on the forecast itself.
The second thing that I see teams just run into constantly is errors when consolidating and that they don't have a process that pulls things together.
That's where systems like data rails are fantastic, because it just consolidates automatically for you.
And if you're submitting something wrong, it's just either not going to go in or it's going to fall out where you can see that happen, right?
Consolidation is just continuous errors.
And then the third one I see is that you get so buried in the details, you can literally miss outliers in your forecast because you're trying to just tweak the things that certain leaders care about.
And there's an automation solution for all of these.
If you think about data overwhelm, I truly believe no one should be manually sourcing the data for their forecasts.
That is, if you're still working in the Excel world, that is a power query gold mine and everybody should be running those processes through power query.
It's saves your steps.
You can pull your data in, you can do your mapping, you should not be doing that manually.
If you have a system, the system should be connected to your data sources, or you should have standardized upload templates where you are not touching it.
All that should be done in the system, whether you need to do it for yourself in Power Query or you need to do it in the system.
An important point, don't wait for people to let you automate.
Power Query is in your Excel.
You don't need permission to do this.
You're not talking about getting a system.
Don't wait for someone to tell you you can just start doing it yourself.
From a consolidation standpoint, if you're working in Excel again, you can literally have Power Query pull these together.
You can even at the most basic level, just have standardized Sheets where Excel can add them up and process them.
Whether you do it with formulas or with macros or if you're doing in the system, which is the best scenario for consolidation, have these processes to remove errors and then build checks in for goodness sake, right?
Automation doesn't mean running an AI tool.
It doesn't mean code.
It means having the computer do something you'd otherwise do having Excel.
Check. Your work is automation, right?
That's what automation is just something you don't physically have to do.
So take advantage of these checks and build that in.
And And then on the data and the outlier side, I've been spending a lot of time testing ChatGPT, and if you upload a data set, say, look for outliers in this data, what might I be missing?
And it's going to go through and scan it and say, you know, this day the data doesn't make sense.
You might want to look at this.
That is AI's sweet spot.
It's not that good at giving you a really good usable forecast. It's phenomenal at telling you where you might be very wrong or where the data is off from trend and where you want to look.
It is phenomenal at that.
Use it, run it through there, run your own forecast. Your value is delivering that great forecast, but AI is great as a second set of eyes to make sure you're not missing anything computers will process data at that scale faster then we ever can.
They always have been able to, right?
Yeah, I love that. And it's a you know, so whether it's variance analysis or finding correlations or whatever, I mean, you can run, you know, you build a correlation matrix and in Excel or whatever, it's fine.
But it's why would you do that every month?
But it's a quick thing to ask, and like you said, machine learning is designed for this, but running data through and say, look for correlation between any of these categories or whatever.
I know correlation isn't always causation, but if it's p -hacking your way into finding some additional insights, but letting AI do what it's good at, and then getting insights that you may not see, and you're exactly right.
Because the focus is it's like you have of the official KPIs that you've defined and then maybe there's somebody on the management team or somebody on the board or whatever that has very specific things that they're always gonna ask you.
So you end up focusing, you know, laser focusing on a certain area and sometimes, you don't see the forest for the trees because you're so focused on that and I think that that is an area where, you know, outside of automation, just having AI come in and be another set of eyes looking at something and catching something maybe that you wouldn't.
Now, a lot of times if you, you know, ask AI to analyze data that's an experienced FPNA person.
It's not going to come back with any like, you know, earth -shattering insights that you wouldn't have figured out from looking at data for years.
But it is nice to kind of have that that AI partner there.
Let's see, let's take another one.
So forecasting, what about variance analysis, something that we do every month when we close the books, are there ways that FPNA teams can leverage automation to kind of simplify the variance reporting and maybe take it to a level where they're able to add that strategic value there?
Yeah, absolutely. So the first thing, just like with forecasting, you should not be manually getting data.
You should have a process, Power Query, your reporting system, whatever it is, to get that data automatically.
It's going to be cleaner.
It's going to be accurate.
You're going to know what you're getting.
That is the biggest thing.
The second thing is you should not be manually building any reports.
I still see so many professionals in the trap that their deck, you open to the first page and it's a wall of numbers, it's a giant detail P and L and they just go down the rows and talk about the variances, right?
The value we add is in telling people what they can do about it.
Yes, you need a snapshot.
You need, here's revenue expense, etc. Of course, you need that.
But full P &Ls belong in the appendix.
You need to be diving into what matters and what has changed in that month.
And that's where having a tool like a dashboard, whether it's in a power BI or in your own financial systems, having those dashboards where you can drill in, find the variances and then really focus on that.
If you're putting a deck together, it should be bringing insights.
If it's a wall of numbers, you can get that and you should be getting that from a dashboard. That's absolutely critical.
I've been testing with AI tools.
I've used both Co -Pilot and ChatGBT for this.
I'm just running my variances in the system and saying, based on my prior month comment, which I trained it on, and based on the variances you see here, what should I prioritize researching?
I very much will not use AI to actually write my variances cause that is where we need to come back with value, All I can tell me is what changed.
But what it's really good at is helping me prioritize so I don't go too far in the weeds on any one topic.
It's going to say, well, last month you talked about this.
The variance is identical.
It's likely the same driver.
If you look at this, this is a new variance.
This is off trend. This is a high priority to research. And it's very good at seeing those things that have changed.
And it's really good at remembering what I've done before.
Which we do this month after month, we can kind of forget.
But if I say, here's my last nine months of commentary and it sees that I've said the exact same thing about labor for nine months.
I probably don't need to do much research there, but all of a sudden, cost of sales percent has skyrocketed to 5 % points, I can go look at that.
Now, clearly that's a bad example, I would know to go look at that.
But if you go, the more you go down into the levels of detail, the more AI is able to just pull out, here's your workflow for you.
Go do the work, don't use me for Epico, do the work and follow that workflow.
And that's a really fantastic way.
And most importantly, when you automate, don't just clock out and go home.
Spend time with the business, right?
What matters, these reports, the variance analysis, it's a tool.
It doesn't change anything, it doesn't add value in and of itself.
What adds value is helping the business change course on things that need to be changed or sustained things that are working.
And you need to be spending time with the business to give them that value to bring back those insights.
Otherwise, you're just spending time doing things that don't move the needle in the slightest it's a tool.
It is not the end result.
Absolutely. And it's, you know, and it's it's only as good as the human in the loop.
And I still think, you know, the the domain expertise that we bring to it, like we it's the example I always give and I apologize to listeners who probably heard me say that before on this show is if you don't have the domain expertise of finance, you don't know the right.
You know, somebody could hand you financial statements.
You don't know the questions to ask.
You don't know the difference between COGs and other expenses or EBITDA and net income and operating income and you know, all the things that we look at and you know, really how to analyze it.
You know, the way to look at capital spend versus, you know, opex and all that.
So it's you know, you have to have that domain expertise.
And so, you know, the right questions to ask.
And rather than just offloading all those questions and everything to the AI, you just get that much more powerful if you know the right questions to ask and you keep, you know, you use it as an aid to make you more efficient or think deeper or be able to spend your time, whether it's AI or just automation in general, but that's, you're getting at the heart of it of how we overcome, and I don't know how many people still think of finance as a cost center, but, you know, we had overcome that sort of historical stigma that we've had of not providing, you know, true value to the organization on a monetary
level. We're just, oh, it's a cost center.
You've got to, you know, you've got to pay to close your books just like you've got to pay to, you know, use software, so.
You mentioned dashboards and also thinking about that wall of numbers, and I've been in so many businesses where they haven't established like sort of the the clear KPIs.
These are the KPIs that matter and these are the ones that are actually, you know, levers that we can pull, that we can make a difference in a business.
And so many times there's these massive dashboards that are just, you know, chart after chart after chart, page after page .
There are so many tools out there right now that can give you these real -time dashboards that I worry.
You also mentioned information overload.
There can be so much data out there.
And these dashboards, if there's too much information it becomes like billboards on the interstate and you don't know what to look at, what to pay attention to.
There might be one or two of them that you kind of glance at, but with all this power in our hands now and the ability and more and more of what we do, kind of moving to this real -time close are certainly very fast closes for companies that aren't in that real -time phase.
But what do you think are the components of a great real -time dashboard, and are there specific tools or technologies that you like for building them?
Yeah, the biggest thing which goes counter to most of the dashboards I've seen is, for a successful dashboard, you should put as little data into it as possible, absolutely as little as possible.
The less data you put in, the more successful your dashboard will be, which is counter to most people who wanna put an entire Snowflake or SAP database into their dashboard. It's slow.
It's overwhelming. It doesn't answer the question.
Small focused data sets focused on actionable items. What are actual items?
The way I think about KPIs from a dashboard perspective is almost like, what are our forecast drivers?
What are the things that we can change or influence that will drive the business forward?
I like to build my dashboards with almost like a top sheet, right, where you'll go in and these are the key KPIs.
If I'm responsible for managing an hourly cost base, My KPIs are gonna be, what is my overtime percentage?
What is my hours? I'm not gonna put labor rate on there because I can't control it.
But I can control the amount of overtime I run, I can control my double shifts, things like that.
What are my actionable items?
Then on the back, so you can click in that and say, okay, I have this labor variance, I wanna research further, you can go back and you can get a sheet of here's all of the labor information.
Then you can see those if you wanna research it and follow that thread.
But what is the truly actionable item?
I, as an operator, can raise or lower my overtime spending, and that will change my labor rate, that will change my labor cost. That is the KPI, not the labor rate.
So building with this few kind of key KPIs for what that leader needs, and then having the ability to go back to other boards in the back of the dashboard if you want additional information or detail that you're looking for.
That way I, as the finance professional, not overwhelming you in the close, the data's My dashboard is lightning fast and it gets used because if it's something that I can change, it gets used.
I love that you said that because one of the things I've been playing around with, and what I think for generative AI, a lot of ways that we're going to see it used.
Our software providers, your net suite and and data Rails and all these tools out there are going to implement generative AI and as a way to interact with the data so people aren't going to have people are going building their own large language models and they're not going to have to everybody become an AI engineer, but it's just going to start being included in software.
And one of the tools that I've built, you mentioned low code and no code tools.
I've been playing around with replit lately, which is just prompt based building apps, but incorporating AI analysis into, you know, financial statements.
So the idea is you close a month, you get the financial statements, you upload them in, it does a first pass on them, does kind of the charts and graphs, kind of what we would do when we get financials, when they're closed and we do our first pass on them.
We do that kind of variance analysis and look at how we're doing compared to budget historically all that, but then provide sort of that high level of footnotes on the financial statements.
And I think that a lot of us are gonna see that.
We're gonna start seeing that incorporated more and more into ERPs and CRMs and GL tools and all that.
That's how most of us are gonna interact with it.
And I think that goes along with the very simple high level dashboard where if you want to drill in, you open dashboard and you can drill a couple levels deep sometimes to get that additional layer of information.
But when you hit that dead end, historically that is meant now I've got to go to the BI team or to FP &A and say, I need a report to see this.
But I see in the very near future, as people figure out how to incorporate generative AI into these systems, instead of having to wait for a person to pull that report, you just ask your dashboard the question and get that level of detail there.
So then it becomes even more important to minimize that top -level to really make you focus on what's important, but you know you can dig in when you need to.
absolutely. We still need to be the guides.
That's that's becoming the role is not just strategy, but the navigator and navigating the business through the finance process and through data and through the value that we can bring to the table.
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Exactly. I guess going back to the generative AI, you have written and you shared with me a great, the ultimate guide for AI prompting for finance.
I hate the idea of in general of prompt libraries because it makes people, I think it's a good introduction, but really people need to know how to interact with generative AI and not memorize plug and play libraries.
even though I mean, if that said, if there are, you know, things that you do every month and you've got, you know, you know, the perfect prompt.
But I just saw a study yesterday that, you know, the exact same prompt is it's generative AI, so it's generating new content.
You're not going to get the same results every time you do it.
So another reason that prompt libraries aren't my favorite.
But tell me, I mean but that said, even without a prompt library, we need to know a basic approach, and there's a million of them out there, but a basic approach for how to interact with these and the fact that you've made one targeted for finance.
Can you tell me a little bit about the guide?
Absolutely. So, just one anecdote on what you were saying about generative AI and the prompts not being continuous.
I was actually reading a study recently that AI models provide worst responses in July and December if you prompt them because human content is weaker in July and December.
So they've actually done studies that it's not as effective And AI models, which in theory should be able to read all the text you give them, will focus more on the beginning of text and the end of text just like people.
Like they're building people like tendencies over time, which also influences the prompt you're getting.
Just like you prompt a person that's different, the generative AI is constantly learning and it's picking up some of our bad habits, which I thought was just fascinating and also a little, little scary at times.
So when I wrote the AI guide for prompting, what I really wanted to focus on was, you said, teaching people the methodology, teaching people how AI tools work, how they think, how they're trained and how you can structure your own.
I do include in the back, I include some example prompts, but very clearly as a way of kind of getting inspiration for what you can do versus, you know, copying and pasting, right?
It's, here's some things you can do you may not have thought of.
So in that, I have a Spark framework which teaches you how to construct a prompt anytime.
I think the two most important things about that process are the first and the last step, which are also the things that most people forget.
The first one is to set the scene.
AI tools perform very well if you tell them how to behave. So if you're starting a finance prompt and you tell the IT will act as a financial analyst, I found you'll continuously get dramatically better problems by telling it what you expect of it.
And then the last thing again, AI models are trained to talk to us like a person would talk.
So I end every prompt by saying, ask me any questions you need to provide the best responses.
So it gives me the extra boost that if the AI doesn't fully understand something instead of guessing, I'm telling it to ask me.
And it will, sometimes it will just run it and it understood me.
Sometimes it will come back and say, could you clarify X, Y or Z?
Those two steps are so commonly missed and the quality of my prompts, no matter how rough I am in the middle with the actual task and the output I want back, back, that piece of telling it how to behave, it puts it in the mindset of a financial analyst and it will not make guesses of what I want.
It will ask me what I want.
That is really the goal.
This way, if you learn this framework and if you build that into every prompt you write, you can work in any system because I don't focus on any specific system in this guide.
It works for all of them, cloud, chat, GPT, Co -pilot, Gemini.
I want it to work with whatever you're in front of.
To your point, if people start putting these tools inside of software, it's still using these same really three or four large language models.
These prompts will work for you and you'll know the secret to success for everyone and then it's easy to train other people.
If you want to teach people how to do this, you can easily train them because you have this kind of cohesive framework of how to ask AI for what you need.
Have you experimented with any of the new reasoning models?
Like, whether it's, you know, O one or O three from open AI, or I guess Claude Three Point seven has some sort of inherent ability.
Like, it will determine how much it's going to reason.
And so, rather than the historical way that they've done, where they just immediately spit out a response, these reasoning models will stop and kind of think about it.
The reason I brought that up is your prompt is sort of forcing that reasoning step, even on a standard model where you're saying, ask me questions if you need more information.
And I think that the reasoning models are ideally doing that on their own.
I was wondering if you've tried out any of those and seen any impact from your prompts or any difference between the two.
So what I've been using reasoning models for isn't those kind of one -step tasks, because typically you want to you want to give AI if you're using traditional models one step.
I've been using the reasoning models to test kind of the agent, the AI agent functionality where it can do multiple steps.
And what I've been working with a lot there is giving it a problem.
So like, I'll say, like I still follow the telecom industry closely, I'll say act like I am Verizon, and I need to understand how AT &T and T -Mobile are performing in quarters relative to me.
So I'll say, first of all, you know, here's the 10Q for Verizon.
research, study, understand this and ask me any questions you have to make sure you're on the right track.
Then go out, get the information from AT &T, get the information from T -Mobile, study it the same way you saw Verizon, and then complete a SWOT analysis.
And then you'll see it kind of reiterate that SWOT analysis.
That kind of a multi -step process is really good for the reasoning models.
With my prompt structure for single tasks, I don't see the need to go to the slower models because they're pretty slow.
If you go to 01, it's it's a lot slower, but it's the multi -step tasks, I can kind of run it.
Like I'll start the AI running over on this screen, I'll pop over my other screen, get some work done.
For things like that, I love the reasoning and it's really good at keeping its logic through different steps and following the original ask while it runs multiple things.
I just haven't found for a single process, like, hey, here's this file, read it, summarize it.
I haven't really found it's been necessary to use that kind of model for that.
Yeah, makes sense. Makes sense.
Well, you've obviously been experimenting a lot with AI.
And I think a lot of our listeners are too, and I know I probably sometimes err on over talking about it, but I think it's so I'm a fanboy, I guess, of what's possible with generative AI.
But I think all of us now kind of see the writing on the wall with this.
But there are issues still, I mean, even even the brand new frontier models are there's still issues with hallucination.
And while you might be able to get away with hallucination in a field where there are gray areas, you know, in marketing, if you're marketing copy is slightly off, that's not the end of the world.
but you know, the trial balance has to balance.
We have to, you know, there are no gray areas in numbers so we need to be specific.
So you know, I think maybe adoption is a little bit slower but people are starting to lean more and more into it.
And I'm wondering, as you've, and like you said, you're not offloading your job.
You're using as a tool to help in your job.
But how do you see, especially as the models, as the technology grows, the models get better and with your focus on automation prior to AI maybe with that lens, how do you see AI kind of transforming and FP &A workflows in the next few years, and with that, I guess maybe a two -part question, what advice do you give to people working in FP &A now who wanna get started in kinda thinking about that, whether it's what they need to be focused on learning, how they should implement it today.
I don't know, that's a lot.
Maybe we need to pause between, but what do you think about what's coming in the future?
So I think one thing I'm spending a lot of time thinking about and concerns me for the future of the profession is I think we're gonna see a lot less entry -level roles, because it's the entry level kind of that analyst role where we're doing a lot of the automation, I don't necessarily see the lead analyst individual contributor managers, I don't see kind of those like expert level individual contributors really shifting a lot because there's this technology needs a lot of support.
But I do worry about entry level roles.
And I think that we have to be cautious about the pipeline.
And I think the same is true for accounting as this technology rolls out, that's something I'm really thinking about, something I encourage—I'm encouraging a lot of people in college now to study data science because that will give them another way into finance if they are not able to find an analyst role.
So that's something I'm really encouraging.
Everyone will need to have some level of understanding of data science.
Right? If you're working with AI tools, you are so much more effective.
Even if you're not fully designing it, even if you're not coding things, if you understand how the tools work, you'll get dramatically better responses back from it.
So I'm encouraging watching everybody to spend some time, even if you just are watching a couple of YouTube videos or listening to a podcast. Start understanding data, how it works, not just the financial or GL data.
Then you also need to be very focused and you have to get out of this mentality like I was saying earlier that the reports and the forecast themselves are what matters.
What matters as I would say, I call it the real deal.
Three things. Increasing revenue, decreasing expenses, and making your leaders look good.
Those are the three things that move your career, not the reports, not the forecasts, tools to get to that.
So as the workflow goes, you have to find ways to add value and you have to find ways to add value that people above you notice.
Otherwise you're going to be one of the ones being automated out of the workforce.
Yeah, absolutely. And I love what you said about data science because to me it goes hand in hand with what I said about your domain expertise in finance.
If you understand how to read financial statements and you understand the role of FP &A, you know the right questions to ask versus just handing someone off the street, financial statements and asking them to analyze them.
When the barrier to being able to do data science in the past, used to be where you have to know Python and you have to really understand all this at a developer and engineer level, but with generative AI, you can now interact with your data just with your natural language, so your natural language becomes the new programming language.
But if you don't know the basics of machine learning, are we doing classification or prediction or clustering, you don't know the right questions to ask.
While you have this powerful tool that could help you with customer segmentation and turn predictions for your forecast and all that, if you don't know the right questions to ask or what it's good at and what it's not, then it's a very powerful tool in the hands of somebody who doesn't know how to use it, you know, all that power is sort of negated.
So you're, you're spot on there.
And I did talk to someone the other day who got an MBA and analytics degree at the same time, and I've heard about those degrees for years, but I'm kind of surprised that even today, I'm not seeing as many of them out there, because I think the two go hand in hand and I think like people kind of on their own are going to have to take, take this on to learn data science.
And it doesn't mean, you know, we, we went to school to be finance professionals.
I'm not saying we all have to become machine learning engineers, But like my last book, AI Mastery for Finance Professionals, it's very little about generative AI.
It's mostly about just this is how machine learning works.
These are the applications of it because to your point, if you want to survive as things are being automated, like those entry levels are gone, if you want to provide the value, you have to understand the technology.
You have to be able to use it.
And then that's how you're adding value to the organization is by having these powerful tools that you can use appropriately to make you better at your job.
It's so true. You never want AI to guess, right?
Like I can get chat GPT even spit out a forecast but it gives me a terrible forecast unless I tell it how to forecast. If I say use time -series analysis with seasonality, it gives me a great forecast. It does very well with that.
If I just say, give me a forecast, who on earth knows what I'm going to get back.
Even with machine learning, using AI, you can quickly overfit your data models.
And if you don't know how to avoid overfitting, you're going down a path and thinking you have a usable product when you don't.
and that's why that's just so critical.
It's still needs direction and guidance, you don't want it to pick for you because it may pick wrong.
Yeah, absolutely. I could talk AI all day, and especially when I have someone like you on the show, I feel like we could geek out on this for a couple hours, but I want to get back to one more thing too, because everybody's talking about AI and people are conflating now AI and automation.
But you've been doing automation long before AI was part of it, And I think that while we, you know, AI is the shiny new thing that we're all kind of chasing right now, we can't overlook just systems, process software, sort of the automation that's been around for years.
And I think getting back to that, if you're talking to other FPNA professionals, like in their thinking, yeah, we got a bunch of annual processes.
I don't even know where to start.
Are there like two to three low -hanging fruit kind of processes that you would recommend?
And maybe this is too general a question, I don't know.
But are there, you know, some specific processes that you'd say, hey, take a look at this and see maybe there's something you're doing.
Yeah. So this will sound a little counterintuitive from a finance guy, but I think if you've done no automation, the first thing I encourage people to look at for automation is their email inbox.
And there's two very specific things here.
There's kind of the bills and invoices, and then there's approval workflows.
You can take two approaches.
One is a great start to automation is literally learning how to just use rules and filters and your Outlook or Gmail inbox.
That's automation and it's so natural, it's so easy to do.
It will save you so much time, it gives you confidence.
The second is you can quickly set up things with Approval Workflow as you can do it with SharePoint, you can do it with Google Drive, where approvals will just automatically route in and out.
And you can use that instead of tracing emails through 20 people who need to approve something, it has statuses.
And that's a really easy approachable way before you get into places where you have data, where you have to consider data cleaning all those things.
It gives you a really fast, quick win and then immediately saves you time, even though it's not kind of a core finance process.
But quick wins are so important.
From there, the next thing I would really focus on automating is data collection and consolidation.
We all have Excel on our computers.
We all have Power Query and Power Pivot, and they're free, and they're sitting there ready to use.
If you are an FPNA professional, you have a use for Power Query and Power Pivot.
I guarantee it unless you are an executive who Dontouch Excel, you have a use for Power Query and PowerPivot, and that is the first thing you can learn.
And I have literally developed programs where you can teach people this in each software in 20 minutes.
You can get the basics, you can get up and running, and it will save you hours.
That's number two, is just look at your data, look at the reports you're building, and automate the backend using Power Query and PowerPivot.
And those lessons are on F9 Finance?
Those are on our YouTube channel.
Yeah, they're on F9 Finance YouTube.
Yeah, there's the last one I would say is monthly reporting decks where you don't have a lot of variation where it's kind of what you know, the standard ones that KPIs that you're doing month after month put in the dashboard. If your company has an Office 365 subscription, there's an 80 percent chance that you have access to Power BI for free.
Pop it in there again.
My biggest thing is don't wait for permission.
Go see what is there.
I know every one of everyone listening who has Excel, has Power Query and Power Pivot and almost everybody who's on Office 365 for enterprise will have some type of Power BI access.
use it, it's free, get started, get these small, quick wins, and then it just compounds from there.
You get so excited about what you've done that it's just a snowball effect from there.
And bonus, you become the go -to person on the team.
It's really good to be the automation person.
Being the automation person is so important because right now executives, senior management, and companies, all they're hearing is the boards, the investors, whoever it is that's driving the company, or, you know, even from the CEO, they're all hearing about AI and automation, and, you know, we've got to get more efficient.
And if you're the person who can actually make that happen, whether it's AI or not, I mean, you know, everybody's kind of AI washing everything that they do now and saying, oh, that's AI when, really, it's just a rule -based something that's been around forever.
But if you're the automation person, then, you know, that is how you show that you're valuable to the organization and make them more efficient, so timing is perfect on that.
And I guess on that note, before we kind of get into the personal and fun questions part of the show, is with everybody talking about AI and automation right now, is there a common misconception about automation or AI in finance in particular that you'd like to debunk?
There's still a belief among almost everyone I talk to that AI and automation requires Python, our advanced coding skills, you know, $10 ,000 a month software, that is still the understanding for most people.
I'm starting to see more companies are starting to get co -pilot, it's starting to be a little bit more approachable.
No code is becoming bigger, but that's still just a general perception from the corporate FP &A population that it requires these advanced coding skills.
Because five to 10 years ago, all we were talking was Python. That's what I really want to break.
Again, if you set up email filters to not have to touch emails, you're automating.
If you use data validation to make sure you're not putting crap into your forecast, that's automation.
Conditional formatting, automation, I teach all these tools.
These are automation and you need to think about it that way, you are automating if you use these tools.
Because the computers doing work for you, you're not highlighting every cell yourself.
If you come with that mindset that there's all these tools out there and it's just about what am I spending time on that I could spend less time on, it becomes so much more approachable and people have an easier time getting started.
Versus I have to go take a class to learn Python, I have to get permission to buy the software to work with Python. I need IT permission to even use Python on my computer in the first place.
It becomes just this overwhelming mountain versus hey, I'm going to open up outlook and set a couple rules for these emails I don't need.
Perfect perfect and quick wins right there too, so.
Alright, let's bring it home with our last two questions that we ask everybody.
So first one, what's what's something that not many people know about you?
I was actually an improv comedian in college, which is not normal for finance people, but I grew up loving whose lines anyways, JS I spent four years on stage, making people laugh before going into finance and making people cry.
JS Perfect. And maybe the takeaway from improv is, the number one rule is never end in a no, don't stop the flow, so you just keep everything is positive and stay moving.
Maybe that's something that you can apply in your finance as well.
JS Oh, absolutely. The best way to work with clients.
Alright, now everybody's favorite question.
What is your favorite Excel function and why I have kind of a more obscure one minus equals RRI which does basically Kager calculations really fast. I know for years we all just did our kegers manually and then someone pointed this out to me and it's just a it's a copy and paste kegger that you can literally build.
You can even build it to be dynamic, so accounts the number of periods by itself and will move the present value of future value if you use offset.
I love that formula and almost No one knows it.
Every time I point it out to somebody, I'm always the first one telling them it exists.
So that's my favorite.
That's so, yeah, I love that you said that because I just did a Kager calculation yesterday and I was so annoyed having to go through it and type all that in, just because I was doing it in multiple cells and as someone who was an executive for a while, I'm no longer cutting edge on Excel stuff.
So it was a very clunky thing for me.
So now I'm gonna go take that same spreadsheet and go apply that, so super excited to try that out.
OK, well, man, this has been great, I guess, just last thing before you go, and we'll put we'll put all this in the show notes and everything, too, but how can our audience connect and learn more about you and F9 finance and just kind of follow what you're doing?
Sure, the two best ways I put out, like a ton of free content for people on automation, we've got our newsletter, the Finance Automation Insider, you'll find that on F9 Finance.
That's just weekly tips, just free stuff on here's how to automate processes, both for work and also how to automate your career for success.
And then I do a ton of automation content, live videos, tutorials, walkthroughs on our YouTube channel, which is also at F9 Finance, just trying to get the word of automation out there and keep giving people free tools to set their career up for success.
Well, Mike, thank you so much for coming on the show.
It's been an absolute pleasure.
Thanks for having me.
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