AI Tools Are Automating Excel. You Have to Be Technical with AI in Finance to Survive.

Full transcript

Lightly edited for readability. The audio is the original.

0:00The Excel expert AI just replaced

Think about the best Excel person on your team. The genuinely best one. The one everybody sends the broken file to on a Thursday afternoon, and it comes back totally working by Friday.

10 years ago, that person had a whole career on the back of that. It was worth a promotion every few years, and it was worth it because they were the fastest way to get a defensible number out of a mess. But the thing is, in a world of AI, I don't think that is a career anymore. I think it's a task now, and the task gets cheaper every quarter.

So today, where did that career actually go? It went in two directions, and the two directions have almost nothing in common.

Welcome to Excel at AI in Finance, the show for finance pros who want to get home sooner and be the ones their company can't afford to lose. I'm Mike, I run F9 Finance, and I'm a senior finance leader working in the trenches. Each week, I share the AI tools, workflows, and mindsets that get you where you want to be. All right, let's get into it.

0:58Bill, the Excel wizard who never touched a mouse

I remember this one Excel whiz I worked with. I'm going to change his name. Let's call him Bill. And Bill was an absolute wizard.

Don't get me wrong, I've spent a lot of time in Excel. If we go by that 10,000 hours rule, I've definitely spent more than 10,000 hours in Excel, so I'd probably be an expert. But Bill knew everything. He never touched the mouse. He could hit five keys in a row and make Excel do anything without lifting his hands.

He was fast. He was quick. He knew what every single button did. Anything you would ask Bill, any problem you brought Bill, he would fix immediately. He was the Excel wizard.

And that became his entire job. Excel was the bottleneck. We needed good numbers out of the system. We needed to put them into our decks. And when we couldn't do that, we went to Bill.

Bill patched everything together. When the planning systems weren't capable of giving us what we needed, Bill patched it together. And that was good enough.

The challenge is that was 10 years ago. And things have changed, especially as AI has started coming on the scene. A lot of that work, looking across an entire model and finding the problem, knowing what every button does, that's all stuff that Copilot in Excel, Claude for Excel, ChatGPT for Excel can do.

And they're doing it really well, especially with newer models like Opus coming on the scene. The performance is fast. It's quick. And that's not really a bottleneck.

That capability has been handed to everyone. Even though most of us are still living in an Excel world, the capability to be Bill is now sitting right in our desktops.

2:41AI in finance is splitting FP&A careers in two directions

As I look across the workplace, I'm seeing it diverge in two different directions. Some people are deciding to become technical. They're investing time in learning Python and learning how large language models work, how to connect data together, how to clean data. They're becoming the people who can run automations and make everything run without them.

People are relying on them, because those people in the middle, that's who they're going to when the AI is getting stuck. That's who they're going to when they need different data connections. That's who they're going to to build.

Then I'm watching another group really double down, especially in FP&A, on adding business value. They're getting closer to the business. They're using AI to do the production faster so that they can get closer to the business and make decisions faster.

3:27The third option: use AI to skip the messy middle

And everybody's left with three options. Become super technical, the person who makes it all run. Become super relationship focused, the person who has tough conversations and drives business value.

Or, and this is what I'm shooting for, a hybrid, where you use the power of AI to be able to accomplish the technical things while also focusing on adding business value. That gives you a leg in both worlds, but avoids that messy middle. By working in both, you're stepping over the messy middle and handing that to AI. And that's the way we want to address our careers going forward.

The deal in finance used to be simple. Get good at the tools, get good at the process, and the profession will look after you. That was true, and it was true for a long time.

But now the tools and the process, that's AI's sweet spot. Making it all run and being the person who can communicate, have tough conversations, and drive business value close to the business, that's something that AI is a long ways from, if ever. And that's the change that we need to focus on as we think about our careers.

5:00The production work was the job

For 25 years in finance, the hard part was just getting the numbers into a usable place. This was the era between essentially paper spreadsheets and now. And Excel became the symbol of that era.

But it's not really about Excel. And that's what I want you to pay attention to as we start thinking through this and what your career needs to look like.

That area is the production. That area is getting data out of systems, analyzing the data, putting it into a usable format, building it into a deck that can be relied upon, making sure the numbers are right month after month. All of those different tasks that we do, the reconciliations even, that's all the middle.

That production work that's not truly technical in the sense of coding or AI models, that technical work that is the core of finance, that's the sweet spot for the AI tools that we're seeing roll out today. So that bottleneck, that production in the middle that we all called Excel, is rapidly shrinking as AI can begin doing it better.

The really tricky part about it is no one is seeing their work just vanish overnight. What they're seeing is it gets smaller and smaller and smaller until, in the end, it's just gone.

And what you might notice is that a year or two ago, that work, that work that was the middle, that work that was the Excel, you were relied upon. You couldn't go on vacation without something breaking and somebody needing you, because you were the production. And now those calls might be getting less and less frequent. And that's a sign that it's time to start focusing on the pivot.

6:49What "get more technical" actually means in finance

When a finance pro hears "more technical," they picture having to learn Python and turning into a data engineer. But that picture is wrong. When I talk about being technical, it's a lot closer to managing a junior analyst or an intern than it is to programming.

Sure, if you want to start programming, go crazy. But remember that AI tools can do a lot of that. What you're looking for is being able to manage the systems and manage the people who manage the systems.

Read output like a reviewer. Think in processes. Think in how data moves from one system to another into your files. Hunt for where it breaks. Hunt for those system bottlenecks. Make sure things are checkable, auditable.

That's what being technical means. Physically running a process and owning one are two different jobs. So when I say be technical, again, you can get in deep to the systems and you will do great.

The huge bonus of AI is that anybody can become technical today. I wasn't a coder before the world of AI. And now, I'm definitely not on the level of people who do coding full time, but I've had AI teach me coding.

Every time I hit a roadblock, every time I can't accomplish something, I go to AI and then I work through it. I'm getting the reps of actually building it and working through the problems, but using the AI for the bulk of the production. And that is just as technical. And that's what I'm talking about when I say technical.

I want you to think about one recurring job you own and how you can build it so it runs without you. Then take a week off from touching it and find out whether that was true. That's how you can start moving towards being more technical and really feeling out and understanding what this means.

8:38Getting closer to the business, and the cost center problem

The other half of this often gets called soft skills, but that's not how I want you to think about it. When I say get closer to the business, it means things like, why does the June number look the way it does? And knowing that from an operating standpoint. Which VP is quietly protecting a headcount? Why? And do you want to expose it or let it ride?

All of these things are not written down in a file anywhere, because they're based on an understanding of the business and, most importantly, on relationships across the business.

Finance, accounting, treasury, all of our departments at the end of the day are a cost center. Now, I know some of you might be seeing exceptions. "Oh, well, FP&A can add value." I'm in FP&A. I've made that argument before, but we are ultimately a cost center. "Treasury can drive value with cash." Yes, we're all still a cost center.

To add value to the business is to get the decision makers, the operators, to be able to make decisions, the right decisions, as quickly as possible. And you can do that without being the best influencer in the business. You can do that without having the best, what we call, soft skills in the business.

You absolutely need soft skills. Don't get me wrong. You absolutely need soft skills. But you can do it without being the best. What's important, though, is that you do it.

In a world of AI, this is the irreplaceable value. This is knowing that Bob in accounting keeps putting the same invoices in incorrectly, and that's something that has to be addressed. This is knowing those headcounts that are sacred and are being protected, and knowing when it's right to call things out, and when to trust the business and let it ride.

There's no course or no certificate you can take to make you good at this. It's really about spending that time getting close to the business and getting close to the people.

Now, a lot of people, myself originally included, picked finance because numbers were the safe part. But numbers are that messy middle where AI is going to be strongest.

So think about how many times this quarter somebody came to you before a decision instead of after. If they're almost always coming to you after a decision, then you need to spend some time building those relationships and getting closer to the business.

For 25 years, Excel was the bottleneck, and standing in the bottleneck was the whole job. The bottleneck is gone now. What is left is building the thing that runs without you and knowing which question is worth running. You need both. Nobody's getting paid to stand in the middle anymore.

And this is hard, because the two ends pull against each other. The technical half wants you at your desk with the door shut, and the relationship half wants you sitting in somebody else's meeting. Almost nobody is naturally good at both, which is why being decent at both is worth…

11:23Three questions to run tomorrow

Okay, so what do you do with this tomorrow? Three questions, and they will tell you which end you're already closer to.

Question one: if you took 2 weeks off starting tomorrow, what would break? Write it down. If the honest answer is that somebody would have to rebuild your file, that is the bottleneck talking. And the bottleneck is the thing you were trying to stop being.

Question two: in the last 3 months, how many times did somebody in the business come to you before they made a decision, rather than after? I want you to count this properly. Zero is a real answer, and a lot of very good finance pros are sitting at zero right now.

And question three: take the last thing AI did for you and explain out loud what it actually did to someone who would push back on you. If you cannot get through that without hand-waving, you have not taken ownership of it yet. And ownership is the part that pays.

Here's the difficult part. Most people score badly on all three. And then the natural move is to pick whichever end looks easier. I'll start with the other one. It takes longer, and it's the one that will still be scarce in 10 years.

The finance pros who move to one of those two ends don't just keep their seat. They become the ones their company can't afford to lose.