E3: How to close your books faster - with Parker Gilbert

How long do teams typically take to close the books, and how have you seen that evolving over time?

We will see anything from it taking a full month down to teams that are sort of really buttoned up, and they're trying to cut hours or or minutes, you know, during their close to get from a a five day close to a four day close and and maybe even to a three day close.

We have been talking about speed mostly. Right? Like, how to do it faster, how to take fewer days. Do you think that there's a inherent trade off between speed and accuracy?

I do I do think there's a trade off.

As you work with those teams and you map the processes, have you seen the answer sometimes being you just need to stop doing this? This doesn't add value. It's not even about making it before. It's just like maybe you can skip it altogether.

How how how good can you just get at predicting what you think your, you know, financial statements will look like for this period? You know, book those and then sort of compare those against sort of when you actually wrap up all your workflows and if and actually finish the actuals, you know. So I actually do think that's like a very real trade off. Like, kind of CEO in me wants to hammer on the notion of, no. It's, a myth that, you know, like You're listening to the 10x Finance Podcast. Quick, candid conversations with the people shaping modern finance.

Hosted by Albert Gozzi.

Hello, everyone, and welcome to the 10x finance podcast, where we dive into what separates good from great finance teams. I'm your host, Albert Gosi, cofounder and CEO of Aleph, and I'm joined today by Parker Gilbert, cofounder and CEO of Numeric, an AI powered close automation tool. Today, we'll be diving into the theme of how to close how to close your books faster. Parker, it's great to have you. Ready to jump in?

Yeah. Let's do it, Albert. Thank you for having me.

Thanks for being here. Alright. So we're gonna start just to set the scene a little bit and and sort of warm up into a theme.

How long do teams typically take to close the books, and how have you seen that evolving over time?

Yeah. So I I think I think maybe sort of helpful context from from the angle that that we see the most. We typically work with companies that sort of are in the kind of mid market growth stage and and maybe early enterprise today, you know, sort of their, you know, kind of a maybe recently publicly public companies or things like that.

And I'd say we sort of think about the close as a lot of sort of like a maturity model where you sort of have you sort of are at different stages depending on sort of, like, you know, the sort of complexity and size and and requirements that's, you know, kinda being placed upon the business.

So I'd say the first first first time we typically will work with companies is when things are starting to get complicated. Right? You know, like the the number of entities is growing, maybe the number of currencies is growing, maybe there are new product lines.

Financial audits are starting to be just a a sort of recurring reality, you know, of the ongoing finance and accounting work. And there's an emphasis on faster reporting and faster numbers and and and utilizing the data further. Right? And I think that's sort of like that intersection where you start to see the pressure and sort of like mandate that, you know, maybe we didn't actually care too much about sort of the speed of our numbers to starting to really push on on on sort of the timing.

And so I would say from, like, a a pure days perspective, we will see anything from it taking a full month to to close down to teams that are sort of really buttoned up, and they're trying to cut hours or or minutes, you know, during their close to get from a a five day close to a four day close and and maybe even to a three day close. So we see a pretty broad range, but it and and and I think a lot of it is sort of, you know, trying to figure out how do we help companies sort of on that maturity cycle as as the business complexity grows too.

How you're thinking about maturity cycle. You're talking about things like multi entity.

So you're saying that is the thing that impacts how long it takes to close more on company scale? So it's like, there seems to be some correlation between one and the other one, but it's about those things that make it complex more about than pure size of the the company or the team?

Yeah. I think so. Right? Like, you can see some sort of SaaS businesses where, you know, candidly, their operations are, like, very, very simple.

You know? But they get to huge scale, whether that's because they've got a handful of really large contracts or or lots of small ones. But typically, think these sort of kind of close burden gets larger as data volumes go up, as the the the variety of tasks and workflow goes up, as as just the sort of, yeah, complexity. And we we even have some small companies where they're, you know, they're pre revenue.

Right? But the but the complexity is really there from day one as they, you know, are spinning up global operations. They're, you know, sort of hiring people around the world. And so I I don't think it's sort of, you know, perfectly correlated with with with, you know, sort of revenue, kind of top line top line revenue.

It's much more about the sort of complexity of of, you know, a variety of factors. Right? You know, multi entity, currencies, products, you know, biz business model, you know, what is the sort of decisions that are being made off of those numbers?

All that will sort of impact kind of the the the imperative, right, of how quick do we need to close and and and how accurate do we need the numbers.

Makes sense. So you're talking about the the best teams close the books in four or five business days, trying to shape by the day, by the hour. How has that evolved over time?

I I know you you operate in an industry where, you know, companies have been around for, you know, tens of years trying to help people close the books faster. What have you seen the the evolution be?

Yeah. I mean, I I think there's I I I think I think the think this sort of evolution has has been one where this sort of category, this this type of process, I think, is is largely been defined by compliance, you know, managing audit trails, making sure that sort of sign offs are are there and present, which which is an important part of of what we do. And an important part of closing the books is not just being accurate and and being correct, but having the right documentation and support.

And then it's been a lot of management. Right? It's been a lot of managing our people. It's been a lot of how do we make sure that our dozen accountants know what to do, when to do it.

If I'm managing them, I know when things are late. You know, I know where to follow-up on. And so I think a lot of the sort of theme, kind of unsurprisingly, has been sort of how do we manage our our humans doing this work. Right?

I think what we're seeing is a lot more of this work getting pushed towards, you know, how do we, you know, automate the underlying work? How do we do more of this work in real time and in a continuous fashion so that we can actually remove it entirely as a burden from the closed process and and kind of pull it forward? And then increasingly, you know, how do we use AI in ways that can just be sort of like for like replacements to a lot of the workflows that people are doing. Right?

I mean, I think the one of the really fun parts of of one of the most one of, like, the the the worst part of the close probably is, like, the fact that it happens every month. You know? And, like, you know, from a sort of automation and an AI perspective, like, that may be the best part is, like, there's actually an imperative to go invest in these processes and figure out, okay, how do I take the steps that Albert goes through, you know, to review this reconciliation every month? And how do I start to have agents and and and sort of tools that can actually go do more of that work for me, highlight things, and and sort of manage more by exception.

Right? And so there's a lot a lot of really interesting opportunities today, but I think so much of that theme is is centered on, like, you know, replacing those human hours and and getting to exception management as opposed to organization or tracking.

Perfect. So so you're highlighting two that I think were very interesting. So I wanna say them back to you. One is, you know, I have five days.

It's taking me forty hours to close. Out of those forty hours, what can I just not do during the close? What can I do before during the month when I have more time? That's great.

Then there's things that, you know, still need to happen within those forty hours. You cannot escape it because the dependencies are not there. So then it's about how you make them faster and AI being a way of making them faster.

Are there other things that you've seen, you know, have good impact great impact in in the the time that it takes for people to close?

Yeah. I mean, I think I I I think it just sort of, like, two two very good themes to to to really focus on.

I I think the I think I think you mentioned it as well too. I think, like, the the way to sort of figure out kind of what bucket are these things in is is, like, is is is it, like, sort of the dependencies in the workflow? You know, just sort of mapping this out and thinking very much of this is a sort of large series of processes to be orchestrated. And that's kind of the one of those first steps that's required is actually being able to say, okay. Well, what can we pull forward? You know, what can we pull forward?

And and sort of that, then we can sort of identify, like, which bucket it needs to be in and and kind of push from there.

And have you seen as you work with those teams and you map the processes, have you seen the answer sometimes being you just need to stop doing this? This doesn't add value. It's not even about making it before. It's just, like, maybe you can skip it altogether.

Maybe, you know, like like like, infrequently, would say, but but I do think that's, like, a good thing to push on. And I I think the look. I think the best teams are sort of asking this question every single month, which is, hey. As we go with finish our close process, let's retro it. Let's talk about what went well. Let's talk about what didn't go well. So I think part of that is is is asking the question, like, I think there's sometimes an assumption that doing things more frequently and at a higher level of granularity is better.

You know, do we need to book our software capitalization every single month and review it with our engineering manager every month to see what projects have been disposed of or not? Like, maybe. You know? Maybe not.

Right? So I I do think there's sort of a a good sort of stress test to to sort of, like, push on some of those assumptions, which is are there things that we can actually, you know, just fundamentally do less frequently or or less of and still, you know, get to the same the same result. But I would say, typically, we're seeing the other the other sort of side of that coin, is their auditors and and and their and their teams are telling them they need more. They need more granularity.

They need more visibility. They need they need more and more metadata tagging. The allocations need to be more deep. Right?

And so we usually see this sort of other side of the curve, which is like, oh, crap. Like, we're we're trying to reduce our close, yet everyone is asking us for more. You know, this is a really sort of uncomfortable position to be in.

Makes sense. We we have been talking about speed mostly. Right? Like, to do it faster, how to take fewer days.

Do you think that there's a inherent trade off between speed and accuracy, or or do you think that the the best teams should chase both?

I do I do think there's a trade off. Right? I mean, I think, like, I think, accruals is a good example of, you know, somewhere where I think there's, like, pretty pretty clearly a trade off. Like, if I if we wanna go just sort of book, you know, all of our sort of estimates and and sort of accruals on on the very first, you know, business day, we can go do that. And we can go accept a tolerance for just being less accurate in terms of, like, what those expense figures are. And it may or may not be a good decision based on, again, like, the sort of requirements and sort of fidelity that that we need to go go report on.

I think that's one of those I do think that's, like, like, an interesting AI use cases. Like, how how how good can you get at predicting what you think your, you know, financial statements will look like for this period? You know, book those, and then sort of compare those against sort of when you actually wrap up all your workflows and if and actually finish the actuals, you know, what those look like. So so I actually do think that's, like, a very real trade off. Like, the sort of, like, kind of CEO in me wants to hammer on the notion of, like, no. It's, a myth and, you know, like, more, like, why don't you do it faster and do it better and, you know, sort of, like, pound the pound the table.

And maybe I do that, you know, too frequently internally. But but I do think in this circumstance, you're you're talking about a real question just like, do I wait for more information, you know, or do I not wait for the information and and and sort of make a a less good estimate about about some of the work we're doing.

Yeah. No. And I think in an age where everyone wants to do, like, things faster and more accurately, I think it is the contrarian take to say that there is some inherent trade off and that you need to choose and that, like, it is important.

So I think that makes sense.

Maybe one more question before we get into the like, our rapid fire wrap up that we like to do.

We you know, Aleph is an FP and A tool. You work mostly with accounting teams. What have you seen works for improving collaboration between finance, FP and A teams, and accounting teams.

Yeah. Yeah. I mean, I I think this is one of the, like, almost, like, most unnecessary sources of friction, you know, that, like, oftentimes gets created in in teams. Right? And I think, like, whenever there's a handoff point, you know, that's the place that is at most risk for for sort of friction getting created. Right?

I would say a couple things. I think I think one, I think it's really important for for both functions increasingly to sort of, like, you know, deeply understand the other's domain.

The best finance people understand accounting deeply. They understand what's going on with the systems. They understand where the data is coming from, and they can help the accounting team make decisions around how to architect that system to to get them the information that they need. Flip side is the exact same goes for accounting teams.

They need to understand what is this what is this data actually being used for. Right? Like, I think accountants can oftentimes be thinking in sort of just the framing of my gap financials and my audit, but that is obviously one use case amongst many that this database that they really own, you know, is being used for. And so I think it's really important that they they understand the goals.

They understand sort of what are the how does that kind of context need to get shaped and managed. And and then, ideally, they're sort of using tools and and and products where they can both collaborate on. Right? That that they can be in each other's systems.

They aren't just sort of siloed, you know, sort of into, you know, the FP and A team lives over here and has this database and this set of tools and the accounting team lives over here. And, you know, like, ideally, there's a sort of really increased sort of level of of collaboration where they are working off the same, you know, the same set of of of of context.

Perfect. So I think just say back to you, the first empathy, understanding each other better, understanding the workflow, and understanding the the pains, and then better tools. And, yeah, may maybe it's a good moment to to give a kudos to Numeric, and we have many mutual customers that seem to be very happy. So kudos on the the company that you've built so far, Parker.

Thank you.

Right. Maybe just to end, quick rapid fire set of questions. What's one mistake you have seen finance teams you see finance teams make over and over again?

Yeah. Good question. I think we we see I I think I think we see a mistake, is is is, you know, teams trying to be too too too perfect. You know, be trying to trying to sort of get everything right and then not maybe sort of recognizing as much as I think they can be, which is just sort of we should be pushing on just trying to get better every month, every period, every week. Like, you know, in many ways, like, iteration is our friend here. You know?

The the operating model we need to build for the company should not be the gold plated five hundred tab model from day one. It should be probably, like, one sheet. You know?

It doesn't matter if it's perfectly accurate across every GL account. Probably, like, eight of them need to be correct, you know, or close to correct. Right? Same things for the close.

Like, we don't need to go map everything perfectly detailed and perfectly nuanced in the first period. What we need to do is just make sure that we are understanding, like, where are we trying to get to, you know, what are the sort of business outcomes we're trying to drive, and then how are we sort of making progress to those goals and and being intentional about sort of the the places we can get the most leverage. I think sometimes there is just a level of, like, almost sort of like cons like, feeling like everything is important and everything must be done perfectly and everything must be done all the time.

And and that can be like a very sort of draining and constraining sort of both feeling and function to to not sort of focus on the places that that truly actually have have the highest leverage. I think it's tough in operational roles like finance where there is a lot of just work that needs to get done. There is a lot of just basic requirements to keep the business sort of ticking over.

I I think that's something which I I certainly know in my my my past role, like, I I I was in the wrong end of that spectrum a number of times and sort of, you know, had to work really hard to to to prioritize as effectively as, like, I needed to.

So maybe related, what's one piece of advice you would give to either finance and accounting leaders scaling their team?

I mean, I think this is sort of a a very much sort of a numeric kind of, like, hiring philosophy, but I think, you know, I think we I I I think it's more true than ever, which is, you know, higher for the intangibles, higher for the slope, higher for how quickly someone is gonna learn, how quickly are they gonna are they enjoying that process, you know, and enjoying the and getting excited by the chance to go do better work and improve things and and help the business. And so I think I think we are sort of definitely continuing to sort of drive towards this, like, you know, age of generalists where the best people can be dangerous in a number of different ways.

They can roll up their sleeves and get into the accounting. They can they can do a good enough job modeling. They can do a job sort of in the the more of the biz ops side of things and getting into more product metrics. And and so I I like the I I I but but I think all that comes back to, like, you need if that's gonna be true, you need people who, like, actually enjoy the work.

Like, they they enjoy learning. They they don't they don't, like, feel like you like, if you have to pull it out of folks, it's just like, you know, it's like it's like too too it's like you you need people, I think, who sort of have that, like, self driving and and excitement about getting better every day.

Makes sense. Final question. What's one trend in finance and accounting you believe will shape the next five years?

Yeah. I mean, I I think we are I think we are I think we are in, like I mean, I I I think this sort of finance I'm I'm curious to get your thoughts, but I feel like I feel like we are in the, like, first inning of sort of AI's impact, whereas in, like, some other fields, we are deeper into the middle of of, you know, the game. You know, it's like we don't see the sort of, you know, code gen tools and the writing tools. Like like, this finance function is different. There's a lot of structured data. There's a lot of different business systems. There's a lot of there there there's a lot of there's there's there's there there haven't been the sort of, like, just off the shelf magic AI experience that, like, you know, transforms finance and accounting.

But I think it does have a huge impact on sort of rethinking sort of how do these systems get used and utilized, how do they get architected from the ground up to to use LMs more effectively. And so I think over the coming years, we will see sort of winners who are getting a lot of the sort of core system architecture and sort of work right that is enabling them to use LMs in in novel and more effective ways.

And and and I do think we will see you know, I I do think sort of, you know, teams like Numeric, like Aleph, in many ways, have to be thinking about sort of a world where many of their sort of core users, like, are not people. You know? They are they are they are agents. They are sort of AI tools. And and I think there's a lot to figure out and a lot of green space to go sort of innovate on on what can that look like and and how can that enable these teams to to do more with less and to to do their best work.

Parker, thanks so much for taking the time. It was a pleasure.

Of course. Thank you, Albert. I appreciate it.

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