The AI hype is real.
The AI hype is real. It's also incredibly useful, ๐ช๐ง you know where to apply it. I think the problem for a lot of people is that the practical examples of how AI can actually help, and how it makes you more efficient, are missing.
Here's my honest take, from a finance seat in a scale-up.
So I thought I'd share some real examples of how I use AI, as the CFO of Orbisk | Zero Food Waste.
In the scale-up phase we're in, capital isn't usually spent on big finance teams. But then all the finance topics still need to be covered:
Monthly reporting, updated rolling forecasts, annual statements, ad hoc analysis whenever the business asks for it.
With a small team, that can become overwhelming.
And it leads to something I want to avoid: only doing the "producing" part of the job, instead of the strategic part, giving guidance and direction to the company.
This is exactly where AI can add value. Because a lot of that producing part can be done with AI.
Some practical examples I've automated, that save me a lot of time:
1๏ธโฃ ๐ ๐ผ๐ป๐๐ต๐น๐ ๐ฐ๐ผ๐ป๐๐ผ๐น๐ถ๐ฑ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฟ๐ถ๐ฎ๐น ๐๐ฎ๐น๐ฎ๐ป๐ฐ๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ถ๐๐ผ๐ป
Consolidates the trial balances of all entities and summarises the differences per month, per G/L account and expense group
2๏ธโฃ ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ฑ ๐ฎ๐ฐ๐ฐ๐ฟ๐๐ฎ๐น๐ ๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ผ๐ป ๐น๐ฎ๐๐ ๐ฏ ๐บ๐ผ๐ป๐๐ต๐ ๐ผ๐ณ ๐๐ฟ๐ฎ๐ป๐๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐
Spots which vendor invoices are still missing on day 1 of the month, based on last month's pattern, including the average amount
3๏ธโฃ ๐ฅ๐ผ๐น๐น๐ผ๐๐ฒ๐ฟ ๐ผ๐ณ ๐บ๐ ๐ณ๐ถ๐ป๐ฎ๐ป๐ฐ๐ถ๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น
Automatically drags forward all formulas from the last forecasting month to the actual month at month-end
4๏ธโฃ ๐๐ฎ๐น๐ฎ๐ป๐ฐ๐ฒ ๐๐ต๐ฒ๐ฒ๐ & ๐ฐ๐ฎ๐๐ต๐ณ๐น๐ผ๐ ๐๐๐ฎ๐๐ฒ๐บ๐ฒ๐ป๐ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป
Based on the trial balances in our ERP, it builds the consolidated balance and cashflow statement automatically, including management corrections and a forecast based on the drivers we've discussed
5๏ธโฃ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ฐ, ๐ถ๐ป๐๐ฒ๐ฟ๐ฎ๐ฐ๐๐ถ๐๐ฒ ๐๐ง๐ ๐ ๐บ๐ผ๐ป๐๐ต๐น๐ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐๐ถ๐ป๐ด ๐ฝ๐ฎ๐ฐ๐ธ๐ฎ๐ด๐ฒ
Built from an auto-generated input Excel, it creates a dashboard and in- and external reporting where I only manually add comments and observations
All of these used to be repetitive, boring, producing work.
I already hear some finance people say: nice story, but are the numbers correct?
Yes. If you build in testing, let AI crosscheck what it produces, and make sure it's always tied back to your source data.
The only catch: you have to invest the time to make it to work. Spend one hour on it and it won't work. Put in the time properly, and it pays you back many times over.
โณ Where in your finance function is AI actually saving you real hours?