Monthly consolidation and trial balance comparison

The AI hype is real. It's also incredibly useful, 𝘪𝘧 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:

1️⃣ 𝗠𝗼𝗻𝘁𝗵𝗹𝘆 𝗰𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 & 𝘁𝗿𝗶𝗮𝗹 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻
Consolidates the trial balances of all entities and summarises the differences per month, per G/L account and expense group

2️⃣ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗮𝗰𝗰𝗿𝘂𝗮𝗹𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗹𝗮𝘀𝘁 𝟯 𝗺𝗼𝗻𝘁𝗵𝘀 𝗼𝗳 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀
Spots which vendor invoices are still missing on day 1 of the month, based on last month's pattern, including the average amount

3️⃣ 𝗥𝗼𝗹𝗹𝗼𝘃𝗲𝗿 𝗼𝗳 𝗺𝘆 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗺𝗼𝗱𝗲𝗹
Automatically drags forward all formulas from the last forecasting month to the actual month at month-end

4️⃣ 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗵𝗲𝗲𝘁 & 𝗰𝗮𝘀𝗵𝗳𝗹𝗼𝘄 𝘀𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
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

5️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰, 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗛𝗧𝗠𝗟 𝗺𝗼𝗻𝘁𝗵𝗹𝘆 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗽𝗮𝗰𝗸𝗮𝗴𝗲
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.

↳ Where in your finance function is AI actually saving you real hours?